  Gastrointestinal (GI) pathologies are periodically screened, biopsied, and
resected using surgical tools. Usually the procedures and the treated or
resected areas are not specifically tracked or analysed during or after
colonoscopies. Information regarding disease borders, development and amount
and size of the resected area get lost. This can lead to poor follow-up and
bothersome reassessment difficulties post-treatment. To improve the current
standard and also to foster more research on the topic we have released the
``Kvasir-Instrument'' dataset which consists of $590$ annotated frames
containing GI procedure tools such as snares, balloons and biopsy forceps, etc.
Beside of the images, the dataset includes ground truth masks and bounding
boxes and has been verified by two expert GI endoscopists. Additionally, we
provide a baseline for the segmentation of the GI tools to promote research and
algorithm development. We obtained a dice coefficient score of 0.9158 and a
Jaccard index of 0.8578 using a classical U-Net architecture. A similar dice
coefficient score was observed for DoubleUNet. The qualitative results showed
that the model did not work for the images with specularity and the frames with
multiple instruments, while the best result for both methods was observed on
all other types of images. Both, qualitative and quantitative results show that
the model performs reasonably good, but there is a large potential for further
improvements. Benchmarking using the dataset provides an opportunity for
researchers to contribute to the field of automatic endoscopic diagnostic and
therapeutic tool segmentation for GI endoscopy.
