Automated abdominal multi-organ segmentation with subject-specific atlas generation

Mesh Regression Based Shape Enhancement Operator Designed for Organ Segmentation

Organ delineation is critical for diagnosis and treatment planning so as to attract a lot of attention. Recently, neural network based methods yield accurate segmentation metrics like dice coefficient. However, they have to face the problem of indistinct boundaries since segmentation is usually modeled as a pixel classification task ignoring anatomical priors. Inspired by the fact that anatomical information is an essential prior for doctors in organ segmentation, this paper proposes a mesh regression-based shape enhancement operator. This operator innovatively models the refinement of segmentation masks as a mesh vertex regression task, enabling the model to refine the segmentation contours from the perspective of segmentation targets rather than purely from a pixel perspective. The proposed operator starts from the coarse segmentation masks produced by any segmentation model. By representing mesh with the fast point feature histogram of mesh vertexes, the displacement of each vertex is predicted by a graph convolutional neural network. Once the coordinate displacements are obtained, the mesh will be evolved through vertex moving. The operator is plug-and-play, and could co-operate with any backbone segmentation model. The constructed two-stage segmentation pipeline is capable of refining organ segmentation results based on geometrical characteristics of target appearance. Validation has been performed on two public accessible datasets to delineate pancreas and liver. Results have shown that the proposed shape enhancement operator could significantly improve segmentation performance, which have also demonstrated its effectiveness and application prospects.

DEBCM: Deep Learning-Based Enhanced Breast Invasive Ductal Carcinoma Classification Model in IoMT Healthcare Systems

Accurate breast cancer (BC) diagnosis is a difficult task that is critical for the proper treatment of BC in IoMT (Internet of Medical Things) healthcare systems. This paper proposes a convolutional neural network (CNN)-based diagnosis method for detecting early-stage breast cancer. In developing the proposed method, we incorporated the CNN model for the invasive ductal carcinoma (IDC) classification using breast histology image data. We have incorporated transfer learning (TL) and data augmentation (DA) mechanisms to improve the CNN model's predictive outcomes. For the fine-tuning process, the CNN model was trained with breast histology image data. Furthermore, the held-out cross-validation method for best model selection and hyper-parameter tuning was incorporated. In addition, various performance evaluation metrics for model performance assessment were computed. The experimental results confirmed that the proposed model outperformed the baseline models across all evaluation metrics, achieving 99.04% accuracy. We recommend the proposed method for early recognition of BC in IoMT healthcare systems due to its high performance.

Feature Extraction Based on Local Histogram with Unequal Bins and a Recurrent Neural Network for the Diagnosis of Kidney Diseases from CT Images

Kidney disease remains one of the most common ailments worldwide, with cancer being one of its most common forms. Early diagnosis can significantly increase the good prognosis for the patient. The development of an artificial intelligence-based system to assist in kidney cancer diagnosis is crucial because kidney illness is a global health concern, and there are limited nephrologists qualified to evaluate kidney cancer. Diagnosing and categorising different forms of renal failure presents the biggest treatment hurdle for kidney cancer. Thus, this article presents a novel method for detecting and classifying kidney cancer subgroups in Computed Tomography (CT) images based on an asymmetric local statistical pixel distribution. In the first step, the input image is non-overlapping windowed, and a statistical distribution of its pixels in each cancer type is built. Then, the method builds the asymmetric statistical distribution of the image's gradient pixels. Finally, the cancer type is identified by applying the two built statistical distributions to a Deep Neural Network (DNN). The proposed method was evaluated using a dataset collected and authorised by the Dhaka Central International Medical Hospital in Bangladesh, which includes 12,446 CT images of the whole abdomen and urogram, acquired with and without contrast. Based on the results, it is possible to confirm that the proposed method outperformed state-of-the-art methods in terms of the usual correctness criteria. The accuracy of the proposed method for all kidney cancer subtypes presented in the dataset was 99.89%, which is promising.

Survey of Denoising, Segmentation and Classification of Pancreatic Cancer Imaging

BACKGROUND

Pancreatic cancer is one of the most serious problems that has taken many lives worldwide. The diagnostic procedure using the traditional approaches was manual by visually analyzing the large volumes of the dataset, making it time-consuming and prone to subjective errors. Hence the need for the computer-aided diagnosis system (CADs) emerged that comprises the machine and deep learning approaches for denoising, segmentation and classification of pancreatic cancer.

INTRODUCTION

There are different modalities used for the diagnosis of pancreatic cancer, such as Positron Emission Tomography/Computed Tomography (PET/CT), Magnetic Resonance Imaging (MRI), Multiparametric-MRI (Mp-MRI), Radiomics and Radio-genomics. Although these modalities gave remarkable results in diagnosis on the basis of different criteria. CT is the most commonly used modality that produces detailed and fine contrast images of internal organs of the body. However, it may also contain a certain amount of gaussian and rician noise that is necessary to be preprocessed before segmentation of the required region of interest (ROI) from the images and classification of cancer.

METHOD

This paper analyzes different methodologies used for the complete diagnosis of pancreatic cancer, including the denoising, segmentation and classification, along with the challenges and future scope for the diagnosis of pancreatic cancer.

RESULT

Various filters are used for denoising and image smoothening and filters as gaussian scale mixture process, non-local means, median filter, adaptive filter and average filter have been used more for better results.

CONCLUSION

In terms of segmentation, atlas based region-growing method proved to give better results as compared to the state of the art whereas, for the classification, deep learning approaches outperformed other methodologies to classify the images as cancerous and non- cancerous. These methodologies have proved that CAD systems have become a better solution to the ongoing research proposals for the detection of pancreatic cancer worldwide.

Partial Label Multi-organ Segmentation based on Local Feature Enhancement

The automatic segmentation of abdominal organs from CT images is essential for surgical planning of abdominal diseases. However, each medical institution only annotates some organs according to its own clinical practice. This brings the partial annotation problem to multi-center abdominal multi-organ segmentation. To address this issue, we introduce a 3D local feature enhanced multi-head segmentation network for multi-organ segmentation of abdominal regions in multiple partially labeled datasets. More specifically, our proposed architecture consists of two branches, the global branch with 3D Transformer and U-Net fusion named 3D TransUNet as the backbone, and the local 3D U-Net branch that provides additional abdominal organ structure information to the global branch to generate more accurate segmentation results. We evaluate our method on four publicly available CT datasets with four different partial label. Our experiments show that the proposed approach provides better accuracy and robustness, with 93.01% average Dice-score-coefficient (DSC) and 3.489 mm Hausdorff Distance (HD) outperforming three existing state-of-the-art methods.

Contour-aware network with class-wise convolutions for 3D abdominal multi-organ segmentation

Accurate delineation of multiple organs is a critical process for various medical procedures, which could be operator-dependent and time-consuming. Existing organ segmentation methods, which were mainly inspired by natural image analysis techniques, might not fully exploit the traits of the multi-organ segmentation task and could not accurately segment the organs with various shapes and sizes simultaneously. In this work, the characteristics of multi-organ segmentation are considered: the global count, position and scale of organs are generally predictable, while their local shape and appearance are volatile. Thus, we supplement the region segmentation backbone with a contour localization task to increase the certainty along delicate boundaries. Meantime, each organ has exclusive anatomical traits, which motivates us to deal with class variability with class-wise convolutions to highlight organ-specific features and suppress irrelevant responses at different field-of-views. To validate our method with adequate amounts of patients and organs, we constructed a multi-center dataset, which contains 110 3D CT scans with 24,528 axial slices, and provided voxel-level manual segmentations of 14 abdominal organs, which adds up to 1,532 3D structures in total. Extensive ablation and visualization studies on it validate the effectiveness of the proposed method. Quantitative analysis shows that we achieve state-of-the-art performance for most abdominal organs, and obtain 3.63 mm 95% Hausdorff Distance and 83.32% Dice Similarity Coefficient on an average.

