Assessing YOLACT++ for real time and robust instance segmentation of medical instruments in endoscopic procedures
This addresses the need for clinically applicable, real-time instrument tracking in surgeries, though it is incremental as it builds on an existing architecture.
The paper tackled real-time instance segmentation of medical instruments in endoscopic procedures by adding attention mechanisms to YOLACT, achieving competitive robustness scores (0.313 MI_DSC and 0.338 MI_NSD) and real-time performance at 37 fps.
Image-based tracking of laparoscopic instruments plays a fundamental role in computer and robotic-assisted surgeries by aiding surgeons and increasing patient safety. Computer vision contests, such as the Robust Medical Instrument Segmentation (ROBUST-MIS) Challenge, seek to encourage the development of robust models for such purposes, providing large, diverse, and annotated datasets. To date, most of the existing models for instance segmentation of medical instruments were based on two-stage detectors, which provide robust results but are nowhere near to the real-time (5 frames-per-second (fps)at most). However, in order for the method to be clinically applicable, real-time capability is utmost required along with high accuracy. In this paper, we propose the addition of attention mechanisms to the YOLACT architecture that allows real-time instance segmentation of instrument with improved accuracy on the ROBUST-MIS dataset. Our proposed approach achieves competitive performance compared to the winner ofthe 2019 ROBUST-MIS challenge in terms of robustness scores,obtaining 0.313 MI_DSC and 0.338 MI_NSD, while achieving real-time performance (37 fps)