Measuring Testis Tubule Wall Thickness in Histopathology Images
Date
2023
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Tartu Ülikool
Abstract
One of many causes of infertility is too thick tubule walls in male testis, locking in the sperm
cells. In this thesis we have developed a machine-learning-powered software pipeline for
analysing testis histopathology images. The software identifies the tubules and measures their
wall thicknesses, allowing medical professionals to draw conclusions and/or perform additional
follow-up analysis as needed. Our value proposition is in a clear focus on practical application.
The software is designed and trained for usage on large-format (50 000 megapixels) testis tissue
samples, measuring specific abnormalities. It is the author’s desire that the software pipeline
could be used by medical facilities in Estonia on real patients, providing real value, actually
helping people and making a difference.
Description
Keywords
Deep learning, Medical image segmentation, Computer Vision, Image processing