Automatic Polyp Segmentation using Fully Convolutional Neural Network
This work aims to improve the accuracy and real-time capability of polyp detection for clinicians during colonoscopy, which is an incremental improvement to existing medical imaging techniques.
This paper addresses the problem of automatic polyp segmentation to reduce the miss-rate of colorectal polyps during colonoscopy. The authors developed a model that achieved a Dice coefficient of 0.7801, mIoU of 0.6847, and a speed of 80.60 FPS on an unseen dataset.
Colorectal cancer is one of fatal cancer worldwide. Colonoscopy is the standard treatment for examination, localization, and removal of colorectal polyps. However, it has been shown that the miss-rate of colorectal polyps during colonoscopy is between 6 to 27%. The use of an automated, accurate, and real-time polyp segmentation during colonoscopy examinations can help the clinicians to eliminate missing lesions and prevent further progression of colorectal cancer. The ``Medico automatic polyp segmentation challenge'' provides an opportunity to study polyp segmentation and build a fast segmentation model. The challenge organizers provide a Kvasir-SEG dataset to train the model. Then it is tested on a separate unseen dataset to validate the efficiency and speed of the segmentation model. The experiments demonstrate that the model trained on the Kvasir-SEG dataset and tested on an unseen dataset achieves a dice coefficient of 0.7801, mIoU of 0.6847, recall of 0.8077, and precision of 0.8126, demonstrating the generalization ability of our model. The model has achieved 80.60 FPS on the unseen dataset with an image resolution of $512 \times 512$.