Auto-segmentation of organs-at-risk for radiotherapy using U-Net combined with improved algorithms(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2023年第3期
- Page:
- 303-312
- Research Field:
- 医学放射物理
- Publishing date:
Info
- Title:
- Auto-segmentation of organs-at-risk for radiotherapy using U-Net combined with improved algorithms
- Author(s):
- WU Chuanfeng1; JIN Xinyan2; BAI Siyue2; GE Yun2; ZHOU Jundong1; HU Rui1; CHEN Ying2; WANG Dongyan1
- 1. Department of Radiation Oncology, the Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou 215000, China 2. School of Electronic Science and Engineering, Nanjing University, Nanjing 210023, China
- Keywords:
- Keywords: deep learning auto-segmentation liver organs-at-risk U-Net
- PACS:
- R318；R811.1
- DOI:
- DOI:10.3969/j.issn.1005-202X.2023.03.008
- Abstract:
- Abstract: Objective To develop a model based on U-Net for the auto-segmentation of organs-at-risk, and to propose 3 improved models for automated liver segmentation. Methods The CT images and tissue structure data of 184 patients with liver cancer and 183 patients receiving head radiotherapy were collected and combined with the public dataset Sliver07 for the training and evaluation of the models. The established U-Net model and 3 models combined with dilated convolution, SLIC super-pixel algorithm and region growing algorithm, respectively, were trained for obtaining prediction models which were then used for the prediction of auto-segmentation results. The segmentation accuracy was evaluated using intersection over union (IoU) and mean intersection over union (MIoU). Results For the test set, the MIoU of the U-Net model for OAR segmentation in head radiotherapy ranged from 0.795 to 0.970 and for the liver segmentation was around 0.876. The improved model for automated liver segmentation improved the MIoU to 0.888 and restricted the occurrence of large prediction deviations, which reduced the proportion of IoU less than 0.8 in the test samples from 16.67% to 7.50%. Visually, the models combined with improved algorithms could capture more complex and confusing boundary areas than U-Net. Conclusion The established U-Net performed well in the auto-segmentations of liver and organs-at-risk for head radiotherapy, and the 3 improved models can obtain better results in liver segmentation.
Last Update: 2023-03-29