Artificial Intelligence Assists in the Detection of Blood Vessels in Whole Slide Images: Practical Benefits for Oncological Pathology

Abstract

The analysis of the microvasculature and the assessment of angiogenesis have significant prognostic value in various diseases, including cancer. The search for invasion into the blood and lymphatic vessels and the assessment of angiogenesis are important aspects of oncological diagnosis. These features determine the prognosis and aggressiveness of the tumor. Traditional manual evaluation methods are time consuming and subject to inter-observer variability. Blood vessel detection is a perfect task for artificial intelligence, which is capable of rapid analyzing thousands of tissue structures in whole slide images. The development of computer vision solutions requires the segmentation of tissue regions, the extraction of features and the training of machine learning models. In this review, we focus on the methodologies employed by researchers to identify blood vessels and vascular invasion across a range of tumor localizations, including breast, lung, colon, brain, renal, pancreatic, gastric and oral cavity cancers. Contemporary models herald a new era of computational pathology in morphological diagnostics.

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

“…Thus, there is a consensus that ML approaches are versatile and can be applied to different species and organs ( Komura and Ishikawa, 2018 ; Timakova et al, 2023 ). In clinical practice, deep learning models for blood vessel segmentation have been utilized to define vascularization through specific protein markers (CD31, CD34, and type IV collagen), delineating vessel geometry ( Kather et al, 2015 ; Karageorgos et al, 2024 ).…”

“…Although the role and relative importance of neovascularization strikingly differ between pathophysiological scenarios (e.g., angiogenesis is mostly beneficial in acute ischemic conditions but is detrimental in the context of chronic inflammation), there is currently a consensus that increased amounts of vasa vasorum are associated with vascular inflammation and the severity of arterial stenosis in experimental models and clinical settings ( Mulligan-Kehoe and Simons, 2014 ; Xu et al, 2015 ; Sedding et al, 2018 ; Kostyunin A. et al, 2020 ). Therefore, computer-assisted annotation of microvessels and immune cells in regenerated arteries may enable the discrimination of physiological and pathological patterns of vascular tissue regeneration ( Weis et al, 2015 ; Bogdanov L. A. et al, 2022 ; Adamo et al, 2022 ; Markova et al, 2023 ; Timakova et al, 2023 ). Here, we designed a machine learning tool for the automated demarcation and quantification of blood vessels, immune cell clusters, and nerve trunks in regenerated vascular tissue that replaced biodegradable TEVGs upon their implantation into the ovine carotid artery for 6 months.…”

Advancing Early Leukemia Diagnostics: A Comprehensive Study Incorporating Image Processing and Transfer Learning

“…These discoveries help categorize the illness and guide treatment choices. Nevertheless, differing viewpoints among pathologists frequently result in varying diagnoses [13]. Evaluating blood images manually can be difficult due to issues like noise, blur, and obscured cells, especially in complex situations [14].…”

Presentation1.pdf

“…Although the role and relative importance of neovascularization strikingly differ between pathophysiological scenarios (e.g., angiogenesis is mostly beneficial in acute ischemic conditions but is detrimental in the context of chronic inflammation), there is currently a consensus that increased amounts of vasa vasorum are associated with vascular inflammation and the severity of arterial stenosis in experimental models and clinical settings (Mulligan-Kehoe and Simons, 2014;Xu et al, 2015;Sedding et al, 2018;Kostyunin A. et al, 2020). Therefore, computer-assisted annotation of microvessels and immune cells in regenerated arteries may enable the discrimination of physiological and pathological patterns of vascular tissue regeneration (Weis et al, 2015;Bogdanov L. A. et al, 2022;Adamo et al, 2022;Markova et al, 2023;Timakova et al, 2023). Here, we designed a machine learning tool for the automated demarcation and quantification of blood vessels, immune cell clusters, and nerve trunks in regenerated vascular tissue that replaced biodegradable TEVGs upon their implantation into the ovine carotid artery for 6 months.…”