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 | DUCK-Net | mean Dice | 0.95 | — | Unverified |
| 2 | EffiSegNet-B5 | mean Dice | 0.95 | — | Unverified |
| 3 | EffiSegNet-B4 | mean Dice | 0.95 | — | Unverified |
| 4 | SegMed | mean Dice | 0.95 | — | Unverified |
| 5 | FCB Former | mean Dice | 0.94 | — | Unverified |
| 6 | FCB-SwinV2 Transformer | mean Dice | 0.94 | — | Unverified |
| 7 | SEP | mean Dice | 0.94 | — | Unverified |
| 8 | LM-Net | mean Dice | 0.94 | — | Unverified |
| 9 | RAPUNet | mean Dice | 0.94 | — | Unverified |
| 10 | FCBFormer | mean Dice | 0.94 | — | Unverified |