1
|
Liu Y, Wen Z, Wang Y, Zhong Y, Wang J, Hu Y, Zhou P, Guo S. Artificial intelligence in ischemic stroke images: current applications and future directions. Front Neurol 2024; 15:1418060. [PMID: 39050128 PMCID: PMC11266078 DOI: 10.3389/fneur.2024.1418060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Accepted: 06/27/2024] [Indexed: 07/27/2024] Open
Abstract
This paper reviews the current research progress in the application of Artificial Intelligence (AI) based on ischemic stroke imaging, analyzes the main challenges, and explores future research directions. This study emphasizes the application of AI in areas such as automatic segmentation of infarct areas, detection of large vessel occlusion, prediction of stroke outcomes, assessment of hemorrhagic transformation risk, forecasting of recurrent ischemic stroke risk, and automatic grading of collateral circulation. The research indicates that Machine Learning (ML) and Deep Learning (DL) technologies have tremendous potential for improving diagnostic accuracy, accelerating disease identification, and predicting disease progression and treatment responses. However, the clinical application of these technologies still faces challenges such as limitations in data volume, model interpretability, and the need for real-time monitoring and updating. Additionally, this paper discusses the prospects of applying large language models, such as the transformer architecture, in ischemic stroke imaging analysis, emphasizing the importance of establishing large public databases and the need for future research to focus on the interpretability of algorithms and the comprehensiveness of clinical decision support. Overall, AI has significant application value in the management of ischemic stroke; however, existing technological and practical challenges must be overcome to achieve its widespread application in clinical practice.
Collapse
Affiliation(s)
- Ying Liu
- School of Nursing, Southwest Medical University, Luzhou, China
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Zhongjian Wen
- School of Nursing, Southwest Medical University, Luzhou, China
- Wound Healing Basic Research and Clinical Applications Key Laboratory of Luzhou, Southwest Medical University, Luzhou, China
| | - Yiren Wang
- School of Nursing, Southwest Medical University, Luzhou, China
- Wound Healing Basic Research and Clinical Applications Key Laboratory of Luzhou, Southwest Medical University, Luzhou, China
| | - Yuxin Zhong
- School of Nursing, Guizhou Medical University, Guiyang, China
| | - Jianxiong Wang
- Department of Rehabilitation, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Yiheng Hu
- Department of Medical Imaging, Southwest Medical University, Luzhou, China
| | - Ping Zhou
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Shengmin Guo
- Nursing Department, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| |
Collapse
|
2
|
Fu L, Guan LN, Zuo H. Long period changes of hippocampal diffusion kurtosis imaging and its correlation with cognitive dysfunction after incomplete cerebral ischemia-reperfusion in rats. Exp Brain Res 2023; 241:2807-2816. [PMID: 37878109 DOI: 10.1007/s00221-023-06723-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 10/13/2023] [Indexed: 10/26/2023]
Abstract
This study aims to summarize the changes of functional diffusion kurtosis imaging (DKI) parameters in the bilateral hippocampal CA1 region of the hemorrhagic shock reperfusion (HSR) model of rats and their correlation with cognitive dysfunction. Adult male Sprague-Dawley rats (9-10 weeks of age, weighing 350-400 g) were randomized into the HSR group (n = 30) and the sham-operated group (Sham) (n = 30). Rats in the HSR group and the Sham group were subdivided into five time points (1, 2, 4, 8, and 12 weeks) for examination. Diffusion kurtosis imaging (DKI) was performed. Cognitive dysfunction was analyzed by the Morris Water Maze. The correlation between the DKI parameters and cognitive dysfunction was analyzed by the Spearman correlation. In the HSR group, the values of axial kurtosis (Ka), radial kurtosis (Kr), and mean kurtosis (MK) in the bilateral hippocampal CA1 of rats at 1, 2, 4, 8 and 12 weeks after the surgery were significantly higher. The rats in the HSR group had significantly longer escape latency than in the Sham group. The rats in the HSR group had significantly shorter time and shorter distance in target quadrant than those in the Sham group. The escape latency had positive correlation with MK, Ka, and Kr. The distance and the time in target quadrant had negative correlation with MK, Ka, and Kr. The parameters get from the DKI could accurately evaluate the abnormal blood perfusion and microstructure changes in hippocampal CA1 area of the incomplete cerebral ischemia reperfusion rats induced by HSR. MK, Ka, and Kr values could reflect the decreased learning and memory ability in HSR rat model.
