Transfer Learning in Polyp and Endoscopic Tool Segmentation from Colonoscopy Images

Colorectal cancer is one of the deadliest and most widespread types of cancer in the world. Colonoscopy is the procedure used to detect and diagnose polyps from the colon, but today's detection rate shows a significant error rate that affects diagnosis and treatment. An automatic image segmentation algorithm may help doctors to improve the detection rate of pathological polyps in the colon. Furthermore, segmenting endoscopic tools in images taken during colonoscopy may contribute towards robotic assisted surgery. In this study, we trained and validated both pre-trained and not pre-trained segmentation models on two different data sets, containing images of polyps and endoscopic tools. Finally, we applied the models on two separate test sets and the best polyp model got a dice score 0.857 and the test instrument model got a dice score 0.948. Moreover, we found that pre-training of the models increased the performance in segmenting polyps and endoscopic tools.

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Results from the Paper

 Ranked #1 on Medical Image Segmentation on Hyper-Kvasir Dataset (using extra training data)

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Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Medical Image Segmentation Hyper-Kvasir Dataset efficientnetb1 Dice score 0.857 # 1
Intersection over Union 0.800 # 1
Medical Image Segmentation Kvasir-Instrument efficientnetb1 DSC 0.948 # 1
Dice Score 0.948 # 1
Intersection over Union 0.911 # 1