An Adapted Deep Convolutional Neural Network for Automatic Measurement of Pancreatic Fat and Pancreatic Volume in Clinical Multi-Protocol Magnetic Resonance Images: A Retrospective Study with Multi-Ethnic External Validation

Pancreatic volume and fat fraction are critical prognoses for metabolic diseases like type 2 diabetes (T2D). Magnetic Resonance Imaging (MRI) is a required non-invasive quantification method for the pancreatic fat fraction. The dramatic development of deep learning has enabled the automatic measurement of MR images. Therefore, based on MRI, we intend to develop a deep convolutional neural network (DCNN) that can accurately segment and measure pancreatic volume and fat fraction. This retrospective study involved abdominal MR images from 148 diabetic patients and 246 healthy normoglycemic participants. We randomly separated them into training and testing sets according to the proportion of 80:20. There were 2364 recognizable pancreas images labeled and pre-treated by an upgraded superpixel algorithm for a discernible pancreatic boundary. We then applied them to the novel DCNN model, mimicking the most accurate and latest manual pancreatic segmentation process. Fat phantom and erosion algorithms were employed to increase the accuracy. The results were evaluated by dice similarity coefficient (DSC). External validation datasets included 240 MR images from 10 additional patients. We assessed the pancreas and pancreatic fat volume using the DCNN and compared them with those of specialists. This DCNN employed the cutting-edge idea of manual pancreas segmentation and achieved the highest DSC (91.2%) compared with any reported models. It is the first framework to measure intra-pancreatic fat volume and fat deposition. Performance validation reflected by regression R² value between manual operation and trained DCNN segmentation on the pancreas and pancreatic fat volume were 0.9764 and 0.9675, respectively. The performance of the novel DCNN enables accurate pancreas segmentation, pancreatic fat volume, fraction measurement, and calculation. It achieves the same segmentation level of experts. With further training, it may well surpass any expert and provide accurate measurements, which may have significant clinical relevance.

MCNN: a multi-level CNN model for the classification of brain tumors in IoT-healthcare system

The classification of brain tumors is significantly important for diagnosing and treating brain tumors in IoT healthcare systems. In this work, we have proposed a robust classification model for brain tumors employing deep learning techniques. In the design of the proposed method, an improved Convolutional neural network is used to classify Meningioma, Glioma, and Pituitary types of brain tumors. To test the multi-level convolutional neural network model, brain magnetic resonance image data is utilized. The MCNN model classification results were improved using data augmentation and transfer learning methods. In addition, hold-out and performance evaluation metrics have been employed in the proposed MCNN model. The experimental results show that the proposed model obtained higher outcomes than the state-of-the-art techniques and achieved 99.89% classification accuracy. Due to the higher results of the proposed approach, we recommend it for the identification of brain cancer in IoT-healthcare systems.

DACBT: deep learning approach for classification of brain tumors using MRI data in IoT healthcare environment

The classification of brain tumors (BT) is significantly essential for the diagnosis of Brian cancer (BC) in IoT-healthcare systems. Artificial intelligence (AI) techniques based on Computer aided diagnostic systems (CADS) are mostly used for the accurate detection of brain cancer. However, due to the inaccuracy of artificial diagnostic systems, medical professionals are not effectively incorporating them into the diagnosis process of Brain Cancer. In this research study, we proposed a robust brain tumor classification method using Deep Learning (DL) techniques to address the lack of accuracy issue in existing artificial diagnosis systems. In the design of the proposed approach, an improved convolution neural network (CNN) is used to classify brain tumors employing brain magnetic resonance (MR) image data. The model classification performance has improved by incorporating data augmentation and transfer learning methods. The results confirmed that the model obtained high accuracy compared to the baseline models. Based on high predictive results we suggest the proposed model for brain cancer diagnosis in IoT-healthcare systems.

Deep collaborative network with alpha matte for precise knee tissue segmentation from MRI

BACKGROUND AND OBJECTIVE

Precise segmentation of knee tissues from magnetic resonance imaging (MRI) is critical in quantitative imaging and diagnosis. Convolutional neural networks (CNNs), being state of the art, often challenged by the lack of image-specific adaptation, such as low tissue contrasts and structural inhomogeneities, thereby leading to incomplete segmentation results.

METHODS

This paper presents a deep learning-based automatic segmentation framework for precise knee tissue segmentation. A novel deep collaborative method is proposed, which consists of an encoder-decoder-based segmentation network in combination with a low rank tensor-reconstructed segmentation network. Low rank reconstruction in MRI tensor sub-blocks is introduced to exploit the morphological variations in knee tissues. To model the tissue boundary regions and effectively utilize the superimposed regions, trimap generation is proposed for defining high, medium and low confidence regions from the multipath CNNs. The secondary path with low rank reconstructed input mitigates the conditions in which the primary segmentation network can potentially fail and overlook the boundary regions. The outcome of the segmentation is solved as an alpha matting problem by blending the trimap with the source input.

RESULTS

Experiments on Osteoarthritis Initiative (OAI) datasets with all the 6 musculoskeletal tissues provide an overall segmentation dice score of 0.8925, where Femoral and Tibial part of cartilage achieving an average dice exceeding 0.9. The volumetric metrics also indicate the superior performances in all tissue compartments.

CONCLUSIONS

Experiments on Osteoarthritis Initiative (OAI) datasets and a self-prepared scan validate the effectiveness of the proposed method. Inclusion of extra prediction scale allowed the model to distinguish and segment the tissue boundary accurately. We specifically demonstrate the application of the proposed method in a cartilage segmentation-based thickness map for diagnosis purposes.

Segmentation of pancreatic ductal adenocarcinoma (PDAC) and surrounding vessels in CT images using deep convolutional neural networks and texture descriptors

Fully automated and volumetric segmentation of critical tumors may play a crucial role in diagnosis and surgical planning. One of the most challenging tumor segmentation tasks is localization of pancreatic ductal adenocarcinoma (PDAC). Exclusive application of conventional methods does not appear promising. Deep learning approaches has achieved great success in the computer aided diagnosis, especially in biomedical image segmentation. This paper introduces a framework based on convolutional neural network (CNN) for segmentation of PDAC mass and surrounding vessels in CT images by incorporating powerful classic features, as well. First, a 3D-CNN architecture is used to localize the pancreas region from the whole CT volume using 3D Local Binary Pattern (LBP) map of the original image. Segmentation of PDAC mass is subsequently performed using 2D attention U-Net and Texture Attention U-Net (TAU-Net). TAU-Net is introduced by fusion of dense Scale-Invariant Feature Transform (SIFT) and LBP descriptors into the attention U-Net. An ensemble model is then used to cumulate the advantages of both networks using a 3D-CNN. In addition, to reduce the effects of imbalanced data, a multi-objective loss function is proposed as a weighted combination of three classic losses including Generalized Dice Loss (GDL), Weighted Pixel-Wise Cross Entropy loss (WPCE) and boundary loss. Due to insufficient sample size for vessel segmentation, we used the above-mentioned pre-trained networks and fine-tuned them. Experimental results show that the proposed method improves the Dice score for PDAC mass segmentation in portal-venous phase by 7.52% compared to state-of-the-art methods in term of DSC. Besides, three dimensional visualization of the tumor and surrounding vessels can facilitate the evaluation of PDAC treatment response.

