Multi-Atlas Segmentation of MR Tumor Brain Images Using Low-Rank Based Image Recovery

Multimodal Brain Tumor Segmentation Boosted by Monomodal Normal Brain Images

Many deep learning based methods have been proposed for brain tumor segmentation. Most studies focus on deep network internal structure to improve the segmentation accuracy, while valuable external information, such as normal brain appearance, is often ignored. Inspired by the fact that radiologists often screen lesion regions with normal appearance as reference in mind, in this paper, we propose a novel deep framework for brain tumor segmentation, where normal brain images are adopted as reference to compare with tumor brain images in a learned feature space. In this way, features at tumor regions, i.e., tumor-related features, can be highlighted and enhanced for accurate tumor segmentation. It is known that routine tumor brain images are multimodal, while normal brain images are often monomodal. This causes the feature comparison a big issue, i.e., multimodal vs. monomodal. To this end, we present a new feature alignment module (FAM) to make the feature distribution of monomodal normal brain images consistent/inconsistent with multimodal tumor brain images at normal/tumor regions, making the feature comparison effective. Both public (BraTS2022) and in-house tumor brain image datasets are used to evaluate our framework. Experimental results demonstrate that for both datasets, our framework can effectively improve the segmentation accuracy and outperforms the state-of-the-art segmentation methods. Codes are available at https://github.com/hb-liu/Normal-Brain-Boost-Tumor-Segmentation.

Unsupervised deep learning registration model for multimodal brain images

Multimodal image registration is a key for many clinical image-guided interventions. However, it is a challenging task because of complicated and unknown relationships between different modalities. Currently, deep supervised learning is the state-of-theart method at which the registration is conducted in end-to-end manner and one-shot. Therefore, a huge ground-truth data is required to improve the results of deep neural networks for registration. Moreover, supervised methods may yield models that bias towards annotated structures. Here, to deal with above challenges, an alternative approach is using unsupervised learning models. In this study, we have designed a novel deep unsupervised Convolutional Neural Network (CNN)-based model based on computer tomography/magnetic resonance (CT/MR) co-registration of brain images in an affine manner. For this purpose, we created a dataset consisting of 1100 pairs of CT/MR slices from the brain of 110 neuropsychic patients with/without tumor. At the next step, 12 landmarks were selected by a well-experienced radiologist and annotated on each slice resulting in the computation of series of metrics evaluation, target registration error (TRE), Dice similarity, Hausdorff, and Jaccard coefficients. The proposed method could register the multimodal images with TRE 9.89, Dice similarity 0.79, Hausdorff 7.15, and Jaccard 0.75 that are appreciable for clinical applications. Moreover, the approach registered the images in an acceptable time 203 ms and can be appreciable for clinical usage due to the short registration time and high accuracy. Here, the results illustrated that our proposed method achieved competitive performance against other related approaches from both reasonable computation time and the metrics evaluation.

EG-Unet: Edge-Guided cascaded networks for automated frontal brain segmentation in MR images

Accurate segmentation of frontal lobe areas on magnetic resonance imaging (MRI) can assist in diagnosing and managing idiopathic normal-pressure hydrocephalus. However, frontal lobe segmentation is challenging due to the complexity of the degree and shape of damage and the ambiguity of the boundaries of frontal lobe sites. Therefore, to extract the rich edge information and feature representation of the frontal lobe, this paper designs an edge guidance (EG) module to enhance the representation of edge features. Accordingly, an edge-guided cascade network framework (EG-Net) is proposed to segment frontal lobe parts automatically. Two-dimensional MRI slice images are fed into the edge generation and segmentation networks. First, the edge generation network extracts the edge information from the input image. Then, the edge information is sent to the EG module to generate an edge attention map for feature representation enhancement. Meanwhile, multi-scale attentional convolution (MSA) is utilized in the feature coding stage of the segmentation network to obtain feature responses from different perceptual fields in the coding stage and enrich the spatial context information. Besides, the feature fusion module is employed to selectively aggregate the multi-scale features in the coding stage with the edge features output by the EG module. Finally, the two components are fused, and a decoder recovers the spatial information to generate the final prediction results. An extensive quantitative comparison is performed on a publicly available brain MRI dataset (MICCAI 2012) to evaluate the effectiveness of the proposed algorithm. The experimental results indicate that the proposed method achieves an average DICE score of 95.77% compared to some advanced methods, which is 4.96% better than the classical U-Net. The results demonstrate the potential of the proposed EG-Net in improving the accuracy of frontal edge pixel classification through edge guidance.

