Journal
BIOMOLECULES
Volume 13, Issue 9, Pages -Publisher
MDPI
DOI: 10.3390/biom13091327
Keywords
digital pathology; deep learning; artificial intelligence; cancer; blood vessel detection
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The analysis of microvasculature and angiogenesis holds significant prognostic value in various diseases, including cancer. Traditional evaluation methods are time consuming and subject to variability among observers. Artificial intelligence, specifically computer vision solutions, can rapidly analyze blood vessel structures in whole slide images, leading to a new era in computational pathology.
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.
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