Deep-learning-based fast and fully automated segmentation on abdominal multiple organs from CT

Jieun Kim; June-Goo Lee

doi:10.1117/12.2521689

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27 March 2019 Deep-learning-based fast and fully automated segmentation on abdominal multiple organs from CT

Jieun Kim, June-Goo Lee

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Proceedings Volume 11050, International Forum on Medical Imaging in Asia 2019; 110500K (2019) https://doi.org/10.1117/12.2521689
Event: 2019 Joint International Workshop on Advanced Image Technology (IWAIT) and International Forum on Medical Imaging in Asia (IFMIA), 2019, Singapore, Singapore

Abstract

Effective segmentation of abdominal organs on CT images is necessary not only in the quantitative analysis but also in the dose simulation of radiational oncology. However, the manual or semi-automatic segmentation is tedious and subject to inter- and intra-observer variances. To overcome these shortcomings, the development of a fully automatic segmentation is required. In this paper, we propose the deep learning based fully-automated method to segment multiple organs from abdominal CT images and evaluate its performance on clinical dataset. Total 120 cases were used for training and testing. The DSC values in 20 test dataset were 0.945±0.016, 0.836±0.084, 0.912±0.052 and 0.886±0.068 for the liver, stomach, right and left kidney, respectively.

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Jieun Kim and June-Goo Lee "Deep-learning-based fast and fully automated segmentation on abdominal multiple organs from CT", Proc. SPIE 11050, International Forum on Medical Imaging in Asia 2019, 110500K (27 March 2019); https://doi.org/10.1117/12.2521689

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