Abstract
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.
Highlights
Colorectal cancer (CRC) was the third most common and second most deadly cancer type worldwide in 2020 [1]
A recent study showed that pre-trained Convolutional Neural Networks (CNN) improved the performance in classifying colorectal polyps from colonoscopy images [6], but still it is not explored whether a pre-trained segmentation models will improve the performance of colorectal polyp segmentation
The polyp model was trained on the Kvasir-SEG open data set consisting of 1000 images, containing one or more polyps [8], whereas the instrument model was trained on Kvasir-Instrument, which is another open data set consisting of 590 images, containing different endoscopic tools [5]
Summary
Colorectal cancer (CRC) was the third most common and second most deadly cancer type worldwide in 2020 [1]. Artificial intelligence (AI) and image segmentation have shown to be useful in segmenting colorectal polyps [2, 3], and this may help the endoscopists to detect the polyps that otherwise are being overseen. A recent study showed that pre-trained Convolutional Neural Networks (CNN) improved the performance in classifying colorectal polyps from colonoscopy images [6], but still it is not explored whether a pre-trained segmentation models will improve the performance of colorectal polyp segmentation. In this study, which is a part of a machine learning challenge [7], we aim to assess pre-trained and not pre-trained CNNs to detect polyps and endoscopic tools from colonoscopic images
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