Image-based diagnosis becoming one of the most important areas in medicine, as the diversity and sophistication of imaging techniques are being increasingly used in hospitals and medical centers. This, however, raises the issue of having image analysis capabilities that go with this trend, to be able to use medical imagery to provide fast and accurate diagnosis. In andrology in particular, the spermiogram analysis is considered the most significant study to evaluate the male reproductive capacity. Spermiograms can be produced with relatively little effort and cost, since they require only standard procedures for sample treatment. However, an adequate assessment of sperm quality requires the careful inspection by higly trained specialists, requiring time, and being prone to high inter- and intra-specialist variances. In this paper we present a system for automatic spermiogram analysis using image processing and machine learning techniques. The system was trained using a repository of spermiograms and the opinion of several experts in andrology and in human reproduction, using different information sources and classification criteria. The results are aimed to develop a SaaS CASA (Computer Assisted Sperm Analysis) system that can provide results over the Internet.

Date Published: 21 December 2018
PDF: 7 pages
Proc. SPIE 10975, 14th International Symposium on Medical Information Processing and Analysis, 109750G (21 December 2018); doi: 10.1117/12.2513676

Published in SPIE Proceedings Vol. 10975:
14th International Symposium on Medical Information Processing and Analysis
Eduardo Romero; Natasha Lepore; Jorge Brieva, Editor(s)