Live image parsing in uterine laparoscopy

Abstract

Augmented Reality (AR) can improve the information delivery to surgeons. In laparosurgery, the primary goal of AR is to provide multimodal information overlaid in live laparoscopic videos. For gynecologic laparoscopy, the 3D reconstruction of uterus and its deformable registration to preoperative data form the major problems in AR. Shape-from-Shading (SfS) and inter-frame registration require an accurate identification of the uterus region, the occlusions due to surgical tools, specularities, and other tissues. We propose a cascaded patient-specific real-time segmentation method to identify these four important regions. We use a color based Gaussian Mixture Model (GMM) to segment the tools and a more elaborate color and texture model to segment the uterus. The specularities are obtained by a saturation test. We show that our segmentation improves SfS and inter-frame registration of the uterus.