About
Medical image segmentation is the task of segmenting objects of interest in a medical image - for pancreas
( Image credit: IVD-Net )
Benchmarks
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Subtasks
Datasets
Greatest papers with code
U-Net: Convolutional Networks for Biomedical Image Segmentation
There is large consent that successful training of deep networks requires many thousand annotated training samples.
Ranked #1 on
Cell Segmentation
on DIC-HeLa
CELL SEGMENTATION COLORECTAL GLAND SEGMENTATION: ELECTRON MICROSCOPY IMAGE SEGMENTATION IMAGE AUGMENTATION LESION SEGMENTATION LUNG NODULE SEGMENTATION MULTI-TISSUE NUCLEUS SEGMENTATION PANCREAS SEGMENTATION RETINAL VESSEL SEGMENTATION SEMANTIC SEGMENTATION SKIN CANCER SEGMENTATION
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
• osmr/imgclsmob
• 
We show that SegNet provides good performance with competitive inference time and more efficient inference memory-wise as compared to other architectures.
Ranked #3 on
Medical Image Segmentation
on RITE
CROWD COUNTING LESION SEGMENTATION REAL-TIME SEMANTIC SEGMENTATION SCENE SEGMENTATION SCENE UNDERSTANDING
Segmentation Loss Odyssey
• JunMa11/SegLoss
• 
Loss functions are one of the crucial ingredients in deep learning-based medical image segmentation methods.
Automated Design of Deep Learning Methods for Biomedical Image Segmentation
• MIC-DKFZ/nnunet
•
Biomedical imaging is a driver of scientific discovery and core component of medical care, currently stimulated by the field of deep learning.
Attention U-Net: Learning Where to Look for the Pancreas
• LeeJunHyun/Image_Segmentation
•
We propose a novel attention gate (AG) model for medical imaging that automatically learns to focus on target structures of varying shapes and sizes.
Ranked #1 on
Pancreas Segmentation
on CT-150
Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation
• LeeJunHyun/Image_Segmentation
•
In this paper, we propose a Recurrent Convolutional Neural Network (RCNN) based on U-Net as well as a Recurrent Residual Convolutional Neural Network (RRCNN) based on U-Net models, which are named RU-Net and R2U-Net respectively.
IMAGE CLASSIFICATION LESION SEGMENTATION LUNG NODULE SEGMENTATION RETINAL VESSEL SEGMENTATION SEMANTIC SEGMENTATION SKIN CANCER SEGMENTATION
UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation
The state-of-the-art models for medical image segmentation are variants of U-Net and fully convolutional networks (FCN).
Ranked #1 on
Medical Image Segmentation
on EM
4 COMPUTED TOMOGRAPHY (CT) ELECTRON MICROSCOPY INSTANCE SEGMENTATION MEDICAL IMAGE SEGMENTATION SEMANTIC SEGMENTATION
UNet++: A Nested U-Net Architecture for Medical Image Segmentation
Implementation of different kinds of Unet Models for Image Segmentation - Unet , RCNN-Unet, Attention Unet, RCNN-Attention Unet, Nested Unet
Ranked #3 on
Medical Image Segmentation
on Kvasir-SEG
Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation
We propose a dual pathway, 11-layers deep, three-dimensional Convolutional Neural Network for the challenging task of brain lesion segmentation.
Ranked #1 on
Lesion Segmentation
on ISLES-2015
3D MEDICAL IMAGING SEGMENTATION BRAIN LESION SEGMENTATION FROM MRI BRAIN TUMOR SEGMENTATION LESION SEGMENTATION
Cutting-edge 3D Medical Image Segmentation Methods in 2020: Are Happy Families All Alike?
Segmentation is one of the most important and popular tasks in medical image analysis, which plays a critical role in disease diagnosis, surgical planning, and prognosis evaluation.