AuthorsD. Jha, P. H. Smedsrud, M. Riegler, D. Johansen, T. de Lange, P. Halvorsen, and H. D. Johansen
TitleResUNet++: An Advanced Architecture for Medical Image Segmentation
AfilliationMachine Learning
Project(s)Department of Holistic Systems
StatusPublished
Publication TypeProceedings, refereed
Year of Publication2019
Conference Name2019 IEEE International Symposium on Multimedia (ISM)
PublisherIEEE
Place PublishedSan Diego, California, USA
Keywordscolonoscopy, deep learning, health informatics, Medical image segmentation, Polyp segmentation, Semantic segmentation
Abstract

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.

Citation Key26812