SOMA: Subject-, object-, and modality-adapted precision atlas approach for automatic anatomy recognition and delineation in medical images

PURPOSE

In the multi-atlas segmentation (MAS) method, a large enough atlas set, which can cover the complete spectrum of the whole population pattern of the target object will benefit the segmentation quality. However, the difficulty in obtaining and generating such a large set of atlases and the computational burden required in the segmentation procedure make this approach impractical. In this paper, we propose a method called SOMA to select subject-, object-, and modality-adapted precision atlases for automatic anatomy recognition in medical images with pathology, following the idea that different regions of the target object in a novel image can be recognized by different atlases with regionally best similarity, so that effective atlases have no need to be globally similar to the target subject and also have no need to be overall similar to the target object.

METHODS

The SOMA method consists of three main components: atlas building, object recognition, and object delineation. Considering the computational complexity, we utilize an all-to-template strategy to align all images to the same image space belonging to the root image determined by the minimum spanning tree (MST) strategy among a subset of radiologically near-normal images. The object recognition process is composed of two stages: rough recognition and refined recognition. In rough recognition, subimage matching is conducted between the test image and each image of the whole atlas set, and only the atlas corresponding to the best-matched subimage contributes to the recognition map regionally. The frequency of best match for each atlas is recorded by a counter, and the atlases with the highest frequencies are selected as the precision atlases. In refined recognition, only the precision atlases are examined, and the subimage matching is conducted in a nonlocal manner of searching to further increase the accuracy of boundary matching. Delineation is based on a U-net-based deep learning network, where the original gray scale image together with the fuzzy map from refined recognition compose a two-channel input to the network, and the output is a segmentation map of the target object.

RESULTS

Experiments are conducted on computed tomography (CT) images with different qualities in two body regions - head and neck (H&N) and thorax, from 298 subjects with nine objects and 241 subjects with six objects, respectively. Most objects achieve a localization error within two voxels after refined recognition, with marked improvement in localization accuracy from rough to refined recognition of 0.6-3 mm in H&N and 0.8-4.9 mm in thorax, and also in delineation accuracy (Dice coefficient) from refined recognition to delineation of 0.01-0.11 in H&N and 0.01-0.18 in thorax.

CONCLUSIONS

The SOMA method shows high accuracy and robustness in anatomy recognition and delineation. The improvements from rough to refined recognition and further to delineation, as well as immunity of recognition accuracy to varying image and object qualities, demonstrate the core principles of SOMA where segmentation accuracy increases with precision atlases and gradually refined object matching.

Automatic multi-organ segmentation from abdominal CT volumes with LLE-based graph partitioning and 3D Chan-Vese model

This paper presents a fully automatic method for multi-organ segmentation from 3D abdominal CT volumes. Firstly, spines and ribs are removed by exponential transformation and binarization to reduce the disturbance to subsequent segmentation. Then, a Local Linear Embedding (LLE)-based graph partitioning approach is employed to perform initial segmentation for liver, spleen, and bilateral kidneys simultaneously, and a novel segmentation refinement scheme is applied composed of hybrid intensity model, 3D Chan-Vese model, and histogram equalization-based organ separation algorithm. Finally, a pseudo-3D bottleneck detection algorithm is introduced for boundary correction. The proposed method does not require heavy training or registration process and is capable of dealing with shape and location variations as well as the weak boundaries of target organs. Experiments on XHCSU20 database show the proposed method is competitive with state-of-the-art methods with Dice similarity coefficients of 95.9%, 95.1%, 94.7%, and 94.5%, Jaccard indices of 92.2%, 90.8%, 90.0%, and 89.5%, and average symmetric surface distances of 1.1 mm, 1.0 mm, 0.9 mm and 1.1 mm, for liver, spleen, left and right kidneys, respectively, and the average running time is around 6 min for a CT volume. The accuracy, precision, recall, and specificity also maintain high values for each of the four organs. Moreover, experiments on SLIVER07 dataset prove its high efficiency and accuracy on liver-only segmentation.

A Two-Phase Approach using Mask R-CNN and 3D U-Net for High-Accuracy Automatic Segmentation of Pancreas in CT Imaging

BACKGROUND AND OBJECTIVE

The size, shape, and position of the pancreas are affected by the patient characteristics such as age, sex, adiposity. Owing to more complex anatomical structures (size, shape, and position) of the pancreas, specialists have some difficulties in the analysis of pancreatic diseases (diabetes, pancreatic cancer, pancreatitis). Therefore, the treatment of the disease requires enormous time and depends on the experience of specialists. In order to decrease the rate of pancreatic disease deaths and to assist the specialist in the analysis of pancreatic diseases, automatic pancreas segmentation techniques have been actively developed in the research article for many years.

METHODS

Although the rapid growth of deep learning and proving satisfactory performance in many medical image processing and computer vision applications, the maximum Dice Similarity Coefficients (DSC) value of these techniques related to automatic pancreas segmentation is only around 85% due to complex structure of the pancreas and other factors. Contrary to previous techniques which are required significantly higher computational power and memory, this paper suggests a novel two-phase approach for high-accuracy automatic pancreas segmentation in computed tomography (CT) imaging. The proposed approach consists of two phases; (1) Pancreas Localization, where the rough pancreas position is detected on the 2D CT slice by adopting Mask R-CNN model, (2) Pancreas Segmentation, where the segmented pancreas region is produced by refining the candidate pancreas region with 3D U-Net on the 2D sub-CT slices generated in the first phase. Both qualitative and quantitative assessments of the proposed approach are performed on the NIH data set.

RESULTS

In order to evaluate the achievement of the recommended approach, a total of 16 different automatic pancreas segmentation techniques reported in the literature are compared by utilizing performance assessment procedures which are Dice Similarity Coefficient (DSC), Jaccard Index (JI), Precision (PRE), Recall (REC), Pixel Accuracy (ACC), Specificity (SPE), Receiver Operating Characteristics (ROC) and Area under ROC curve (AUC). The average values of DSC, JI, REC and ACC are computed as 86.15%, 75.93%, 86.27%, 86.27% and 99.95% respectively, which are the best values among well-established studies for automatic pancreas segmentation.

CONCLUSION

It is demonstrated with qualitative and quantitative results that our suggested two-phase approach creates more favorable results than other existing approaches.

Multiorgan segmentation from partially labeled datasets with conditional nnU-Net

Accurate and robust multiorgan abdominal CT segmentation plays a significant role in numerous clinical applications, such as therapy treatment planning and treatment delivery. Almost all existing segmentation networks rely on fully annotated data with strong supervision. However, annotating fully annotated multiorgan data in CT images is both laborious and time-consuming. In comparison, massive partially labeled datasets are usually easily accessible. In this paper, we propose conditional nnU-Net trained on the union of partially labeled datasets for multiorgan segmentation. The deep model employs the state-of-the-art nnU-Net as the backbone and introduces a conditioning strategy by feeding auxiliary information into the decoder architecture as an additional input layer. This model leverages the prior conditional information to identify the organ class at the pixel-wise level and encourages organs' spatial information recovery. Furthermore, we adopt a deep supervision mechanism to refine the outputs at different scales and apply the combination of Dice loss and Focal loss to optimize the training model. Our proposed method is evaluated on seven publicly available datasets of the liver, pancreas, spleen and kidney, in which promising segmentation performance has been achieved. The proposed conditional nnU-Net breaks down the barriers between nonoverlapping labeled datasets and further alleviates the problem of data hunger in multiorgan segmentation.

