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 | RAPUNet | mean Dice | 0.95 | — | Unverified |
| 2 | DUCK-Net | mean Dice | 0.94 | — | Unverified |
| 3 | EMCAD | mean Dice | 0.92 | — | Unverified |
| 4 | SegMed | mean Dice | 0.92 | — | Unverified |
| 5 | UniNet | mean Dice | 0.92 | — | Unverified |
| 6 | ProMISe | mean Dice | 0.87 | — | Unverified |
| 7 | Meta-Polyp | mean Dice | 0.87 | — | Unverified |
| 8 | ResUNet++ + TTA | mean Dice | 0.85 | — | Unverified |
| 9 | PVT-GCASCADE | mean Dice | 0.83 | — | Unverified |
| 10 | PVT-CASCADE | mean Dice | 0.83 | — | Unverified |