Amodal Segmentation for Laparoscopic Surgery Video Instruments
This addresses the limitation of conventional segmentation techniques in surgery, which fail to handle occlusions, potentially improving surgical guidance and error analysis, though it is incremental as it applies an existing technique to a new domain.
The paper tackles the problem of segmenting surgical instruments in laparoscopic videos by predicting both visible and occluded parts, introducing a new dataset and benchmarking existing methods for this task.
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.