Incorporating the hybrid deformable model for improving the performance of abdominal CT segmentation via multi-scale feature fusion network

Automated multi-organ abdominal Computed Tomography (CT) image segmentation can assist the treatment planning, diagnosis, and improve many clinical workflows' efficiency. The 3-D Convolutional Neural Network (CNN) recently attained state-of-the-art accuracy, which typically relies on supervised training with many manual annotated data. Many methods used the data augmentation strategy with a rigid or affine spatial transformation to alleviate the over-fitting problem and improve the network's robustness. However, the rigid or affine spatial transformation fails to capture the complex voxel-based deformation in the abdomen, filled with many soft organs. We developed a novel Hybrid Deformable Model (HDM), which consists of the inter-and intra-patient deformation for more effective data augmentation to tackle this issue. The inter-patient deformations were extracted from the learning-based deformable registration between different patients, while the intra-patient deformations were formed using the random 3-D Thin-Plate-Spline (TPS) transformation. Incorporating the HDM enabled the network to capture many of the subtle deformations of abdominal organs. To find a better solution and achieve faster convergence for network training, we fused the pre-trained multi-scale features into the a 3-D attention U-Net. We directly compared the segmentation accuracy of the proposed method to the previous techniques on several centers' datasets via cross-validation. The proposed method achieves the average Dice Similarity Coefficient (DSC) 0.852, which outperformed the other state-of-the-art on multi-organ abdominal CT segmentation results.

A Semiautomated Deep Learning Approach for Pancreas Segmentation

Accurate pancreas segmentation from 3D CT volumes is important for pancreas diseases therapy. It is challenging to accurately delineate the pancreas due to the poor intensity contrast and intrinsic large variations in volume, shape, and location. In this paper, we propose a semiautomated deformable U-Net, i.e., DUNet for the pancreas segmentation. The key innovation of our proposed method is a deformable convolution module, which adaptively adds learned offsets to each sampling position of 2D convolutional kernel to enhance feature representation. Combining deformable convolution module with U-Net enables our DUNet to flexibly capture pancreatic features and improve the geometric modeling capability of U-Net. Moreover, a nonlinear Dice-based loss function is designed to tackle the class-imbalanced problem in the pancreas segmentation. Experimental results show that our proposed method outperforms all comparison methods on the same NIH dataset.

Esophagus Segmentation in CT Images via Spatial Attention Network and STAPLE Algorithm

One essential step in radiotherapy treatment planning is the organ at risk of segmentation in Computed Tomography (CT). Many recent studies have focused on several organs such as the lung, heart, esophagus, trachea, liver, aorta, kidney, and prostate. However, among the above organs, the esophagus is one of the most difficult organs to segment because of its small size, ambiguous boundary, and very low contrast in CT images. To address these challenges, we propose a fully automated framework for the esophagus segmentation from CT images. The proposed method is based on the processing of slice images from the original three-dimensional (3D) image so that our method does not require large computational resources. We employ the spatial attention mechanism with the atrous spatial pyramid pooling module to locate the esophagus effectively, which enhances the segmentation performance. To optimize our model, we use group normalization because the computation is independent of batch sizes, and its performance is stable. We also used the simultaneous truth and performance level estimation (STAPLE) algorithm to reach robust results for segmentation. Firstly, our model was trained by k-fold cross-validation. And then, the candidate labels generated by each fold were combined by using the STAPLE algorithm. And as a result, Dice and Hausdorff Distance scores have an improvement when applying this algorithm to our segmentation results. Our method was evaluated on SegTHOR and StructSeg 2019 datasets, and the experiment shows that our method outperforms the state-of-the-art methods in esophagus segmentation. Our approach shows a promising result in esophagus segmentation, which is still challenging in medical analyses.

Artificial intelligence: a critical review of current applications in pancreatic imaging

The applications of artificial intelligence (AI), including machine learning and deep learning, in the field of pancreatic disease imaging are rapidly expanding. AI can be used for the detection of pancreatic ductal adenocarcinoma and other pancreatic tumors but also for pancreatic lesion characterization. In this review, the basic of radiomics, recent developments and current results of AI in the field of pancreatic tumors are presented. Limitations and future perspectives of AI are discussed.

Iterative confidence relabeling with deep ConvNets for organ segmentation with partial labels

Training deep ConvNets requires large labeled datasets. However, collecting pixel-level labels for medical image segmentation is very expensive and requires a high level of expertise. In addition, most existing segmentation masks provided by clinical experts focus on specific anatomical structures. In this paper, we propose a method dedicated to handle such partially labeled medical image datasets. We propose a strategy to identify pixels for which labels are correct, and to train Fully Convolutional Neural Networks with a multi-label loss adapted to this context. In addition, we introduce an iterative confidence self-training approach inspired by curriculum learning to relabel missing pixel labels, which relies on selecting the most confident prediction with a specifically designed confidence network that learns an uncertainty measure which is leveraged in our relabeling process. Our approach, INERRANT for Iterative coNfidencE Relabeling of paRtial ANnoTations, is thoroughly evaluated on two public datasets (TCAI and LITS), and one internal dataset with seven abdominal organ classes. We show that INERRANT robustly deals with partial labels, performing similarly to a model trained on all labels even for large missing label proportions. We also highlight the importance of our iterative learning scheme and the proposed confidence measure for optimal performance. Finally we show a practical use case where a limited number of completely labeled data are enriched by publicly available but partially labeled data.

Abdominal multi-organ segmentation with cascaded convolutional and adversarial deep networks

Abdominal anatomy segmentation is crucial for numerous applications from computer-assisted diagnosis to image-guided surgery. In this context, we address fully-automated multi-organ segmentation from abdominal CT and MR images using deep learning. The proposed model extends standard conditional generative adversarial networks. Additionally to the discriminator which enforces the model to create realistic organ delineations, it embeds cascaded partially pre-trained convolutional encoder-decoders as generator. Encoder fine-tuning from a large amount of non-medical images alleviates data scarcity limitations. The network is trained end-to-end to benefit from simultaneous multi-level segmentation refinements using auto-context. Employed for healthy liver, kidneys and spleen segmentation, our pipeline provides promising results by outperforming state-of-the-art encoder-decoder schemes. Followed for the Combined Healthy Abdominal Organ Segmentation (CHAOS) challenge organized in conjunction with the IEEE International Symposium on Biomedical Imaging 2019, it gave us the first rank for three competition categories: liver CT, liver MR and multi-organ MR segmentation. Combining cascaded convolutional and adversarial networks strengthens the ability of deep learning pipelines to automatically delineate multiple abdominal organs, with good generalization capability. The comprehensive evaluation provided suggests that better guidance could be achieved to help clinicians in abdominal image interpretation and clinical decision making.

