DeepRecS: From RECIST Diameters to Precise Liver Tumor Segmentation

A paired multi-scale attention network for liver tumor segmentation in 99mTc-MAA SPECT/CT imaging

Automatic liver tumor segmentation using Single-Photon Emission Computed Tomography/Computed Tomography (SPECT/CT) provides detailed insights that facilitate accurate tumor targeting and effective treatment planning. However, challenges such as spill-out can distort tumor size, which complicates accurate segmentation. This study introduces a Paired Multi-Scale Attention Network (P-MANet) using Top-Hat Cross-Features to address these challenges by enhancing the identification of true positives while minimizing false positives in liver tumor segmentation. P-MANet employs a dual-branch architecture. The first branch utilizes a Multi-Scale Attention Network (MA-Net) to process SPECT/CT datasets, whereas the second branch applies the White Top-Hat Transform to extract features that are then integrated with those from the first branch. This innovative approach effectively mitigates errors stemming from spectral light variation, leading to improved accuracy in tumor delineation. The model was trained on a dataset comprising 43 cases of 99mTc-MAA SPECT/CT. P-MANet achieved a Dice Similarity Coefficient (DSC) of 67.93% and 66.56% for normal and abnormal spectral light distributions, respectively, and obtained a DSC of 67.00%, on average, which outperformed other models, including results from a previous study that was tested on the same dataset.

A Comparative Study of Decoders for Liver and Tumor Segmentation Using a Self-ONN-Based Cascaded Framework

Background/Objectives: Accurate liver and tumor detection and segmentation are crucial in diagnosis of early-stage liver malignancies. As opposed to manual interpretation, which is a difficult and time-consuming process, accurate tumor detection using a computer-aided diagnosis system can save both time and human efforts. Methods: We propose a cascaded encoder-decoder technique based on self-organized neural networks, which is a recent variant of operational neural networks (ONNs), for accurate segmentation and identification of liver tumors. The first encoder-decoder CNN segments the liver. For generating the liver region of interest, the segmented liver mask is placed over the input computed tomography (CT) image and then fed to the second Self-ONN model for tumor segmentation. For further investigation the other three distinct encoder-decoder architectures U-Net, feature pyramid networks (FPNs), and U-Net++, have also been investigated by altering the backbone at the encoders utilizing ResNet and DenseNet variants for transfer learning. Results: For the liver segmentation task, Self-ONN with a ResNet18 backbone has achieved a dice similarity coefficient score of 98.182% and an intersection over union of 97.436%. Tumor segmentation with Self-ONN with the DenseNet201 encoder resulted in an outstanding DSC of 92.836% and IoU of 91.748%. Conclusions: The suggested method is capable of precisely locating liver tumors of various sizes and shapes, including tiny infection patches that were said to be challenging to find in earlier research.

Tumor therapeutics in the era of "RECIST": past, current insights, and future prospects

In recent years, advancements in medical treatment and imaging technologies have revolutionized the assessment of tumor response. However, the Response Evaluation Criteria in Solid Tumors (RECIST) has long been established as the gold standard for evaluating tumor treatment. As treatment modalities evolve, the need for continuous refinement and adaptation of RECIST becomes increasingly apparent. This review explores the historical evolution, current applications, limitations, and future directions of RECIST. It discusses the challenges of distinguishing true progression from pseudo-progression in ICIs (immune checkpoint inhibitors), the integration of advanced imaging tools, and the necessity for RECIST criteria tailored to specific therapies like neoadjuvant treatments. The review highlights the ongoing efforts to enhance RECIST's accuracy and reliability in clinical decision-making and the potential for developing new standards to better evaluate treatment efficacy in the rapidly evolving landscape of oncology.

Artificial intelligence techniques in liver cancer

Hepatocellular Carcinoma (HCC), the most common primary liver cancer, is a significant contributor to worldwide cancer-related deaths. Various medical imaging techniques, including computed tomography, magnetic resonance imaging, and ultrasound, play a crucial role in accurately evaluating HCC and formulating effective treatment plans. Artificial Intelligence (AI) technologies have demonstrated potential in supporting physicians by providing more accurate and consistent medical diagnoses. Recent advancements have led to the development of AI-based multi-modal prediction systems. These systems integrate medical imaging with other modalities, such as electronic health record reports and clinical parameters, to enhance the accuracy of predicting biological characteristics and prognosis, including those associated with HCC. These multi-modal prediction systems pave the way for predicting the response to transarterial chemoembolization and microvascular invasion treatments and can assist clinicians in identifying the optimal patients with HCC who could benefit from interventional therapy. This paper provides an overview of the latest AI-based medical imaging models developed for diagnosing and predicting HCC. It also explores the challenges and potential future directions related to the clinical application of AI techniques.