Integrated 3d flow-based multi-atlas brain structure segmentation

MRI brain structure segmentation plays an important role in neuroimaging studies. Existing methods either spend much CPU time, require considerable annotated data, or fail in segmenting volumes with large deformation. In this paper, we develop a novel multi-atlas-based algorithm for 3D MRI brain structure segmentation. It consists of three modules: registration, atlas selection and label fusion. Both registration and label fusion leverage an integrated flow based on grayscale and SIFT features. We introduce an effective and efficient strategy for atlas selection by employing the accompanying energy generated in the registration step. A 3D sequential belief propagation method and a 3D coarse-to-fine flow matching approach are developed in both registration and label fusion modules. The proposed method is evaluated on five public datasets. The results show that it has the best performance in almost all the settings compared to competitive methods such as ANTs, Elastix, Learning to Rank and Joint Label Fusion. Moreover, our registration method is more than 7 times as efficient as that of ANTs SyN, while our label transfer method is 18 times faster than Joint Label Fusion in CPU time. The results on the ADNI dataset demonstrate that our method is applicable to image pairs that require a significant transformation in registration. The performance on a composite dataset suggests that our method succeeds in a cross-modality manner. The results of this study show that the integrated 3D flow-based method is effective and efficient for brain structure segmentation. It also demonstrates the power of SIFT features, multi-atlas segmentation and classical machine learning algorithms for a medical image analysis task. The experimental results on public datasets show the proposed method's potential for general applicability in various brain structures and settings.

A Segmentation Method of Foramen Ovale Based on Multiatlas

Trigeminal neuralgia is a neurological disease. It is often treated by puncturing the trigeminal nerve through the skin and the oval foramen of the skull to selectively destroy the pain nerve. The process of puncture operation is difficult because the morphology of the foramen ovale in the skull base is varied and the surrounding anatomical structure is complex. Computer-aided puncture guidance technology is extremely valuable for the treatment of trigeminal neuralgia. Computer-aided guidance can help doctors determine the puncture target by accurately locating the foramen ovale in the skull base. Foramen ovale segmentation is a prerequisite for locating but is a tedious and error-prone task if done manually. In this paper, we present an image segmentation solution based on the multiatlas method that automatically segments the foramen ovale. We developed a data set of 30 CT scans containing 20 foramen ovale atlas and 10 CT scans for testing. Our approach can perform foramen ovale segmentation in puncture operation scenarios based solely on limited data. We propose to utilize this method as an enabler in clinical work.

Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images

Brain tumor localization and segmentation from magnetic resonance imaging (MRI) are hard and important tasks for several applications in the field of medical analysis. As each brain imaging modality gives unique and key details related to each part of the tumor, many recent approaches used four modalities T1, T1c, T2, and FLAIR. Although many of them obtained a promising segmentation result on the BRATS 2018 dataset, they suffer from a complex structure that needs more time to train and test. So, in this paper, to obtain a flexible and effective brain tumor segmentation system, first, we propose a preprocessing approach to work only on a small part of the image rather than the whole part of the image. This method leads to a decrease in computing time and overcomes the overfitting problems in a Cascade Deep Learning model. In the second step, as we are dealing with a smaller part of brain images in each slice, a simple and efficient Cascade Convolutional Neural Network (C-ConvNet/C-CNN) is proposed. This C-CNN model mines both local and global features in two different routes. Also, to improve the brain tumor segmentation accuracy compared with the state-of-the-art models, a novel Distance-Wise Attention (DWA) mechanism is introduced. The DWA mechanism considers the effect of the center location of the tumor and the brain inside the model. Comprehensive experiments are conducted on the BRATS 2018 dataset and show that the proposed model obtains competitive results: the proposed method achieves a mean whole tumor, enhancing tumor, and tumor core dice scores of 0.9203, 0.9113 and 0.8726 respectively. Other quantitative and qualitative assessments are presented and discussed.

[A tissue recovery-based brain tumor image registration method]

We propose an algorithm for registration between brain tumor images and normal brain images based on tissue recovery. U-Net is first used in BraTS2018 dataset to segment the brain tumors, and PConv-Net is then used to simulate the generation of missing normal tissues in the tumor region to replace the tumor region. Finally, the normal brain image is registered to the tissue recovery brain image. We evaluated the effectiveness of this method by comparing the registration results of the repaired image and the tumor image corresponding to the surrounding tissues of the tumor area. The experimental results showed that the proposed method could reduce the effect of pathological variation, achieve a high registration accuracy, and effectively simulate and generate normal tissues to replace the tumor regions, thus improving the registration effect between brain tumor images and normal brain images.