Automatic Pancreas Segmentation in CT Images With Distance-Based Saliency-Aware DenseASPP Network

Pancreas identification and segmentation is an essential task in the diagnosis and prognosis of pancreas disease. Although deep neural networks have been widely applied in abdominal organ segmentation, it is still challenging for small organs (e.g. pancreas) that present low contrast, highly flexible anatomical structure and relatively small region. In recent years, coarse-to-fine methods have improved pancreas segmentation accuracy by using coarse predictions in the fine stage, but only object location is utilized and rich image context is neglected. In this paper, we propose a novel distance-based saliency-aware model, namely DSD-ASPP-Net, to fully use coarse segmentation to highlight the pancreas feature and boost accuracy in the fine segmentation stage. Specifically, a DenseASPP (Dense Atrous Spatial Pyramid Pooling) model is trained to learn the pancreas location and probability map, which is then transformed into saliency map through geodesic distance-based saliency transformation. In the fine stage, saliency-aware modules that combine saliency map and image context are introduced into DenseASPP to develop the DSD-ASPP-Net. The architecture of DenseASPP brings multi-scale feature representation and achieves larger receptive field in a denser way, which overcome the difficulties brought by variable object sizes and locations. Our method was evaluated on both public NIH pancreas dataset and local hospital dataset, and achieved an average Dice-Sørensen Coefficient (DSC) value of 85.49±4.77% on the NIH dataset, outperforming former coarse-to-fine methods.

Fully Automatic Volume Measurement of the Spleen at CT Using Deep Learning

Purpose

To develop a fully automated algorithm for spleen segmentation and to assess the performance of this algorithm in a large dataset.

Materials and Methods

In this retrospective study, a three-dimensional deep learning network was developed to segment the spleen on thorax-abdomen CT scans. Scans were extracted from patients undergoing oncologic treatment from 2014 to 2017. A total of 1100 scans from 1100 patients were used in this study, and 400 were selected for development of the algorithm. For testing, a dataset of 50 scans was annotated to assess the segmentation accuracy and was compared against the splenic index equation. In a qualitative observer experiment, an enriched set of 100 scan-pairs was used to evaluate whether the algorithm could aid a radiologist in assessing splenic volume change. The reference standard was set by the consensus of two other independent radiologists. A Mann-Whitney U test was conducted to test whether there was a performance difference between the algorithm and the independent observer.

Results

The algorithm and the independent observer obtained comparable Dice scores (P = .834) on the test set of 50 scans of 0.962 and 0.964, respectively. The radiologist had an agreement with the reference standard in 81% (81 of 100) of the cases after a visual classification of volume change, which increased to 92% (92 of 100) when aided by the algorithm.

Conclusion

A segmentation method based on deep learning can accurately segment the spleen on CT scans and may help radiologists to detect abnormal splenic volumes and splenic volume changes.Supplemental material is available for this article.© RSNA, 2020.

High-resolution 3D abdominal segmentation with random patch network fusion

Deep learning for three dimensional (3D) abdominal organ segmentation on high-resolution computed tomography (CT) is a challenging topic, in part due to the limited memory provide by graphics processing units (GPU) and large number of parameters and in 3D fully convolutional networks (FCN). Two prevalent strategies, lower resolution with wider field of view and higher resolution with limited field of view, have been explored but have been presented with varying degrees of success. In this paper, we propose a novel patch-based network with random spatial initialization and statistical fusion on overlapping regions of interest (ROIs). We evaluate the proposed approach using three datasets consisting of 260 subjects with varying numbers of manual labels. Compared with the canonical "coarse-to-fine" baseline methods, the proposed method increases the performance on multi-organ segmentation from 0.799 to 0.856 in terms of mean DSC score (p-value < 0.01 with paired t-test). The effect of different numbers of patches is evaluated by increasing the depth of coverage (expected number of patches evaluated per voxel). In addition, our method outperforms other state-of-the-art methods in abdominal organ segmentation. In conclusion, the approach provides a memory-conservative framework to enable 3D segmentation on high-resolution CT. The approach is compatible with many base network structures, without substantially increasing the complexity during inference. Given a CT scan with at high resolution, a low-res section (left panel) is trained with multi-channel segmentation. The low-res part contains down-sampling and normalization in order to preserve the complete spatial information. Interpolation and random patch sampling (mid panel) is employed to collect patches. The high-dimensional probability maps are acquired (right panel) from integration of all patches on field of views.

Efficient COVID-19 Segmentation from CT Slices Exploiting Semantic Segmentation with Integrated Attention Mechanism

Coronavirus (COVID-19) is a pandemic, which caused suddenly unexplained pneumonia cases and caused a devastating effect on global public health. Computerized tomography (CT) is one of the most effective tools for COVID-19 screening. Since some specific patterns such as bilateral, peripheral, and basal predominant ground-glass opacity, multifocal patchy consolidation, crazy-paving pattern with a peripheral distribution can be observed in CT images and these patterns have been declared as the findings of COVID-19 infection. For patient monitoring, diagnosis and segmentation of COVID-19, which spreads into the lung, expeditiously and accurately from CT, will provide vital information about the stage of the disease. In this work, we proposed a SegNet-based network using the attention gate (AG) mechanism for the automatic segmentation of COVID-19 regions in CT images. AGs can be easily integrated into standard convolutional neural network (CNN) architectures with a minimum computing load as well as increasing model precision and predictive accuracy. Besides, the success of the proposed network has been evaluated based on dice, Tversky, and focal Tversky loss functions to deal with low sensitivity arising from the small lesions. The experiments were carried out using a fivefold cross-validation technique on a COVID-19 CT segmentation database containing 473 CT images. The obtained sensitivity, specificity, and dice scores were reported as 92.73%, 99.51%, and 89.61%, respectively. The superiority of the proposed method has been highlighted by comparing with the results reported in previous studies and it is thought that it will be an auxiliary tool that accurately detects automatic COVID-19 regions from CT images.

Thyroid gland delineation in noncontrast-enhanced CTs using deep convolutional neural networks

The purpose of this study is to develop a deep learning method for thyroid delineation with high accuracy, efficiency, and robustness in noncontrast-enhanced head and neck CTs. The cross-sectional analysis consisted of six tests, including randomized cross-validation and hold-out experiments, tests of prediction accuracy between cancer and benign and cross-gender analysis were performed to evaluate the proposed deep-learning-based performance method. CT images of 1977 patients with suspected thyroid carcinoma were retrospectively investigated. The automatically segmented thyroid gland volume was compared against physician-approved clinical contours using metrics, the Pearson correlation and Bland-Altman analysis. Quantitative metrics included: the Dice similarity coefficient (DSC), sensitivity, specificity, Jaccard index (JAC), Hausdorff distance (HD), mean surface distance (MSD), residual mean square distance (RMSD) and the center of mass distance (CMD). The robustness of the proposed method was further tested using the nonparametric Kruskal-Wallis test to assess the equality of distribution of DSC values. The proposed method's accuracy remained high through all the tests, with the median DSC, JAC, sensitivity and specificity higher than 0.913, 0.839, 0.856 and 0.979, respectively. The proposed method also resulted in median MSD, RMSD, HD and CMD, of less than 0.31 mm, 0.48 mm, 2.06 mm and 0.50 mm, respectively. The MSD and RMSD were 0.40 ± 0.29 mm and 0.70 ± 0.46 mm, respectively. Concurrent testing of the proposed method with 3D U-Net and V-Net showed that the proposed method had significantly improved performance. The proposed deep-learning method achieved accurate and robust performance through six cross-sectional analysis tests.

