Automated assessment of endometrium from transvaginal ultrasound using Deep Learned Snake

Endometrium assessment via thickness measurement is commonly performed in routine gynecological ultrasound examination for assessing the reproductive health of patients undergoing fertility related treatments and endometrium cancer screening in women with post-menopausal bleeding. This paper introduces a fully automated technique for endometrium thickness measurement from three-dimensional transvaginal ultrasound (TVUS) images. The algorithm combines the robustness of deep neural networks with the more interpretable level set method for segmentation. We propose a hybrid variational curve propagation model which embeds a deep-learned endometrium probability map in the segmentation energy functional. This solution provides approximately 30% performance improvement over a contemporary supervised learning method on a database of 59 TVUS images and the thickness measurement is found to be within ±2mm of the manual measurement in 87% of the cases.