Segmenting Medical Instruments in Minimally Invasive Surgeries using AttentionMask
This work addresses medical instrument segmentation for minimally invasive surgeries, which is incremental as it adapts an existing method to a specific domain.
The authors tackled the problem of segmenting medical instruments in minimally invasive surgery images by adapting the AttentionMask object proposal system and adding dedicated post-processing. Their method achieved state-of-the-art performance on the ROBUST-MIS Challenge 2019, showing robustness to image degradations, generalization to unseen surgeries, and effectiveness with small instruments.
Precisely locating and segmenting medical instruments in images of minimally invasive surgeries, medical instrument segmentation, is an essential first step for several tasks in medical image processing. However, image degradations, small instruments, and the generalization between different surgery types make medical instrument segmentation challenging. To cope with these challenges, we adapt the object proposal generation system AttentionMask and propose a dedicated post-processing to select promising proposals. The results on the ROBUST-MIS Challenge 2019 show that our adapted AttentionMask system is a strong foundation for generating state-of-the-art performance. Our evaluation in an object proposal generation framework shows that our adapted AttentionMask system is robust to image degradations, generalizes well to unseen types of surgeries, and copes well with small instruments.