Marginal loss and exclusion loss for partially supervised multi-organ segmentation

Annotating multiple organs in medical images is both costly and time-consuming; therefore, existing multi-organ datasets with labels are often low in sample size and mostly partially labeled, that is, a dataset has a few organs labeled but not all organs. In this paper, we investigate how to learn a single multi-organ segmentation network from a union of such datasets. To this end, we propose two types of novel loss function, particularly designed for this scenario: (i) marginal loss and (ii) exclusion loss. Because the background label for a partially labeled image is, in fact, a 'merged' label of all unlabelled organs and 'true' background (in the sense of full labels), the probability of this 'merged' background label is a marginal probability, summing the relevant probabilities before merging. This marginal probability can be plugged into any existing loss function (such as cross entropy loss, Dice loss, etc.) to form a marginal loss. Leveraging the fact that the organs are non-overlapping, we propose the exclusion loss to gauge the dissimilarity between labeled organs and the estimated segmentation of unlabelled organs. Experiments on a union of five benchmark datasets in multi-organ segmentation of liver, spleen, left and right kidneys, and pancreas demonstrate that using our newly proposed loss functions brings a conspicuous performance improvement for state-of-the-art methods without introducing any extra computation.

MAD-UNet: A deep U-shaped network combined with an attention mechanism for pancreas segmentation in CT images

PURPOSE

Pancreas segmentation is a difficult task because of the high intrapatient variability in the shape, size, and location of the organ, as well as the low contrast and small footprint of the CT scan. At present, the U-Net model is likely to lead to the problems of intraclass inconsistency and interclass indistinction in pancreas segmentation. To solve this problem, we improved the contextual and semantic feature information acquisition method of the biomedical image segmentation model (U-Net) based on a convolutional network and proposed an improved segmentation model called the multiscale attention dense residual U-shaped network (MAD-UNet).

METHODS

There are two aspects considered in this method. First, we adopted dense residual blocks and weighted binary cross-entropy to enhance the semantic features to learn the details of the pancreas. Using such an approach can reduce the effects of intraclass inconsistency. Second, we used an attention mechanism and multiscale convolution to enrich the contextual information and suppress learning in unrelated areas. We let the model be more sensitive to pancreatic marginal information and reduced the impact of interclass indistinction.

RESULTS

We evaluated our model using fourfold cross-validation on 82 abdominal enhanced three-dimensional (3D) CT scans from the National Institutes of Health (NIH-82) and 281 3D CT scans from the 2018 MICCAI segmentation decathlon challenge (MSD). The experimental results showed that our method achieved state-of-the-art performance on the two pancreatic datasets. The mean Dice coefficients were 86.10% ± 3.52% and 88.50% ± 3.70%.

CONCLUSIONS

Our model can effectively solve the problems of intraclass inconsistency and interclass indistinction in the segmentation of the pancreas, and it has value in clinical application. Code is available at https://github.com/Mrqins/pancreas-segmentation.

Automatic quantification of myocardium and pericardial fat from coronary computed tomography angiography: a multicenter study

OBJECTIVES

To develop a deep learning-based method for simultaneous myocardium and pericardial fat quantification from coronary computed tomography angiography (CCTA) for the diagnosis and treatment of cardiovascular disease (CVD).

METHODS

We retrospectively identified CCTA data obtained between May 2008 and July 2018 in a multicenter (six centers) CVD study. The proposed method was evaluated on 422 patients' data by two studies. The first overall study involves training model on CVD patients and testing on non-CVD patients, as well as training on non-CVD patients and testing on CVD patients. The second study was performed using the leave-center-out approach. The method performance was evaluated using Dice similarity coefficient (DSC), Jaccard index (JAC), 95% Hausdorff distance (HD95), mean surface distance (MSD), residual mean square distance (RMSD), and the center of mass distance (CMD). The robustness of the proposed method was tested using the nonparametric Kruskal-Wallis test and post hoc test to assess the equality of distribution of DSC values among different tests.

RESULTS

The automatic segmentation achieved a strong correlation with contour (ICC and R > 0.97, p value < 0.001 throughout all tests). The accuracy of the proposed method remained high through all the tests, with the median DSC higher than 0.88 for pericardial fat and 0.96 for myocardium. The proposed method also resulted in mean MSD, RMSD, HD95, and CMD of less than 1.36 mm for pericardial fat and 1.00 mm for myocardium.

CONCLUSIONS

The proposed deep learning-based segmentation method enables accurate simultaneous quantification of myocardium and pericardial fat in a multicenter study.

KEY POINTS

• Deep learning-based myocardium and pericardial fat segmentation method tested on 422 patients' coronary computed tomography angiography in a multicenter study. • The proposed method provides segmentations with high volumetric accuracy (ICC and R > 0.97, p value < 0.001) and similar shape as manual annotation by experienced radiologists (median Dice similarity coefficient ≥ 0.88 for pericardial fat and 0.96 for myocardium).

Multi-Organ Segmentation Over Partially Labeled Datasets With Multi-Scale Feature Abstraction

Shortage of fully annotated datasets has been a limiting factor in developing deep learning based image segmentation algorithms and the problem becomes more pronounced in multi-organ segmentation. In this paper, we propose a unified training strategy that enables a novel multi-scale deep neural network to be trained on multiple partially labeled datasets for multi-organ segmentation. In addition, a new network architecture for multi-scale feature abstraction is proposed to integrate pyramid input and feature analysis into a U-shape pyramid structure. To bridge the semantic gap caused by directly merging features from different scales, an equal convolutional depth mechanism is introduced. Furthermore, we employ a deep supervision mechanism to refine the outputs in different scales. To fully leverage the segmentation features from all the scales, we design an adaptive weighting layer to fuse the outputs in an automatic fashion. All these mechanisms together are integrated into a Pyramid Input Pyramid Output Feature Abstraction Network (PIPO-FAN). Our proposed method was evaluated on four publicly available datasets, including BTCV, LiTS, KiTS and Spleen, where very promising performance has been achieved. The source code of this work is publicly shared at https://github.com/DIAL-RPI/PIPO-FAN to facilitate others to reproduce the work and build their own models using the introduced mechanisms.

Block Level Skip Connections Across Cascaded V-Net for Multi-Organ Segmentation

Multi-organ segmentation is a challenging task due to the label imbalance and structural differences between different organs. In this work, we propose an efficient cascaded V-Net model to improve the performance of multi-organ segmentation by establishing dense Block Level Skip Connections (BLSC) across cascaded V-Net. Our model can take full advantage of features from the first stage network and make the cascaded structure more efficient. We also combine stacked small and large kernels with an inception-like structure to help our model to learn more patterns, which produces superior results for multi-organ segmentation. In addition, some small organs are commonly occluded by large organs and have unclear boundaries with other surrounding tissues, which makes them hard to be segmented. We therefore first locate the small organs through a multi-class network and crop them randomly with the surrounding region, then segment them with a single-class network. We evaluated our model on SegTHOR 2019 challenge unseen testing set and Multi-Atlas Labeling Beyond the Cranial Vault challenge validation set. Our model has achieved an average dice score gain of 1.62 percents and 3.90 percents compared to traditional cascaded networks on these two datasets, respectively. For hard-to-segment small organs, such as the esophagus in SegTHOR 2019 challenge, our technique has achieved a gain of 5.63 percents on dice score, and four organs in Multi-Atlas Labeling Beyond the Cranial Vault challenge have achieved a gain of 5.27 percents on average dice score.

