ML-driven segmentation of microvascular features during histological examination of tissue-engineered vascular grafts.

Introduction: The development of next-generation tissue-engineered medical devices such as tissue-engineered vascular grafts (TEVGs) is a leading trend in translational medicine. Microscopic examination is an indispensable part of animal experimentation, and histopathological analysis of regenerated tissue is crucial for assessing the outcomes of implanted medical devices. However, the objective quantification of regenerated tissues can be challenging due to their unusual and complex architecture. To address these challenges, research and development of advanced ML-driven tools for performing adequate histological analysis appears to be an extremely promising direction. Methods: We compiled a dataset of 104 representative whole slide images (WSIs) of TEVGs which were collected after a 6-month implantation into the sheep carotid artery. The histological examination aimed to analyze the patterns of vascular tissue regeneration in TEVGs in situ. Having performed an automated slicing of these WSIs by the Entropy Masker algorithm, we filtered and then manually annotated 1,401 patches to identify 9 histological features: arteriole lumen, arteriole media, arteriole adventitia, venule lumen, venule wall, capillary lumen, capillary wall, immune cells, and nerve trunks. To segment and quantify these features, we rigorously tuned and evaluated the performance of six deep learning models (U-Net, LinkNet, FPN, PSPNet, DeepLabV3, and MA-Net). Results: After rigorous hyperparameter optimization, all six deep learning models achieved mean Dice Similarity Coefficients (DSC) exceeding 0.823. Notably, FPN and PSPNet exhibited the fastest convergence rates. MA-Net stood out with the highest mean DSC of 0.875, demonstrating superior performance in arteriole segmentation. DeepLabV3 performed well in segmenting venous and capillary structures, while FPN exhibited proficiency in identifying immune cells and nerve trunks. An ensemble of these three models attained an average DSC of 0.889, surpassing their individual performances. Conclusion: This study showcases the potential of ML-driven segmentation in the analysis of histological images of tissue-engineered vascular grafts. Through the creation of a unique dataset and the optimization of deep neural network hyperparameters, we developed and validated an ensemble model, establishing an effective tool for detecting key histological features essential for understanding vascular tissue regeneration. These advances herald a significant improvement in ML-assisted workflows for tissue engineering research and development.

A fully automated approach to prostate biopsy segmentation based on level-set and mean filtering.

With modern automated microscopes and digital cameras, pathologists no longer have to examine samples looking through microscope binoculars. Instead, the slide is digitized to an image, which can then be examined on a screen. This creates the possibility for computers to analyze the image. In this work, a fully automated approach to region of interest (ROI) segmentation in prostate biopsy images is proposed. This will allow the pathologists to focus on the most important areas of the image. The method proposed is based on level-set and mean filtering techniques for lumen centered expansion and cell density localization respectively. The novelty of the technique lies in the ability to detect complete ROIs, where a ROI is composed by the conjunction of three different structures, that is, lumen, cytoplasm, and cells, as well as regions with a high density of cells. The method is capable of dealing with full biopsies digitized at different magnifications. In this paper, results are shown with a set of 100 H and E slides, digitized at 5×, and ranging from 12 MB to 500 MB. The tests carried out show an average specificity above 99% across the board and average sensitivities of 95% and 80%, respectively, for the lumen centered expansion and cell density localization. The algorithms were also tested with images at 10× magnification (up to 1228 MB) obtaining similar results.