Amodal Segmentation for Laparoscopic Surgery Video Instruments

TL;DR

This paper introduces amodal segmentation for surgical instruments, enabling the prediction of both visible and occluded parts, which can improve surgical guidance and training.

Contribution

It presents a new amodal segmentation approach for surgical instruments and introduces the AIS dataset with complete instrument masks.

Findings

Established a benchmark for amodal segmentation methods on the AIS dataset.

Demonstrated the effectiveness of amodal segmentation in capturing occluded instrument parts.

Provided a new dataset for future research in surgical instrument segmentation.

Abstract

Segmentation of surgical instruments is crucial for enhancing surgeon performance and ensuring patient safety. Conventional techniques such as binary, semantic, and instance segmentation share a common drawback: they do not accommodate the parts of instruments obscured by tissues or other instruments. Precisely predicting the full extent of these occluded instruments can significantly improve laparoscopic surgeries by providing critical guidance during operations and assisting in the analysis of potential surgical errors, as well as serving educational purposes. In this paper, we introduce Amodal Segmentation to the realm of surgical instruments in the medical field. This technique identifies both the visible and occluded parts of an object. To achieve this, we introduce a new Amoal Instruments Segmentation (AIS) dataset, which was developed by reannotating each instrument with its complete mask, utilizing the 2017 MICCAI EndoVis Robotic Instrument Segmentation Challenge dataset. Additionally, we evaluate several leading amodal segmentation methods to establish a benchmark for this new dataset.