CT-based multi-organ segmentation using a 3D self-attention U-net network for pancreatic radiotherapy

PURPOSE

Segmentation of organs-at-risk (OARs) is a weak link in radiotherapeutic treatment planning process because the manual contouring action is labor-intensive and time-consuming. This work aimed to develop a deep learning-based method for rapid and accurate pancreatic multi-organ segmentation that can expedite the treatment planning process.

METHODS

We retrospectively investigated one hundred patients with computed tomography (CT) simulation scanned and contours delineated. Eight OARs including large bowel, small bowel, duodenum, left kidney, right kidney, liver, spinal cord and stomach were the target organs to be segmented. The proposed three-dimensional (3D) deep attention U-Net is featured with a deep attention strategy to effectively differentiate multiple organs. Performance of the proposed method was evaluated using six metrics, including Dice similarity coefficient (DSC), sensitivity, specificity, Hausdorff distance 95% (HD95), mean surface distance (MSD) and residual mean square distance (RMSD).

RESULTS

The contours generated by the proposed method closely resemble the ground-truth manual contours, as evidenced by encouraging quantitative results in terms of DSC, sensitivity, specificity, HD95, MSD and RMSD. For DSC, mean values of 0.91 ± 0.03, 0.89 ± 0.06, 0.86 ± 0.06, 0.95 ± 0.02, 0.95 ± 0.02, 0.96 ± 0.01, 0.87 ± 0.05 and 0.93 ± 0.03 were achieved for large bowel, small bowel, duodenum, left kidney, right kidney, liver, spinal cord and stomach, respectively.

CONCLUSIONS

The proposed method could significantly expedite the treatment planning process by rapidly segmenting multiple OARs. The method could potentially be used in pancreatic adaptive radiotherapy to increase dose delivery accuracy and minimize gastrointestinal toxicity.

Self-paced DenseNet with boundary constraint for automated multi-organ segmentation on abdominal CT images

Automated multi-organ segmentation on abdominal CT images may replace or complement manual segmentation for clinical applications including image-guided radiation therapy. However, the accuracy of auto-segmentation is challenged by low image contrast, large spatial and inter-patient anatomical variations. In this study, we propose an end-to-end segmentation network, termed self-paced DenseNet, for improved multi-organ segmentation performance, especially for the difficult-to-segment organs. Specifically, a learning-based attention mechanism and dense connection block are seamlessly integrated into the proposed self-paced DenseNet to improve the learning capability and efficiency of the backbone network. To heavily focus on the organs showing low soft-tissue contrast and motion artifacts, a boundary condition is utilized to constrain the network optimization. Additionally, to ease the large learning pace discrepancies of individual organs, a task-wise self-paced-learning strategy is employed to adaptively control the learning paces of individual organs. The proposed self-paced DenseNet was trained and evaluated on a public abdominal CT data set consisting of 90 subjects with manually labeled ground truths of eight organs (including spleen, left kidney, esophagus, gallbladder, stomach, liver, pancreas, and duodenum). For quantitative evaluation, the Dice similarity coefficient (DSC) and average surface distance (ASD) were calculated. An average DSC of 84.46% and ASD of 1.82 mm were achieved on the eight organs, which outperforms the state-of-the-art segmentation methods 2.96% on DSC under the same experimental configuration. Moreover, the proposed segmentation method shows notable improvements on the duodenum and gallbladder, obtaining an average DSC of 69.26% and 80.94% and ASD of 2.14 mm and 2.24 mm, respectively. The results are markedly superior to the average DSC of 63.12% and 76.35% and average ASD of 3.87 mm and 4.33 mm using the vanilla DenseNet, respectively, for the two organs. We demonstrated the effectiveness of the proposed self-paced DenseNet to automatically segment abdominal organs with low boundary conspicuity. The self-paced DenseNet achieved consistently superior segmentation performance on eight abdominal organs with varying segmentation difficulties. The demonstrated computational efficiency (<2 s/CT) makes it well-suited for online applications.

Kidney segmentation from computed tomography images using deep neural network

BACKGROUND

The precise segmentation of kidneys and kidney tumors can help medical specialists to diagnose diseases and improve treatment planning, which is highly required in clinical practice. Manual segmentation of the kidneys is extremely time-consuming and prone to variability between different specialists due to their heterogeneity. Because of this hard work, computational techniques, such as deep convolutional neural networks, have become popular in kidney segmentation tasks to assist in the early diagnosis of kidney tumors. In this study, we propose an automatic method to delimit the kidneys in computed tomography (CT) images using image processing techniques and deep convolutional neural networks (CNNs) to minimize false positives.

METHODS

The proposed method has four main steps: (1) acquisition of the KiTS19 dataset, (2) scope reduction using AlexNet, (3) initial segmentation using U-Net 2D, and (4) false positive reduction using image processing to maintain the largest elements (kidneys).

RESULTS

The proposed method was evaluated in 210 CTs from the KiTS19 database and obtained the best result with an average Dice coefficient of 96.33%, an average Jaccard index of 93.02%, an average sensitivity of 97.42%, an average specificity of 99.94% and an average accuracy of 99.92%. In the KiTS19 challenge, it presented an average Dice coefficient of 93.03%.

CONCLUSION

In our method, we demonstrated that the kidney segmentation problem in CT can be solved efficiently using deep neural networks to define the scope of the problem and segment the kidneys with high precision and with the use of image processing techniques to reduce false positives.

Improving the slice interaction of 2.5D CNN for automatic pancreas segmentation

PURPOSE

Volumetric pancreas segmentation can be used in the diagnosis of pancreatic diseases, the research about diabetes and surgical planning. Since manual delineation is time-consuming and laborious, we develop a deep learning-based framework for automatic pancreas segmentation in three dimensional (3D) medical images.

METHODS

A two-stage framework is designed for automatic pancreas delineation. In the localization stage, a Square Root Dice loss is developed to handle the trade-off between sensitivity and specificity. In refinement stage, a novel 2.5D slice interaction network with slice correlation module is proposed to capture the non-local cross-slice information at multiple feature levels. Also a self-supervised learning-based pre-training method, slice shuffle, is designed to encourage the inter-slice communication. To further improve the accuracy and robustness, ensemble learning and a recurrent refinement process are adopted in the segmentation flow.

RESULTS

The segmentation technique is validated in a public dataset (NIH Pancreas-CT) with 82 abdominal contrast-enhanced 3D CT scans. Fourfold cross-validation is performed to assess the capability and robustness of our method. The dice similarity coefficient, sensitivity, and specificity of our results are 86.21 ± 4.37%, 87.49 ± 6.38% and 85.11 ± 6.49% respectively, which is the state-of-the-art performance in this dataset.

CONCLUSIONS

We proposed an automatic pancreas segmentation framework and validate in an open dataset. It is found that 2.5D network benefits from multi-level slice interaction and suitable self-supervised learning method for pre-training can boost the performance of neural network. This technique could provide new image findings for the routine diagnosis of pancreatic disease.

