Abstract
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, 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. Besides 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 tools, 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 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.
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, 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. Besides 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 tools, 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 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.