Sparse deep belief network coupled with extended local fuzzy active contour model-based liver cancer segmentation from abdomen CT images

Liver cancer from abdominal CT images must be accurately segmented for the purpose of diagnosis with treatment planning. But, the similarity in gray values between the liver and the surrounding tissues poses a challenge. To address this, a novel sparse deep belief network coupled with extended local fuzzy active contour model-based liver cancer segmentation from abdomen CT images (SDBN-ELFAC-LCS-CT) is proposed. This method incorporates dynamic adaptive pooling and residual modules in SDBN to improve the feature selection and generalization ability. Additionally, the 3D reconstruction is performed to refine segmentation results. The proposed SDBN-ELFAC-LCS-CT approach is implemented in MATLAB. The performance of the proposed SDBN-ELFAC-LCS-CT achieves dice coefficients that were up to 96.16% higher and 75.88%, 88.75%, and 71.16% lower. Volumetric overlap error compared with existing models, like basic ensembles of vanilla-style deep learning modes, increases liver segmentation from CT imageries (BEVS-LCS-CT), an incorporated 3 dimensional sparse deep belief network along enriched seagull optimization approach for liver segmentation (3DBN-ESOA-LCS-CT) and iterative convolutional encoder-decoder network and multiple scale context learning for segmenting liver (ICEDN-LCS-CT), respectively.

Lung Nodule Segmentation and Uncertain Region Prediction With an Uncertainty-Aware Attention Mechanism

Radiologists possess diverse training and clinical experiences, leading to variations in the segmentation annotations of lung nodules and resulting in segmentation uncertainty. Conventional methods typically select a single annotation as the learning target or attempt to learn a latent space comprising multiple annotations. However, these approaches fail to leverage the valuable information inherent in the consensus and disagreements among the multiple annotations. In this paper, we propose an Uncertainty-Aware Attention Mechanism (UAAM) that utilizes consensus and disagreements among multiple annotations to facilitate better segmentation. To this end, we introduce the Multi-Confidence Mask (MCM), which combines a Low-Confidence (LC) Mask and a High-Confidence (HC) Mask. The LC mask indicates regions with low segmentation confidence, where radiologists may have different segmentation choices. Following UAAM, we further design an Uncertainty-Guide Multi-Confidence Segmentation Network (UGMCS-Net), which contains three modules: a Feature Extracting Module that captures a general feature of a lung nodule, an Uncertainty-Aware Module that produces three features for the annotations' union, intersection, and annotation set, and an Intersection-Union Constraining Module that uses distances between the three features to balance the predictions of final segmentation and MCM. To comprehensively demonstrate the performance of our method, we propose a Complex-Nodule Validation on LIDC-IDRI, which tests UGMCS-Net's segmentation performance on lung nodules that are difficult to segment using common methods. Experimental results demonstrate that our method can significantly improve the segmentation performance on nodules that are difficult to segment using conventional methods.

Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review

OBJECTIVES

Machine learning (ML) for medical imaging is emerging for several organs and image modalities. Our objectives were to provide clinicians with an overview of this field by answering the following questions: (1) How is ML applied in liver computed tomography (CT) imaging? (2) How well do ML systems perform in liver CT imaging? (3) What are the clinical applications of ML in liver CT imaging?

METHODS

A systematic review was carried out according to the guidelines from the PRISMA-P statement. The search string focused on studies containing content relating to artificial intelligence, liver, and computed tomography.

