Medical Image Segmentation
Medical Image Segmentation is a computer vision task that involves dividing an medical image into multiple segments, where each segment represents a different object or structure of interest in the image. The goal of medical image segmentation is to provide a precise and accurate representation of the objects of interest within the image, typically for the purpose of diagnosis, treatment planning, and quantitative analysis.
( Image credit: IVD-Net )
Papers
Showing 1–10 of 2089 papers
All datasetsKvasir-SEGCVC-ClinicDBCVC-ColonDBETIS-LARIBPOLYPDBSynapse multi-organ CTAutomatic Cardiac Diagnosis Challenge (ACDC)MoNuSeg2018 Data Science BowlGlaSBKAI-IGH NeoPolyp-SmallMICCAI 2015 Multi-Atlas Abdomen Labeling ChallengeACDC
Benchmark Results
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | ReN-UNet | Dice | 92.79 | — | Unverified |
| 2 | EMCAD | Dice | 0.93 | — | Unverified |
| 3 | DuAT | Dice | 0.93 | — | Unverified |
| 4 | SSFormer-L | Dice | 0.92 | — | Unverified |
| 5 | Trans2Unet | Dice | 0.92 | — | Unverified |
| 6 | MSRF-Net | Dice | 0.92 | — | Unverified |
| 7 | FANet | Dice | 0.92 | — | Unverified |
| 8 | DoubleUNet | Dice | 0.91 | — | Unverified |
| 9 | Unet++ | Dice | 0.9 | — | Unverified |
| 10 | DCSAU-Net | mIoU | 0.85 | — | Unverified |