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.
@article{arxiv.2109.10601,
title = {Efficient Context-Aware Network for Abdominal Multi-organ Segmentation},
author = {Fan Zhang and Yu Wang and Hua Yang},
journal= {arXiv preprint arXiv:2109.10601},
year = {2021}
}