RESULTS

One hundred ninety-one studies were included in the study. ML was applied to CT liver imaging by image analysis without clinicians' intervention in majority of studies while in newer studies the fusion of ML method with clinical intervention have been identified. Several were documented to perform very accurately on reliable but small data. Most models identified were deep learning-based, mainly using convolutional neural networks. Potentially many clinical applications of ML to CT liver imaging have been identified through our review including liver and its lesion segmentation and classification, segmentation of vascular structure inside the liver, fibrosis and cirrhosis staging, metastasis prediction, and evaluation of chemotherapy.

CONCLUSION

Several studies attempted to provide transparent result of the model. To make the model convenient for a clinical application, prospective clinical validation studies are in urgent call. Computer scientists and engineers should seek to cooperate with health professionals to ensure this.

KEY POINTS

• ML shows great potential for CT liver image tasks such as pixel-wise segmentation and classification of liver and liver lesions, fibrosis staging, metastasis prediction, and retrieval of relevant liver lesions from similar cases of other patients. • Despite presenting the result is not standardized, many studies have attempted to provide transparent results to interpret the machine learning method performance in the literature. • Prospective studies are in urgent call for clinical validation of ML method, preferably carried out by cooperation between clinicians and computer scientists.

Self-Supervised Tumor Segmentation With Sim2Real Adaptation

This paper targets on self-supervised tumor segmentation. We make the following contributions: (i) we take inspiration from the observation that tumors are often characterised independently of their contexts, we propose a novel proxy task "layer-decomposition", that closely matches the goal of the downstream task, and design a scalable pipeline for generating synthetic tumor data for pre-training; (ii) we propose a two-stage Sim2Real training regime for unsupervised tumor segmentation, where we first pre-train a model with simulated tumors, and then adopt a self-training strategy for downstream data adaptation; (iii) when evaluating on different tumor segmentation benchmarks, e.g. BraTS2018 for brain tumor segmentation and LiTS2017 for liver tumor segmentation, our approach achieves state-of-the-art segmentation performance under the unsupervised setting. While transferring the model for tumor segmentation under a low-annotation regime, the proposed approach also outperforms all existing self-supervised approaches; (iv) we conduct extensive ablation studies to analyse the critical components in data simulation, and validate the necessity of different proxy tasks. We demonstrate that, with sufficient texture randomization in simulation, model trained on synthetic data can effortlessly generalise to datasets with real tumors.

Slice-Fusion: Reducing False Positives in Liver Tumor Detection for Mask R-CNN

Automatic liver tumor detection from computed tomography (CT) makes clinical examinations more accurate. However, deep learning-based detection algorithms are characterized by high sensitivity and low precision, which hinders diagnosis given that false-positive tumors must first be identified and excluded. These false positives arise because detection models incorrectly identify partial volume artifacts as lesions, which in turn stems from the inability to learn the perihepatic structure from a global perspective. To overcome this limitation, we propose a novel slice-fusion method in which mining the global structural relationship between the tissues in the target CT slices and fusing the features of adjacent slices according to the importance of the tissues. Furthermore, we design a new network based on our slice-fusion method and Mask R-CNN detection model, called Pinpoint-Net. We evaluated proposed model on the Liver Tumor Segmentation Challenge (LiTS) dataset and our liver metastases dataset. Experiments demonstrated that our slice-fusion method not only enhance tumor detection ability via reducing the number of false-positive tumors smaller than 10mm, but also improve segmentation performance. Without bells and whistles, a single Pinpoint-Net showed outstanding performance in liver tumor detection and segmentation on LiTS test dataset compared with other state-of-the-art models.

Deep learning techniques in liver tumour diagnosis using CT and MR imaging - A systematic review

Deep learning has become a thriving force in the computer aided diagnosis of liver cancer, as it solves extremely complicated challenges with high accuracy over time and facilitates medical experts in their diagnostic and treatment procedures. This paper presents a comprehensive systematic review on deep learning techniques applied for various applications pertaining to liver images, challenges faced by the clinicians in liver tumour diagnosis and how deep learning bridges the gap between clinical practice and technological solutions with an in-depth summary of 113 articles. Since, deep learning is an emerging revolutionary technology, recent state-of-the-art research implemented on liver images are reviewed with more focus on classification, segmentation and clinical applications in the management of liver diseases. Additionally, similar review articles in literature are reviewed and compared. The review is concluded by presenting the contemporary trends and unaddressed research issues in the field of liver tumour diagnosis, offering directions for future research in this field.