Automatic Polyp Segmentation using Fully Convolutional Neural Network

TL;DR

This paper presents a real-time fully convolutional neural network for automatic polyp segmentation in colonoscopy images, significantly reducing missed lesions and aiding colorectal cancer prevention.

Contribution

It introduces a fast, generalizable segmentation model trained on the Kvasir-SEG dataset and validated on unseen data, achieving high accuracy and real-time performance.

Findings

Dice coefficient of 0.7801 on unseen data

Achieves 80.60 FPS at 512x512 resolution

Demonstrates strong generalization ability

Abstract

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 $512 \times 512$.