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10 March 2020 Spatial information-embedded fully convolutional networks for multi-organ segmentation with improved data augmentation and instance normalization
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Abstract
The purpose of this paper is to present multi-organ segmentation method using spatial information-embedded fully convolutional networks (FCNs). Semantic segmentation of major anatomical structure from CT volumes is promising to apply in clinical work ows. A multitude of deep-learning-based approaches have been proposed for 3D image processing. With the rapid development of FCNs, the encoder-decoder network architecture is proved to achieved acceptable performance on segmentation tasks. However, it is hard to obtain the spatial information from sub-volumes during training. In this paper, we extend the spatial position information-embeded FCNs which designed for binary segmentation tor multi-class organ segmentation. We introduced gamma correction in data augmentation to improve the FCNs robustness. We compared the FCNs performance with different normalization methods, including batch normalization and instance normalization. Experiment results showed that our modifications positively influence the segmentation performance on abdominal CT dataset. Our highest average dice score achieves 87.2%, while the previous method achieved 86.2%.
Conference Presentation
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Chen Shen, Chenglong Wang, Holger R. Roth, Masahiro Oda, Yuichiro Hayashi, Kazunari Misawa, and Kensaku Mori "Spatial information-embedded fully convolutional networks for multi-organ segmentation with improved data augmentation and instance normalization", Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 1131316 (10 March 2020); https://doi.org/10.1117/12.2550496
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