Authors | D. Jha, P. H. Smedsrud, M. Riegler, D. Johansen, T. de Lange, P. Halvorsen and H. D. Johansen |
Title | ResUNet++: An Advanced Architecture for Medical Image Segmentation |
Afilliation | Machine Learning |
Project(s) | Department of Holistic Systems |
Status | Published |
Publication Type | Proceedings, refereed |
Year of Publication | 2019 |
Conference Name | 2019 IEEE International Symposium on Multimedia (ISM) |
Publisher | IEEE |
Place Published | San Diego, California, USA |
Keywords | colonoscopy, 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 Key | 26812 |