Accurate computer-aided polyp detection and segmentation during colonoscopy examinations can help endoscopists resect abnormal tissue and thereby decrease chances of polyps growing into cancer. Towards developing a fully automated model for pixel-wise polyp segmentation, we propose ResUNet++, which is an improved ResUNet architecture for colonoscopic image segmentation. Our experimental evaluations show that the suggested architecture produces good segmentation results on publicly available datasets. Furthermore, ResUNet++ significantly outperforms U-Net and ResUNet, two key state-of-the-art deep learning architectures, by achieving high evaluation scores with a dice coefficient of 81.33%, and a mean Intersection over Union (mIoU) of 79.27% for the Kvasir-SEG dataset and a dice coefficient of 79.55%, and a mIoU of 79.62% with CVC-612 dataset.
@article{arxiv.1911.07067,
title = {ResUNet++: An Advanced Architecture for Medical Image Segmentation},
author = {Debesh Jha and Pia H. Smedsrud and Michael A. Riegler and Dag Johansen and Thomas de Lange and Pal Halvorsen and Havard D. Johansen},
journal= {arXiv preprint arXiv:1911.07067},
year = {2019}
}
Comments
7 pages, 3 figures, 21st IEEE International Symposium on Multimedia