文献类型：期刊文献

英文题名：An optimal deep learning framework for multi-type hemorrhagic lesions detection and quantification in head CT images for traumatic brain injury

作者：Phaphuangwittayakul, Aniwat[1];Guo, Yi[1,2,3];Ying, Fangli[4];Dawod, Ahmad Yahya[5];Angkurawaranon, Salita[6];Angkurawaranon, Chaisiri[7]

机构：[1]East China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai, Peoples R China;[2]Natl Engn Lab Big Data Distribut & Exchange Techn, Shanghai, Peoples R China;[3]Shanghai Engn Res Ctr Big Data & Internet Audienc, Shanghai, Peoples R China;[4]East China Univ Sci & Technol, Dept Comp Sci & Engn, State Key Lab Bioreactor Engn, Shanghai, Peoples R China;[5]Chiang Mai Univ, Int Coll Digital Innovat ICDI, Chiang Mai, Thailand;[6]Chiang Mai Univ, Fac Med, Dept Radiol, Chiang Mai, Thailand;[7]Chiang Mai Univ, Fac Med, Dept Family Med, Chiang Mai, Thailand

年份：2022

卷号：52

期号：7

起止页码：7320

外文期刊名：APPLIED INTELLIGENCE

收录：;EI(收录号：20213910951908);WOS:【SCI-EXPANDED(收录号:WOS:000699008300001)】；

基金：This research is financially supported by The National Key Research and Development Program of China (grant number 2018YFC0807105) and Science and Technology Committee of Shanghai Municipality (STCSM) (grant numbers 17DZ1101003, 18511106602 and 18DZ2252300). Partially Supported by Open Funding Project of the State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, China. Study resources and dataset were supported by International College of Digital Innovation (ICDI) and Faculty of Medicine (grant number 143-2562), Chiang Mai University.

语种：英文

外文关键词：Traumatic brain injury; Medical imaging; Computed tomography; Deep learning; Brain lesion segmentation; Quantitative assessment algorithm

摘要：Traumatic Brain Injury (TBI) could lead to intracranial hemorrhage (ICH), which has now been identified as a major cause of death after trauma if it is not adequately diagnosed and properly treated within the first 24 hours. CT examination is widely preferred for urgent ICH diagnosis, which enables the fast identification and detection of ICH regions. However, the use of it requires the clinical interpretation by experts to identify the subtypes of ICH. Besides, it is unable to provide the details needed to conduct quantitative assessment, such as the volume and thickness of hemorrhagic lesions, which may have prognostic importance to the decision-making on emergency treatment. In this paper, an optimal deep learning framework is proposed to assist the quantitative assessment for ICH diagnosis and the accurate detection of different subtypes of ICH through head CT scan. Firstly, the format of raw input data is converted from 3D DICOM to NIfTI. Secondly, a pre-trained multi-class semantic segmentation model is applied to each slice of CT images, so as to obtain a precise 3D mask of the whole ICH region. Thirdly, a fine-tuned classification neural network is employed to extract the key features from the raw input data and identify the subtypes of ICH. Finally, a quantitative assessment algorithm is adopted to automatically measure both thickness and volume via the 3D shape mask combined with the output probabilities of the classification network. The results of our extensive experiments demonstrate the effectiveness of the proposed framework where the average accuracy of 96.21 percent is achieved for three types of hemorrhage. The capability of our optimal classification model to distinguish between different types of lesion plays a significant role in reducing the false-positive rate in the existing work. Furthermore, the results suggest that our automatic quantitative assessment algorithm is effective in providing clinically relevant quantification in terms of volume and thickness. It is more important than the qualitative assessment conducted through visual inspection to the decision-making on emergency surgical treatment.