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Intraoperative Glioma Grading Using Neural Architecture Search and Multi-Modal Imaging

文献类型：期刊论文

作者Xiao, Anqi1,2,3; Shen, Biluo1,2,3; Shi, Xiaojing1,2,3; Zhang, Zhe4,5; Zhang, Zeyu2,3,6; Tian, Jie1,2,3,6,7; Ji, Nan4,5,6; Hu, Zhenhua1,2,3
刊名IEEE TRANSACTIONS ON MEDICAL IMAGING
出版日期2022-10-01
卷号41期号:10页码:2570-2581
ISSN号0278-0062
关键词Imaging Computer architecture Fluorescence Feature extraction Surgery Biomedical imaging Medical diagnostic imaging Deep learning glioma grading intraoperative imaging multi-modal imaging neural architecture search NIR-II fluorescence imaging
DOI10.1109/TMI.2022.3166129
通讯作者Tian, Jie(tian@ieee.org) ; Ji, Nan(jinan@bjtth.org) ; Hu, Zhenhua(zhenhua.hu@ia.ac.cn)
英文摘要Glioma grading during surgery can help clinical treatment planning and prognosis, but intraoperative pathological examination of frozen sections is limited by the long processing time and complex procedures. Near-infrared fluorescence imaging provides chances for fast and accurate real-time diagnosis. Recently, deep learning techniques have been actively explored for medical image analysis and disease diagnosis. However, issues of near-infrared fluorescence images, including small-scale, noise, and low-resolution, increase the difficulty of training a satisfying network. Multi-modal imaging can provide complementary information to boost model performance, but simultaneously designing a proper network and utilizing the information of multi-modal data is challenging. In this work, we propose a novel neural architecture search method DLS-DARTS to automatically search for network architectures to handle these issues. DLS-DARTS has two learnable stems for multi-modal low-level feature fusion and uses a modified perturbation-based derivation strategy to improve the performance on the area under the curve and accuracy. White light imaging and fluorescence imaging in the first near-infrared window (650-900 nm) and the second near-infrared window (1,000-1,700 nm) are applied to provide multi-modal information on glioma tissues. In the experiments on 1,115 surgical glioma specimens, DLS-DARTS achieved an area under the curve of 0.843 and an accuracy of 0.634, which outperformed manually designed convolutional neural networks including ResNet, PyramidNet, and EfficientNet, and a state-of-the-art neural architecture search method for multi-modal medical image classification. Our study demonstrates that DLS-DARTS has the potential to help neurosurgeons during surgery, showing high prospects in medical image analysis.
WOS关键词CENTRAL-NERVOUS-SYSTEM ; CLASSIFICATION ; TUMORS
资助项目National Key Research and Development Program of China[2017YFA0205200] ; National Natural Science Foundation of China (NSFC)[62027901] ; National Natural Science Foundation of China (NSFC)[81930053] ; National Natural Science Foundation of China (NSFC)[92059207] ; National Natural Science Foundation of China (NSFC)[81227901] ; Beijing Natural Science Foundation[JQ19027] ; CAS Youth Interdisciplinary Team[JCTD-2021-08] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA16021200] ; Zhuhai High-Level Health Personnel Team Project[Zhuhai HLHPTP201703] ; Innovative Research Team of High-Level Local Universities in Shanghai
WOS研究方向Computer Science ; Engineering ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:000862400100003
资助机构National Key Research and Development Program of China ; National Natural Science Foundation of China (NSFC) ; Beijing Natural Science Foundation ; CAS Youth Interdisciplinary Team ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Zhuhai High-Level Health Personnel Team Project ; Innovative Research Team of High-Level Local Universities in Shanghai
源URL[http://ir.ia.ac.cn/handle/173211/50331]  
专题自动化研究所_中国科学院分子影像重点实验室
通讯作者Tian, Jie; Ji, Nan; Hu, Zhenhua
作者单位1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
2.Chinese Acad Sci, CAS Key Lab Mol Imaging, Beijing Key Lab Mol Imaging, Inst Automat, Beijing 100190, Peoples R China
3.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing 100190, Peoples R China
4.Capital Med Univ, Beijing Tiantan Hosp, Dept Neurosurg, Beijing 100070, Peoples R China
5.Beijing Tiantan Hosp, China Natl Clin Res Ctr Neurol Dis, Beijing 100070, Peoples R China
6.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Engn Med, Beijing 100191, Peoples R China
7.Xidian Univ, Engn Res Ctr Mol & Neuro Imaging Minist Educ, Sch Life Sci & Technol, Xian 710071, Peoples R China
推荐引用方式
GB/T 7714		 Xiao, Anqi,Shen, Biluo,Shi, Xiaojing,et al. Intraoperative Glioma Grading Using Neural Architecture Search and Multi-Modal Imaging[J]. IEEE TRANSACTIONS ON MEDICAL IMAGING,2022,41(10):2570-2581.
APA Xiao, Anqi.,Shen, Biluo.,Shi, Xiaojing.,Zhang, Zhe.,Zhang, Zeyu.,...&Hu, Zhenhua.(2022).Intraoperative Glioma Grading Using Neural Architecture Search and Multi-Modal Imaging.IEEE TRANSACTIONS ON MEDICAL IMAGING,41(10),2570-2581.
MLA Xiao, Anqi,et al."Intraoperative Glioma Grading Using Neural Architecture Search and Multi-Modal Imaging".IEEE TRANSACTIONS ON MEDICAL IMAGING 41.10(2022):2570-2581.

入库方式： OAI收割

来源：自动化研究所

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