Organ Segmentation
116 papers with code • 1 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Organ Segmentation
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Libraries
Use these libraries to find Organ Segmentation models and implementations
BTCV
Extended Task10_Colon Medical Decathlon
Most implemented papers
TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
Medical image segmentation is an essential prerequisite for developing healthcare systems, especially for disease diagnosis and treatment planning.
UNETR: Transformers for 3D Medical Image Segmentation
Project-MONAI/research-contributions
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Inspired by the recent success of transformers for Natural Language Processing (NLP) in long-range sequence learning, we reformulate the task of volumetric (3D) medical image segmentation as a sequence-to-sequence prediction problem.
Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks
abhi4ssj/squeeze_and_excitation
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Fully convolutional neural networks (F-CNNs) have set the state-of-the-art in image segmentation for a plethora of applications.
Self-Supervision with Superpixels: Training Few-shot Medical Image Segmentation without Annotation
cheng-01037/Self-supervised-Fewshot-Medical-Image-Segmentation
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ECCV 2020
Few-shot semantic segmentation (FSS) has great potential for medical imaging applications.
WORD: A large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from CT image
Deep learning-based medical image segmentation has shown the potential to reduce manual delineation efforts, but it still requires a large-scale fine annotated dataset for training, and there is a lack of large-scale datasets covering the whole abdomen region with accurate and detailed annotations for the whole abdominal organ segmentation.
AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation
Constraint by the high cost of collecting and labeling 3D medical data, most of the deep learning models to date are driven by datasets with a limited number of organs of interest or samples, which still limits the power of modern deep models and makes it difficult to provide a fully comprehensive and fair estimate of various methods.
A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Scans
twni2016/OrganSegRSTN_PyTorch
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Deep neural networks have been widely adopted for automatic organ segmentation from abdominal CT scans.
Autofocus Layer for Semantic Segmentation
We propose the autofocus convolutional layer for semantic segmentation with the objective of enhancing the capabilities of neural networks for multi-scale processing.
3D TransUNet: Advancing Medical Image Segmentation through Vision Transformers
In this paper, we extend the 2D TransUNet architecture to a 3D network by building upon the state-of-the-art nnU-Net architecture, and fully exploring Transformers' potential in both the encoder and decoder design.
Recurrent Saliency Transformation Network: Incorporating Multi-Stage Visual Cues for Small Organ Segmentation
twni2016/OrganSegRSTN_PyTorch
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CVPR 2018
The key innovation is a saliency transformation module, which repeatedly converts the segmentation probability map from the previous iteration as spatial weights and applies these weights to the current iteration.
