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.
2015 MICCAI Polyp Detection
DoubleUNet
2018 Data Science Bowl
DoubleUNet
ACDC
FCT
AMOS
MedNeXt-L (5x5x5)
ASU-Mayo Clinic dataset
ResUNet++
Autoimmune Dataset
Unet with APP
Automatic Cardiac Diagnosis Challenge (ACDC)
FCT
Autooral dataset
HF-UNet
BKAI-IGH NeoPolyp-Small
QTSeg
Brain US
MedT
Cell
CHAOS MRI Dataset
MS-Dual-Guided
CHASE_DB1
CVC-ClinicDB
DUCK-Net
CVC-ColonDB
RAPUNet
CVC-VideoClinicDB
ResUNet++ + TTA
DRIVE
EM
UNet++
Endotect Polyp Segmentation Challenge Dataset
DDANet
ENSeg
YOLOv8-m + SAM-b
ETIS-LARIBPOLYPDB
RAPUNet
Extended Task10_Colon Medical Decathlon
nnUNet
GlaS
Hi-gMISnet
HSVM
MS-Dual-Guided
Hyper-Kvasir Dataset
efficientnetb1
ISBI 2012 EM Segmentation
DC-UNet
iSEG 2017 Challenge
HyperDenseNet
ISIC 2018
EMCAD
ISIC 2018
ProMISe
ISIC2018
EMCAD
Kvasir-Instrument
DoubleUNet
Kvasir-SEG
DUCK-Net
KvasirCapsule-SEG
NanoNet
LiTS2017
UNet 3+
Medical Segmentation Decathlon
Swin UNETR
Medico automatic polyp segmentation challenge (dataset)
MICCAI 2015 Head and Neck Challenge
AnatomyNet
MICCAI 2015 Multi-Atlas Abdomen Labeling Challenge
MERIT
MoNuSAC
MaxViT-UNet
MoNuSeg
MDM
MoNuSeg 2018
MosMedData
C2FVL
PROMISE12
Hi-gMISnet
RITE
KiU-Net
ROBUST-MIS
SegPC-2021
DCSAU-Net
Synapse
nnFormer
Synapse multi-organ CT
Interactive AI-SAM gt box