  Intraoperative tracking of laparoscopic instruments is often a prerequisite
for computer and robotic-assisted interventions. While numerous methods for
detecting, segmenting and tracking of medical instruments based on endoscopic
video images have been proposed in the literature, key limitations remain to be
addressed: Firstly, robustness, that is, the reliable performance of
state-of-the-art methods when run on challenging images (e.g. in the presence
of blood, smoke or motion artifacts). Secondly, generalization; algorithms
trained for a specific intervention in a specific hospital should generalize to
other interventions or institutions.
  In an effort to promote solutions for these limitations, we organized the
Robust Medical Instrument Segmentation (ROBUST-MIS) challenge as an
international benchmarking competition with a specific focus on the robustness
and generalization capabilities of algorithms. For the first time in the field
of endoscopic image processing, our challenge included a task on binary
segmentation and also addressed multi-instance detection and segmentation. The
challenge was based on a surgical data set comprising 10,040 annotated images
acquired from a total of 30 surgical procedures from three different types of
surgery. The validation of the competing methods for the three tasks (binary
segmentation, multi-instance detection and multi-instance segmentation) was
performed in three different stages with an increasing domain gap between the
training and the test data. The results confirm the initial hypothesis, namely
that algorithm performance degrades with an increasing domain gap. While the
average detection and segmentation quality of the best-performing algorithms is
high, future research should concentrate on detection and segmentation of
small, crossing, moving and transparent instrument(s) (parts).
