Papers › ResUNet++: An Advanced Architecture for Medical Image Segmentation

ResUNet++: An Advanced Architecture for Medical Image Segmentation

16 Nov 2019arXiv:1911.07067archive 2025-07-28

Debesh Jha, Pia H. Smedsrud, Michael A. Riegler, Dag Johansen, Thomas de Lange, Pal Halvorsen, Havard D. Johansen

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.

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DebeshJha/ResUNetplusplus officialmentioned on GitHubtf report
GewelsJI/PNS-Net mentioned on GitHubpytorchApache-2.0 report
rishikksh20/ResUnet mentioned on GitHubpytorch report

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Tasks

Colorectal Polyps CharacterizationImage SegmentationMedical Image SegmentationPolyp SegmentationSegmentationSemantic Segmentation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Medical Image Segmentation ASU-Mayo Clinic dataset ResUNet++ DSC 0.8743 #1 of 1 Archive leaderboard report
Medical Image Segmentation ASU-Mayo Clinic dataset ResUNet++ Precision 0.4896 #1 of 1 Archive leaderboard report
Medical Image Segmentation ASU-Mayo Clinic dataset ResUNet++ Recall 0.6534 #1 of 1 Archive leaderboard report
Medical Image Segmentation ASU-Mayo Clinic dataset ResUNet++ mIoU 0.8569 #1 of 1 Archive leaderboard report
Medical Image Segmentation CVC-ClinicDB ResUNet++ mean Dice 0.7955 #46 of 48 Archive leaderboard report
Medical Image Segmentation CVC-VideoClinicDB ResUNet++ Dice 0.8798 #3 of 5 Archive leaderboard report
Medical Image Segmentation CVC-VideoClinicDB ResUNet++ Recall 0.7749 #3 of 5 Archive leaderboard report
Medical Image Segmentation CVC-VideoClinicDB ResUNet++ mIoU 0.8730 #3 of 5 Archive leaderboard report
Medical Image Segmentation CVC-VideoClinicDB ResUNet++ precision 0.6702 #3 of 5 Archive leaderboard report
Medical Image Segmentation ETIS-LARIBPOLYPDB ResUNet++ mIoU 0.7534 #23 of 25 Archive leaderboard report
Medical Image Segmentation ETIS-LARIBPOLYPDB ResUNet++ mean Dice 0.6364 #23 of 25 Archive leaderboard report
Medical Image Segmentation Kvasir-SEG ResUNet++ mean Dice 0.8133 #56 of 58 Archive leaderboard report
Medical Image Segmentation KvasirCapsule-SEG ResUNet+ DSC 0.9499 #2 of 2 Archive leaderboard report
Medical Image Segmentation KvasirCapsule-SEG ResUNet+ mIoU 0.9087 #2 of 2 Archive leaderboard report
Polyp Segmentation Kvasir-SEG ResUNet++ mDice 0.8133 #6 of 8 Archive leaderboard report
Polyp Segmentation Kvasir-SEG ResUNet++ mIoU 0.7927 #6 of 8 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Concatenated Skip ConnectionConvolutionMax PoolingReLUU-Net

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