Purpose: Transrectal ultrasound (TRUS) is a versatile and real-time imaging modality that is commonly used in image-guided prostate cancer interventions (e.g., biopsy and brachytherapy). Accurate segmentation of the prostate is key to biopsy needle placement, brachytherapy treatment planning, and motion management. Manual segmentation during these interventions is time-consuming and subject to inter-and intraobserver variation. To address these drawbacks, we aimed to develop a deep learning-based method which integrates deep supervision into a three-dimensional (3D) patch-based V-Net for prostate segmentation. Methods and materials: We developed a multidirectional deep-learning-based method to automatically segment the prostate for ultrasound-guided radiation therapy. A 3D supervision mechanism is integrated into the V-Net stages to deal with the optimization difficulties when training a deep network with limited training data. We combine a binary cross-entropy (BCE) loss and a batch-based Dice loss into the stage-wise hybrid loss function for a deep supervision training. During the segmentation stage, the patches are extracted from the newly acquired ultrasound image as the input of the well-trained network and the well-trained network adaptively labels the prostate tissue. The final segmented prostate volume is reconstructed using patch fusion and further refined through a contour refinement processing. Results: Forty-four patients' TRUS images were used to test our segmentation method. Our segmentation results were compared with the manually segmented contours (ground truth). The mean prostate volume Dice similarity coefficient (DSC), Hausdorff distance (HD), mean surface distance (MSD), and residual mean surface distance (RMSD) were 0.92 AE 0.03, 3.94 AE 1.55, 0.60 AE 0.23, and 0.90 AE 0.38 mm, respectively. Conclusion: We developed a novel deeply supervised deep learning-based approach with reliable contour refinement to automatically segment the TRUS prostate, demonstrated its clinical feasibility, and validated its accuracy compared to manual segmentation. The proposed technique could be a useful tool for diagnostic and therapeutic applications in prostate cancer.

Purpose Automatic breast ultrasound (ABUS) imaging has become an essential tool in breast cancer diagnosis since it provides complementary information to other imaging modalities. Lesion segmentation on ABUS is a prerequisite step of breast cancer computer‐aided diagnosis (CAD). This work aims to develop a deep learning‐based method for breast tumor segmentation using three‐dimensional (3D) ABUS automatically. Methods For breast tumor segmentation in ABUS, we developed a Mask scoring region‐based convolutional neural network (R‐CNN) that consists of five subnetworks, that is, a backbone, a regional proposal network, a region convolutional neural network head, a mask head, and a mask score head. A network block building direct correlation between mask quality and region class was integrated into a Mask scoring R‐CNN based framework for the segmentation of new ABUS images with ambiguous regions of interest (ROIs). For segmentation accuracy evaluation, we retrospectively investigated 70 patients with breast tumor confirmed with needle biopsy and manually delineated on ABUS, of which 40 were used for fivefold cross‐validation and 30 were used for hold‐out test. The comparison between the automatic breast tumor segmentations and the manual contours was quantified by I) six metrics including Dice similarity coefficient (DSC), Jaccard index, 95% Hausdorff distance (HD95), mean surface distance (MSD), residual mean square distance (RMSD), and center of mass distance (CMD); II) Pearson correlation analysis and Bland–Altman analysis. Results The mean (median) DSC was 85% ± 10.4% (89.4%) and 82.1% ± 14.5% (85.6%) for cross‐validation and hold‐out test, respectively. The corresponding HD95, MSD, RMSD, and CMD of the two tests was 1.646 ± 1.191 and 1.665 ± 1.129 mm, 0.489 ± 0.406 and 0.475 ± 0.371 mm, 0.755 ± 0.755 and 0.751 ± 0.508 mm, and 0.672 ± 0.612 and 0.665 ± 0.729 mm. The mean volumetric difference (mean and ± 1.96 standard deviation) was 0.47 cc ([−0.77, 1.71)) for the cross‐validation and 0.23 cc ([−0.23 0.69]) for hold‐out test, respectively. Conclusion We developed a novel Mask scoring R‐CNN approach for the automated segmentation of the breast tumor in ABUS images and demonstrated its accuracy for breast tumor segmentation. Our learning‐based method can potentially assist the clinical CAD of breast cancer using 3D ABUS imaging.

The cancer cells obtain their invasion potential not only by genetic mutations, but also by changing their cellular biophysical and biomechanical features and adapting to the surrounding microenvironments. The extracellular matrix, as a crucial component of the tumor microenvironment, provides the mechanical support for the tissue, mediates the cell-microenvironment interactions, and plays a key role in cancer cell invasion. The biomechanics of the extracellular matrix, particularly collagen, have been extensively studied in the biomechanics community. Cell migration has also enjoyed much attention from both the experimental and modeling efforts. However, the detailed mechanistic understanding of tumor cell-ECM interactions, especially during cancer invasion, has been unclear. This chapter reviews the recent advances in the studies of ECM biomechanics, cell migration, and cell-ECM interactions in the context of cancer invasion.

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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.