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Precisely locating and segmenting medical instruments in images of minimallyinvasive surgeries, medical instrument segmentation, is an essential first stepfor several tasks in medical image processing. However, image degradations,small instruments, and the generalization between different surgery types makemedical instrument segmentation challenging. To cope with these challenges, weadapt the object proposal generation system AttentionMask and propose adedicated post-processing to select promising proposals. The results on theROBUST-MIS Challenge 2019 show that our adapted AttentionMask system is astrong foundation for generating state-of-the-art performance. Our evaluationin an object proposal generation framework shows that our adapted AttentionMasksystem is robust to image degradations, generalizes well to unseen types ofsurgeries, and copes well with small instruments.

Segmenting Medical Instruments in Minimally Invasive Surgeries using AttentionMask