  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.