Deep Learning for Multigrade Brain Tumor Classification in Smart Healthcare Systems: A Prospective Survey

Brain tumor is one of the most dangerous cancers in people of all ages, and its grade recognition is a challenging problem for radiologists in health monitoring and automated diagnosis. Recently, numerous methods based on deep learning have been presented in the literature for brain tumor classification (BTC) in order to assist radiologists for a better diagnostic analysis. In this overview, we present an in-depth review of the surveys published so far and recent deep learning-based methods for BTC. Our survey covers the main steps of deep learning-based BTC methods, including preprocessing, features extraction, and classification, along with their achievements and limitations. We also investigate the state-of-the-art convolutional neural network models for BTC by performing extensive experiments using transfer learning with and without data augmentation. Furthermore, this overview describes available benchmark data sets used for the evaluation of BTC. Finally, this survey does not only look into the past literature on the topic but also steps on it to delve into the future of this area and enumerates some research directions that should be followed in the future, especially for personalized and smart healthcare.

An Indirect Multimodal Image Registration and Completion Method Guided by Image Synthesis

Multimodal registration is a challenging task due to the significant variations exhibited from images of different modalities. CT and MRI are two of the most commonly used medical images in clinical diagnosis, since MRI with multicontrast images, together with CT, can provide complementary auxiliary information. The deformable image registration between MRI and CT is essential to analyze the relationships among different modality images. Here, we proposed an indirect multimodal image registration method, i.e., sCT-guided multimodal image registration and problematic image completion method. In addition, we also designed a deep learning-based generative network, Conditional Auto-Encoder Generative Adversarial Network, called CAE-GAN, combining the idea of VAE and GAN under a conditional process to tackle the problem of synthetic CT (sCT) synthesis. Our main contributions in this work can be summarized into three aspects: (1) We designed a new generative network called CAE-GAN, which incorporates the advantages of two popular image synthesis methods, i.e., VAE and GAN, and produced high-quality synthetic images with limited training data. (2) We utilized the sCT generated from multicontrast MRI as an intermediary to transform multimodal MRI-CT registration into monomodal sCT-CT registration, which greatly reduces the registration difficulty. (3) Using normal CT as guidance and reference, we repaired the abnormal MRI while registering the MRI to the normal CT.

Adversarial learning for mono- or multi-modal registration

This paper introduces an unsupervised adversarial similarity network for image registration. Unlike existing deep learning registration methods, our approach can train a deformable registration network without the need of ground-truth deformations and specific similarity metrics. We connect a registration network and a discrimination network with a deformable transformation layer. The registration network is trained with the feedback from the discrimination network, which is designed to judge whether a pair of registered images are sufficiently similar. Using adversarial training, the registration network is trained to predict deformations that are accurate enough to fool the discrimination network. The proposed method is thus a general registration framework, which can be applied for both mono-modal and multi-modal image registration. Experiments on four brain MRI datasets and a multi-modal pelvic image dataset indicate that our method yields promising registration performance in accuracy, efficiency and generalizability compared with state-of-the-art registration methods, including those based on deep learning.

Label fusion method combining pixel greyscale probability for brain MR segmentation

Multi-atlas-based segmentation (MAS) methods have demonstrated superior performance in the field of automatic image segmentation, and label fusion is an important part of MAS methods. In this paper, we propose a label fusion method that incorporates pixel greyscale probability information. The proposed method combines the advantages of label fusion methods based on sparse representation (SRLF) and weighted voting methods using patch similarity weights (PSWV) and introduces pixel greyscale probability information to improve the segmentation accuracy. We apply the proposed method to the segmentation of deep brain tissues in challenging 3D brain MR images from publicly available IBSR datasets, including images of the thalamus, hippocampus, caudate, putamen, pallidum and amygdala. The experimental results show that the proposed method has higher segmentation accuracy and robustness than the related methods. Compared with the state-of-the-art methods, the proposed method obtains the best putamen, pallidum and amygdala segmentation results and hippocampus and caudate segmentation results that are similar to those of the comparison methods.

