AuthorsD. Jha, S. Ali, K. Emanuelsen, S. A. Hicks, V. Thambawita, E. Garcia-Ceja, M. Riegler, T. de Lange, P. T. Schmidt, H. D. Johansen et al.
TitleKvasir-instrument: Diagnostic and therapeutic tool segmentation dataset in gastrointestinal endoscopy
AfilliationMachine Learning
Project(s)Department of Holistic Systems
Publication TypeProceedings, refereed
Year of Publication2020
Conference NameInternational Conference on Multimedia Modeling
Publisher Springer
KeywordsBenchmarking, Convolutional neural network, Gastrointestinal endoscopy, Tool segmentation

Gastrointestinal 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.

Citation Key39534

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