Segmentation of pancreas in CT images based on 2.5D cascaded convolutional neural network(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2020年第6期
- Page:
- 786-791
- Research Field:
- 医学人工智能
- Publishing date:
Info
- Title:
- Segmentation of pancreas in CT images based on 2.5D cascaded convolutional neural network
- Author(s):
- ZHOU Fangfang; WANG Yida; SONG Yang; YANG Guang
- Shanghai Key Laboratory of MRI /School of Physics and Electronic Science, East China Normal University, Shanghai 200062, China
- Keywords:
- Keywords: convolutional neural network pancreas segmentation deep supervision
- PACS:
- R318；TP391.4
- DOI:
- DOI:10.3969/j.issn.1005-202X.2020.06.024
- Abstract:
- Abstract: Objective Due to the small size of the pancreas and inter-individual structural variances, it is difficult to segment the pancreas in images accurately. Therefore, a 2.5D cascaded convolutional neural network is proposed for the segmentation of pancreas in CT images. Methods The data used in the experiment were the open dataset for pancreas segmentation published by NIH. The 82 abdominal CT images contained in the dataset were randomly divided into training, validation, and test sets (training/validation/test=56/9/17). During the training, rotating, stretching, shifting and shearing were used for data augmentation. The combination of probability graph and 2.5D cascaded deep supervision UNet, namely CSNet (Cascading deep Supervision UNet), was proposed for pancreas segmentation. The proposed network consisted of 3 sub-networks. Based on UNet, 5 consecutive slices of image were input into the first sub-network to obtain the coarse segmentation images of the middle 3 slices. After a suitable threshold was set, the segmentation images were transformed into binary segmentation images. The original image at the center slice was combined with the coarse segmentation images of two adjacent slices (the first and third slices) and then input into the second sub-network, another deep supervision UNet, to obtain a fine segmentation of the center slice. In the third sub-network, the coarse segmentation probability images of the middle slices from the first sub-network were combined with the fine segmentation probability images from the second sub-network by 1×1 convolution to obtain the final segmentation. The 3 sub-networks were trained simultaneously. During the training, the energy functions of 3 sub-networks were optimized jointly, thereby obtaining a more accurate segmentation result. Finally, the results of pancreas segmentation were evaluated by DSC. Results CSNet achieve a DSC of (83.74±5.27)% on the independent test set. Conclusion CSNet can be used to accurately segment the pancreas in CT images.
Last Update: 2020-07-03