Deep MR Brain Image Super-Resolution Using Spatio-Structural Priors

High resolution Magnetic Resonance (MR) images are desired for accurate diagnostics. In practice, image resolution is restricted by factors like hardware and processing constraints. Recently, deep learning methods have been shown to produce compelling state-of-the-art results for image enhancement/super-resolution. Paying particular attention to desired hi-resolution MR image structure, we propose a new regularized network that exploits image priors, namely a low-rank structure and a sharpness prior to enhance deep MR image super-resolution (SR). Our contributions are then incorporating these priors in an analytically tractable fashion as well as towards a novel prior guided network architecture that accomplishes the super-resolution task. This is particularly challenging for the low rank prior since the rank is not a differentiable function of the image matrix (and hence the network parameters), an issue we address by pursuing differentiable approximations of the rank. Sharpness is emphasized by the variance of the Laplacian which we show can be implemented by a fixed feedback layer at the output of the network. As a key extension, we modify the fixed feedback (Laplacian) layer by learning a new set of training data driven filters that are optimized for enhanced sharpness. Experiments performed on publicly available MR brain image databases and comparisons against existing state-of-the-art methods show that the proposed prior guided network offers significant practical gains in terms of improved SNR/image quality measures. Because our priors are on output images, the proposed method is versatile and can be combined with a wide variety of existing network architectures to further enhance their performance.

Deep feature descriptor based hierarchical dense matching for X-ray angiographic images

Backgroud and Objective: X-ray angiography, a powerful technique for blood vessel visualization, is widely used for interventional diagnosis of coronary artery disease because of its fast imaging speed and perspective inspection ability. Matching feature points in angiographic images is a considerably challenging task due to repetitive weak-textured regions.

METHODS

In this paper, we propose an angiographic image matching method based on the hierarchical dense matching framework, where a novel deep feature descriptor is designed to compute multilevel correlation maps. In particular, the deep feature descriptor is computed by a deep learning model specifically designed and trained for angiographic images, thereby making the correlation maps more distinctive for corresponding feature points in different angiographic images. Moreover, point correspondences are further hierarchically extracted from multilevel correlation maps with the highest similarity response(s), which is relatively robust and accurate. To overcome the problem regarding the lack of training samples, the convolutional neural network (designed for deep feature descriptor) is initially trained on samples from natural images and then fine-tuned on manually annotated angiographic images. Finally, a dense matching completion method, based on the distance between deep feature descriptors, is proposed to generate dense matches between images.

RESULTS

The proposed method has been evaluated on the number and accuracy of extracted matches and the performance of subtraction images. Experiments on a variety of angiographic images show promising matching accuracy, compared with state-of-the-art methods.

CONCLUSIONS

The proposed angiographic image matching method is shown to be accurate and effective for feature matching in angiographic images, and further achieves good performance in image subtraction.

Low-Rank Based Image Analyses for Pathological MR Image Segmentation and Recovery

The presence of pathologies in magnetic resonance (MR) brain images causes challenges in various image analysis areas, such as registration, atlas construction and atlas-based segmentation. We propose a novel method for the simultaneous recovery and segmentation of pathological MR brain images. Low-rank and sparse decomposition (LSD) approaches have been widely used in this field, decomposing pathological images into (1) low-rank components as recovered images, and (2) sparse components as pathological segmentation. However, conventional LSD approaches often fail to produce recovered images reliably, due to the lack of constraint between low-rank and sparse components. To tackle this problem, we propose a transformed low-rank and structured sparse decomposition (TLS2D) method. The proposed TLS2D integrates the structured sparse constraint, LSD and image alignment into a unified scheme, which is robust for distinguishing pathological regions. Furthermore, the well recovered images can be obtained using TLS2D with the combined structured sparse and computed image saliency as the adaptive sparsity constraint. The efficacy of the proposed method is verified on synthetic and real MR brain tumor images. Experimental results demonstrate that our method can effectively provide satisfactory image recovery and tumor segmentation.

BIRNet: Brain image registration using dual-supervised fully convolutional networks

In this paper, we propose a deep learning approach for image registration by predicting deformation from image appearance. Since obtaining ground-truth deformation fields for training can be challenging, we design a fully convolutional network that is subject to dual-guidance: (1) Ground-truth guidance using deformation fields obtained by an existing registration method; and (2) Image dissimilarity guidance using the difference between the images after registration. The latter guidance helps avoid overly relying on the supervision from the training deformation fields, which could be inaccurate. For effective training, we further improve the deep convolutional network with gap filling, hierarchical loss, and multi-source strategies. Experiments on a variety of datasets show promising registration accuracy and efficiency compared with state-of-the-art methods.