Measuring Testis Tubule Wall Thickness in Histopathology Images

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.