Automatic Polyp Segmentation using U-Net-ResNet50
This work aims to improve the detection rate of colorectal polyps for clinicians during colonoscopy, which is an incremental improvement on existing methods.
This paper addresses the challenge of automatic polyp segmentation in colonoscopy images to aid in early detection of colorectal cancer. The authors developed a U-Net model with a pre-trained ResNet50 encoder, achieving a Dice coefficient of 0.8154 and Jaccard of 0.7396 on the Medico automatic polyp segmentation challenge dataset.
Polyps are the predecessors to colorectal cancer which is considered as one of the leading causes of cancer-related deaths worldwide. Colonoscopy is the standard procedure for the identification, localization, and removal of colorectal polyps. Due to variability in shape, size, and surrounding tissue similarity, colorectal polyps are often missed by the clinicians during colonoscopy. With the use of an automatic, accurate, and fast polyp segmentation method during the colonoscopy, many colorectal polyps can be easily detected and removed. The ``Medico automatic polyp segmentation challenge'' provides an opportunity to study polyp segmentation and build an efficient and accurate segmentation algorithm. We use the U-Net with pre-trained ResNet50 as the encoder for the polyp segmentation. The model is trained on Kvasir-SEG dataset provided for the challenge and tested on the organizer's dataset and achieves a dice coefficient of 0.8154, Jaccard of 0.7396, recall of 0.8533, precision of 0.8532, accuracy of 0.9506, and F2 score of 0.8272, demonstrating the generalization ability of our model.