Pixel-accurate Segmentation of Surgical Tools based on Bounding Box Annotations

George Leifman, Amit Aides, Tomer Golany, Daniel Freedman, Ehud Rivlin

Abstract: Detection and segmentation of surgical instruments is an important problem for laparoscopic surgery. Accurate pixel-wise instrument segmentation is a useful intermediate task for the development of computer-assisted surgery systems, such as pose estimation, surgical phase estimation, enhanced image fusion, video retrieval and others. In this paper we describe a deep learning-based approach to instrument segmentation, which addresses the binary segmentation problem in which every pixel in an image is labeled as instrument or background. The key novelty of our approach relates to the use of training data which is inexpensive and fast to acquire. First, our approach relies on weak annotations provided as bounding boxes of the instruments, which are much faster and cheaper to obtain than a dense pixel-level annotations. Second, to further improve the system’s accuracy we propose a novel approach to generate synthetic training images. Our approach achieves state-of-the-art results, outperforming previously proposed methods for automatic instrument segmentation, based only on weak annotations.