ML-driven segmentation of microvascular features during histological examination of tissue-engineered vascular grafts
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- dc.contributor.author Danilov, Viacheslav V.
- dc.contributor.author Laptev, Vladislav V.
- dc.contributor.author Klyshnikov, Kirill Yu
- dc.contributor.author Stepanov, Alexander D.
- dc.contributor.author Bogdanov, Leo A.
- dc.contributor.author Antonova, Larisa V.
- dc.contributor.author Krivkina, Eugenia O.
- dc.contributor.author Kutikhin, Anton G.
- dc.contributor.author Ovcharenko, Evgeny A.
- dc.date.accessioned 2025-06-02T08:19:01Z
- dc.date.available 2025-06-02T08:19:01Z
- dc.date.issued 2024
- dc.description.abstract 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.
- dc.format.mimetype application/pdf
- dc.identifier.citation Danilov VV, Laptev VV, Klyshnikov KY, Stepanov AD, Bogdanov LA, Antonova LV, et al. ML-driven segmentation of microvascular features during histological examination of tissue-engineered vascular grafts. Front Bioeng Biotechnol. 2024;12:1411680. DOI: 10.3389/fbioe.2024.1411680
- dc.identifier.doi http://dx.doi.org/10.3389/fbioe.2024.1411680
- dc.identifier.issn 2296-4185
- dc.identifier.uri http://hdl.handle.net/10230/70589
- dc.language.iso eng
- dc.publisher Frontiers
- dc.relation.ispartof Frontiers in Bioengineering and Biotechnology. 2024;12:1411680
- dc.rights © 2024 Danilov, Laptev, Klyshnikov, Stepanov, Bogdanov, Antonova, Krivkina, Kutikhin and Ovcharenko. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.rights.uri http://creativecommons.org/licenses/by/4.0/
- dc.subject.keyword Histological segmentation
- dc.subject.keyword Deep learning
- dc.subject.keyword Vascular tissue engineering
- dc.subject.keyword Digital pathology
- dc.subject.keyword Tissue-engineered vascular grafts
- dc.title ML-driven segmentation of microvascular features during histological examination of tissue-engineered vascular grafts
- dc.type info:eu-repo/semantics/article
- dc.type.version info:eu-repo/semantics/publishedVersion