Automatic segmentation and quantification of epicardial adipose tissue from coronary computed tomography angiography

Epicardial adipose tissue (EAT) is a visceral fat deposit, that's known for its association with factors, such as obesity, diabetes mellitus, age, and hypertension. Segmentation of the EAT in a fast and reproducible way is important for the interpretation of its role as an independent risk marker intricate. However, EAT has a variable distribution, and various diseases may affect the volume of the EAT, which can increase the complexity of the already time-consuming manual segmentation work. We propose a 3D deep attention U-Net method to automatically segment the EAT from coronary computed tomography angiography (CCTA). Five-fold cross-validation and hold-out experiments were used to evaluate the proposed method through a retrospective investigation of 200 patients. The automatically segmented EAT volume was compared with physician-approved clinical contours. Quantitative metrics used were the Dice similarity coefficient (DSC), sensitivity, specificity, Jaccard index (JAC), Hausdorff distance (HD), mean surface distance (MSD), residual mean square distance (RMSD), and the center of mass distance (CMD). For cross-validation, the median DSC, sensitivity, and specificity were 92.7%, 91.1%, and 95.1%, respectively, with JAC, HD, CMD, MSD, and RMSD are 82.9% ± 8.8%, 3.77 ± 1.86 mm, 1.98 ± 1.50 mm, 0.37 ± 0.24 mm, and 0.65 ± 0.37 mm, respectively. For the hold-out test, the accuracy of the proposed method remained high. We developed a novel deep learning-based approach for the automated segmentation of the EAT on CCTA images. We demonstrated the high accuracy of the proposed learning-based segmentation method through comparison with ground truth contour of 200 clinical patient cases using 8 quantitative metrics, Pearson correlation, and Bland-Altman analysis. Our automatic EAT segmentation results show the potential of the proposed method to be used in computer-aided diagnosis of coronary artery diseases (CADs) in clinical settings.

CBCT-based synthetic CT generation using deep-attention cycleGAN for pancreatic adaptive radiotherapy

PURPOSE

Current clinical application of cone-beam CT (CBCT) is limited to patient setup. Imaging artifacts and Hounsfield unit (HU) inaccuracy make the process of CBCT-based adaptive planning presently impractical. In this study, we developed a deep-learning-based approach to improve CBCT image quality and HU accuracy for potential extended clinical use in CBCT-guided pancreatic adaptive radiotherapy.

METHODS

Thirty patients previously treated with pancreas SBRT were included. The CBCT acquired prior to the first fraction of treatment was registered to the planning CT for training and generation of synthetic CT (sCT). A self-attention cycle generative adversarial network (cycleGAN) was used to generate CBCT-based sCT. For the cohort of 30 patients, the CT-based contours and treatment plans were transferred to the first fraction CBCTs and sCTs for dosimetric comparison.

RESULTS

At the site of abdomen, mean absolute error (MAE) between CT and sCT was 56.89 ± 13.84 HU, comparing to 81.06 ± 15.86 HU between CT and the raw CBCT. No significant differences (P > 0.05) were observed in the PTV and OAR dose-volume-histogram (DVH) metrics between the CT- and sCT-based plans, while significant differences (P < 0.05) were found between the CT- and the CBCT-based plans.

CONCLUSIONS

The image similarity and dosimetric agreement between the CT and sCT-based plans validated the dose calculation accuracy carried by sCT. The CBCT-based sCT approach can potentially increase treatment precision and thus minimize gastrointestinal toxicity.

Interactive 3D U-net for the segmentation of the pancreas in computed tomography scans

The increasing incidence of pancreatic cancer will make it the second deadliest cancer in 2030. Imaging based early diagnosis and image guided treatment are emerging potential solutions. Artificial intelligence (AI) can help provide and improve widespread diagnostic expertise and accurate interventional image interpretation. Accurate segmentation of the pancreas is essential to create annotated data sets to train AI, and for computer assisted interventional guidance. Automated deep learning segmentation performance in pancreas computed tomography (CT) imaging is low due to poor grey value contrast and complex anatomy. A good solution seemed a recent interactive deep learning segmentation framework for brain CT that helped strongly improve initial automated segmentation with minimal user input. This method yielded no satisfactory results for pancreas CT, possibly due to a sub-optimal neural network architecture. We hypothesize that a state-of-the-art U-net neural network architecture is better because it can produce a better initial segmentation and is likely to be extended to work in a similar interactive approach. We implemented the existing interactive method, iFCN, and developed an interactive version of U-net method we call iUnet. The iUnet is fully trained to produce the best possible initial segmentation. In interactive mode it is additionally trained on a partial set of layers on user generated scribbles. We compare initial segmentation performance of iFCN and iUnet on a 100CT dataset using dice similarity coefficient analysis. Secondly, we assessed the performance gain in interactive use with three observers on segmentation quality and time. Average automated baseline performance was 78% (iUnet) versus 72% (FCN). Manual and semi-automatic segmentation performance was: 87% in 15 min. for manual, and 86% in 8 min. for iUNet. We conclude that iUnet provides a better baseline than iFCN and can reach expert manual performance significantly faster than manual segmentation in case of pancreas CT. Our novel iUnet architecture is modality and organ agnostic and can be a potential novel solution for semi-automatic medical imaging segmentation in general.

LinSEM: Linearizing segmentation evaluation metrics for medical images

Numerous algorithms are available for segmenting medical images. Empirical discrepancy metrics are commonly used in measuring the similarity or difference between segmentations by algorithms and "true" segmentations. However, one issue with the commonly used metrics is that the same metric value often represents different levels of "clinical acceptability" for different objects depending on their size, shape, and complexity of form. An ideal segmentation evaluation metric should be able to reflect degrees of acceptability directly from metric values and be able to show the same acceptability meaning by the same metric value for objects of different shape, size, and form. Intuitively, metrics which have a linear relationship with degree of acceptability will satisfy these conditions of the ideal metric. This issue has not been addressed in the medical image segmentation literature. In this paper, we propose a method called LinSEM for linearizing commonly used segmentation evaluation metrics based on corresponding degrees of acceptability evaluated by an expert in a reader study. LinSEM consists of two main parts: (a) estimating the relationship between metric values and degrees of acceptability separately for each considered metric and object, and (b) linearizing any given metric value corresponding to a given segmentation of an object based on the estimated relationship. Since algorithmic segmentations do not usually cover the full range of variability of acceptability, we create a set (SS) of simulated segmentations for each object that guarantee such coverage by using image transformations applied to a set (ST) of true segmentations of the object. We then conduct a reader study wherein the reader assigns an acceptability score (AS) for each sample in SS, expressing the acceptability of the sample on a 1 to 5 scale. Then the metric-AS relationship is constructed for the object by using an estimation method. With the idea that the ideal metric should be linear with respect to acceptability, we can then linearize the metric value of any segmentation sample of the object from a set (SA) of actual segmentations to its linearized value by using the constructed metric-acceptability relationship curve. Experiments are conducted involving three metrics - Dice coefficient (DC), Jaccard index (JI), and Hausdorff Distance (HD) - on five objects: skin outer boundary of the head and neck (cervico-thoracic) body region superior to the shoulders, right parotid gland, mandible, cervical esophagus, and heart. Actual segmentations (SA) of these objects are generated via our Automatic Anatomy Recognition (AAR) method. Our results indicate that, generally, JI has a more linear relationship with acceptability before linearization than other metrics. LinSEM achieves significantly improved uniformity of meaning post-linearization across all tested objects and metrics, except in a few cases where the departure from linearity was insignificant. This improvement is generally the largest for DC and HD reaching 8-25% for many tested cases. Although some objects (such as right parotid gland and esophagus for DC and JI) are close in their meaning between themselves before linearization, they are distant in this meaning from other objects but are brought close to other objects after linearization. This suggests the importance of performing linearization considering all objects in a body region and body-wide.