Organ Segmentation

89 papers with code • 1 benchmarks • 0 datasets

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Most implemented papers

TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

Beckschen/TransUNet 8 Feb 2021

Medical image segmentation is an essential prerequisite for developing healthcare systems, especially for disease diagnosis and treatment planning.

Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks

abhi4ssj/squeeze_and_excitation 7 Mar 2018

Fully convolutional neural networks (F-CNNs) have set the state-of-the-art in image segmentation for a plethora of applications.

UNETR: Transformers for 3D Medical Image Segmentation

Project-MONAI/research-contributions 18 Mar 2021

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.

A Fixed-Point Model for Pancreas Segmentation in Abdominal CT Scans

twni2016/OrganSegRSTN_PyTorch 25 Dec 2016

Deep neural networks have been widely adopted for automatic organ segmentation from abdominal CT scans.

Autofocus Layer for Semantic Segmentation

yaq007/Autofocus-Layer 22 May 2018

We propose the autofocus convolutional layer for semantic segmentation with the objective of enhancing the capabilities of neural networks for multi-scale processing.

WORD: A large scale dataset, benchmark and clinical applicable study for abdominal organ segmentation from CT image

hilab-git/word 3 Nov 2021

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.

Recurrent Saliency Transformation Network: Incorporating Multi-Stage Visual Cues for Small Organ Segmentation

twni2016/OrganSegRSTN_PyTorch 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.

Elastic Boundary Projection for 3D Medical Image Segmentation

twni2016/Elastic-Boundary-Projection CVPR 2019

The key observation is that, although the object is a 3D volume, what we really need in segmentation is to find its boundary which is a 2D surface.

'Squeeze & Excite' Guided Few-Shot Segmentation of Volumetric Images

abhi4ssj/few-shot-segmentation 4 Feb 2019

This representation is passed on to the segmenter arm that uses this information to segment the new query image.