# Efficient Context-Aware Network for Abdominal Multi-organ Segmentation

arXiv: https://arxiv.org/abs/2109.10601 · HTML: https://mlsanity.com/paper/2109.10601/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 15 references that are in the map.
- First posted on arXiv 22 Sep 2021.

## Builds on (most cited first, 7 of 7)

- [The Liver Tumor Segmentation Benchmark (LiTS)](https://mlsanity.com/paper/1901.04056/) (2019)
- [A large annotated medical image dataset for the development and evaluation of segmentation algorithms](https://mlsanity.com/paper/1902.09063/) (2019)
- [AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem?](https://mlsanity.com/paper/2010.14808/) (2020)
- [The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 Challenge](https://mlsanity.com/paper/1912.01054/) (2019)
- [DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation](https://mlsanity.com/paper/1506.06448/) (2015)
- [Strip Pooling: Rethinking Spatial Pooling for Scene Parsing](https://mlsanity.com/paper/2003.13328/) (2020)
- [A 3D Coarse-to-Fine Framework for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1712.00201/) (2017)

## Built on by (most cited first, 7 of 7)

- [Large-Kernel Attention for 3D Medical Image Segmentation](https://mlsanity.com/paper/2207.11225/) (2022)
- [Implicit U-Net for volumetric medical image segmentation](https://mlsanity.com/paper/2206.15217/) (2022)
- [Combining Self-Training and Hybrid Architecture for Semi-supervised Abdominal Organ Segmentation](https://mlsanity.com/paper/2207.11512/) (2022)
- [Towards more precise automatic analysis: a comprehensive survey of deep learning-based multi-organ segmentation](https://mlsanity.com/paper/2303.00232/) (2023)
- [Boosting Convolution with Efficient MLP-Permutation for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2303.13111/) (2023)
- [Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotation](https://mlsanity.com/paper/2302.03296/) (2023)
- [Knowledge Distillation from Cross Teaching Teachers for Efficient Semi-Supervised Abdominal Organ Segmentation in CT](https://mlsanity.com/paper/2211.05942/) (2022)

## Abstract (arXiv)

The contextual information, presented in abdominal CT scan, is relative consistent. In order to make full use of the overall 3D context, we develop a whole-volume-based coarse-to-fine framework for efficient and effective abdominal multi-organ segmentation. We propose a new efficientSegNet network, which is composed of basic encoder, slim decoder and efficient context block. For the decoder module, anisotropic convolution with a k*k*1 intra-slice convolution and a 1*1*k inter-slice convolution, is designed to reduce the computation burden. For the context block, we propose strip pooling module to capture anisotropic and long-range contextual information, which exists in abdominal scene. Quantitative evaluation on the FLARE2021 validation cases, this method achieves the average dice similarity coefficient (DSC) of 0.895 and average normalized surface distance (NSD) of 0.775. This method won the 1st place on the 2021-MICCAI-FLARE challenge. Codes and models are available at https://github.com/Shanghai-Aitrox-Technology/EfficientSegmentation.