Auto-segmentation of the hippocampus in multimodal image using artificial intelligence(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2022年第3期
- Page:
- 390-396
- Research Field:
- 医学人工智能
- Publishing date:
Info
- Title:
- Auto-segmentation of the hippocampus in multimodal image using artificial intelligence
- Author(s):
- ZHANG Ruiping1; LIU Yinglong2; ZHANG Wenjing3; DAI Zhuojie4; CHEN Changshun1; LI Dongbo1; FU Chunpeng1; YANG Rui1; ZHANG Junjun1; ZHANG Wei5; JIA Lecheng2
- 1. Department of Radiotherapy, the First Hospital of Tsinghua University, Beijing 100016, China 2. Shenzhen United Imaging High-end Medical Equipment Innovation Research Institute, Shenzhen 518045, China 3. Division of Medical Services, the First Hospital of Tsinghua University, Beijing 100016, China 4. Beijing United Imaging Intelligent Technology Research Institute, Beijing 100094, China 5. Shanghai United Imaging Technology Co., Ltd, Shanghai 201807, China
- Keywords:
- Keywords: artificial intelligence deep learning multimodal image hippocampus automatic segmentation
- PACS:
- R318
- DOI:
- DOI:10.3969/j.issn.1005-202X.2022.03.021
- Abstract:
- Objective To develop a technique for auto-segmentation of the hippocampal using artificial intelligence based on deep learning in the multimodal image combining magnetic resonance imaging (MRI) with computed tomography (CT), thereby providing an efficient and accurate automatic segmentation method for hippocampus sparing in cranial radiotherapy. Methods The cranial CT and MRI images of 40 patients with brain metastases treated in the Department of Radiotherapy, the First Affiliated Hospital of Tsinghua University from January 2020 to December 2020 were collected. Three kinds of deep learning models, namely 3D U-Net, 3D U-Net Cascade and 3D BUC-Net, were trained on the datasets of CT images and CT-MRI registration images separately. The Dice similarity coefficient (DSC) and 95% Hausdorff distance (95HD) between the contours of left and right hippocampus segmented automatically by models and labelde by experts, as well as the hippocampus volume were used for evaluating the segmentation accuracy of models. The time taken for auto-segmentation on the same a patch of 3D image was used to assess the efficiency of models. Results The auto-segmentation accuracy of left and right hippocampus was improved significantly by importing MRI information to CT. Among 3 kinds of models, 3D BUC-Net model had the best segmentation performance for both left and right hippocampus on CT-MRI dataset (DSC: 0.900±0.017, 0.882±0.026 95HD: 0.792±0.084, 0.823±0.093), and its segmentation efficiency was the highest. Conclusion 3D BUC-Net model can achieve more efficient and accurate automatic segmentation of the hippocampus in multimodal image, which provides a lot of convenience for the hippocampus sparing during cranial radiotherapy.
Last Update: 2022-03-28