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Medical Image Segmentation
Medical image segmentation is a task in the field of computer vision aimed at dividing medical images into multiple regions, each representing different objects of interest or structures within the image. The goal is to provide precise and accurate representations of these objects, primarily for diagnosis, treatment planning, and quantitative analysis.
Kvasir-SEG
SSFormer-L
CVC-ClinicDB
DUCK-Net
ETIS-LARIBPOLYPDB
DUCK-Net
CVC-ColonDB
RAPUNet
Synapse multi-organ CT
Interactive AI-SAM gt box
Automatic Cardiac Diagnosis Challenge (ACDC)
FCT
MoNuSeg
Stardist
GlaS
Hi-gMISnet
BKAI-IGH NeoPolyp-Small
QTSeg
2018 Data Science Bowl
DoubleUNet
MICCAI 2015 Multi-Atlas Abdomen Labeling Challenge
MERIT
ACDC
FCT
ISIC 2018
ProMISe
DRIVE
Medical Segmentation Decathlon
Swin UNETR
CVC-VideoClinicDB
ResUNet++ + TTA
Kvasir-Instrument
DoubleUNet
EM
UNet++
Brain US
MedT
ISBI 2012 EM Segmentation
CE-Net
CHASE_DB1
RITE
KiU-Net
ISIC2018
EMCAD
LiTS2017
UNet 3+
ROBUST-MIS
KvasirCapsule-SEG
NanoNet
Medico automatic polyp segmentation challenge (dataset)
ISIC 2018
EMCAD
Endotect Polyp Segmentation Challenge Dataset
DDANet
ENSeg
YOLOv8-m + SAM-b
CHAOS MRI Dataset
MS-Dual-Guided
ASU-Mayo Clinic dataset
ResUNet++
MoNuSeg 2018
MosMedData
C2FVL
SegPC-2021
DCSAU-Net
Hyper-Kvasir Dataset
efficientnetb1
Autoimmune Dataset
Unet with APP
MoNuSAC
MaxViT-UNet
Extended Task10_Colon Medical Decathlon
nnUNet
Autooral dataset
HF-UNet
2015 MICCAI Polyp Detection
DoubleUNet
iSEG 2017 Challenge
HyperDenseNet
PROMISE12
Hi-gMISnet
Synapse
nnFormer
MICCAI 2015 Head and Neck Challenge
AnatomyNet
Cell
HSVM
MS-Dual-Guided
AMOS
MedNeXt-L (5x5x5)