Pixelwise segmentation of uterine wall in endoscopic video frame using convolutional neural networks

Abstract

Though the number of in vitro fertilization (IVF) has been rising continuously from the beginning of the new millennium, however the success rate of the implantations remained low. According to the statistics, the main reason of unsuccessful IVF relates to the woman factors. The aim of our research project is to provide an automatic image processing based decision support system for the gynecologists which tries to help medical experts to determine the most appropriate time for the insemination. In this paper, we present the first component of this tool, which deals with the preprocessing of the videos about the uterus for further examinations. It includes the segmentation of the video frames by fully convolutional neural network (FCNN) to determines the region of interest. The chosen model has been trained on 4000 images acquired during real hysteroscopic surgeries and tested on other 716 ones. We have achieved 92% segmentation accuracy regarding the correct recognition of the fundus.