Collapse
Affiliation(s)
- Lan Fu
- Department of Computed Tomography Diagnosis, Cangzhou Central Hospital, No. 16 Xinhua Western Road, Yunhe District, Cangzhou, 061000, Hebei, China.
| | - Lin-Na Guan
- Department of Computed Tomography Diagnosis, Cangzhou Central Hospital, No. 16 Xinhua Western Road, Yunhe District, Cangzhou, 061000, Hebei, China
| | - Hongye Zuo
- Department of Computed Tomography Diagnosis, Cangzhou Central Hospital, No. 16 Xinhua Western Road, Yunhe District, Cangzhou, 061000, Hebei, China
| |
Collapse
|
3
|
Chen S, Duan J, Zhang N, Qi M, Li J, Wang H, Wang R, Ju R, Duan Y, Qi S. MSA-YOLOv5: Multi-scale attention-based YOLOv5 for automatic detection of acute ischemic stroke from multi-modality MRI images. Comput Biol Med 2023; 165:107471. [PMID: 37716245 DOI: 10.1016/j.compbiomed.2023.107471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 09/02/2023] [Accepted: 09/04/2023] [Indexed: 09/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Acute ischemic stroke (AIS) is a common neurological disorder characterized by the sudden onset of cerebral ischemia, leading to functional impairments. Swift and precise detection of AIS lesions is crucial for stroke diagnosis and treatment but poses a significant challenge. This study aims to leverage multimodal fusion technology to combine complementary information from various modalities, thereby enhancing the detection performance of AIS target detection models. METHODS In this retrospective study of AIS, we collected data from 316 AIS patients and created a multi-modality magnetic resonance imaging (MRI) dataset. We propose a Multi-Scale Attention-based YOLOv5 (MSA-YOLOv5), targeting challenges such as small lesion size and blurred borders at low resolutions. Specifically, we augment YOLOv5 with a prediction head to detect objects at various scales. Next, we replace the original prediction head with a Multi-Scale Swin Transformer Prediction Head (MS-STPH), which reduces computational complexity to linear levels and enhances the ability to detect small lesions. We incorporate a Second-Order channel attention (SOCA) module to adaptively rescale channel features by employing second-order feature statistics for more discriminative representations. Finally, we further validate the effectiveness of our method using the ISLES 2022 dataset. RESULTS On our in-house AIS dataset, MSA-YOLOv5 achieves a 79.0% mAP0.5, substantially surpassing other single-stage models. Compared to two-stage models, it maintains a comparable performance level while significantly reducing the number of parameters and resolution. On the ISLES 2022 dataset, MSA-YOLOv5 attains an 80.0% mAP0.5, outperforming other network models by a considerable margin. MS-STPH and SOCA modules can significantly increase mAP0.5 by 2.7% and 1.9%, respectively. Visualization interpretability results show that the proposed MSA-YOLOv5 restricts high attention in the small regions of AIS lesions. CONCLUSIONS The proposed MSA-YOLOv5 is capable of automatically and effectively detecting acute ischemic stroke lesions in multimodal images, particularly for small lesions and artifacts. Our enhanced model reduces the number of parameters while improving detection accuracy. This model can potentially assist radiologists in providing more accurate diagnosis, and enable clinicians to develop better treatment plans.
Collapse
Affiliation(s)
- Shannan Chen
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China.
| | - Jinfeng Duan
- Department of Cardiovascular Surgery, General Hospital of Northern Theater Command, Shenyang, China; Postgraduate College, China Medical University, Shenyang, China.
| | - Nan Zhang
- Department of Radiology, General Hospital of Northern Theater Command, Shenyang, China.
| | - Miao Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China; Department of Radiology, General Hospital of Northern Theater Command, Shenyang, China.
| | - Jinze Li
- Department of Radiology, General Hospital of Northern Theater Command, Shenyang, China.
| | - Hong Wang
- Department of Radiology, General Hospital of Northern Theater Command, Shenyang, China.
| | - Rongqiang Wang
- Department of Radiology, General Hospital of Northern Theater Command, Shenyang, China.
| | - Ronghui Ju
- Department of Radiology, The People's Hospital of Liaoning Province, Shenyang, China.
| | - Yang Duan
- Department of Radiology, General Hospital of Northern Theater Command, Shenyang, China.
| | - Shouliang Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China.
| |
Collapse
|
4
|
Li YH, Lin SC, Chung HW, Chang CC, Peng HH, Huang TY, Shen WC, Tsai CH, Lo YC, Lee TY, Juan CH, Juan CE, Chang HC, Liu YJ, Juan CJ. The role of input imaging combination and ADC threshold on segmentation of acute ischemic stroke lesion using U-Net. Eur Radiol 2023; 33:6157-6167. [PMID: 37095361 DOI: 10.1007/s00330-023-09622-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Revised: 02/11/2023] [Accepted: 02/17/2023] [Indexed: 04/26/2023]
Abstract
BACKGROUND To evaluate the effect of the weighting of input imaging combo and ADC threshold on the performance of the U-Net and to find an optimized input imaging combo and ADC threshold in segmenting acute ischemic stroke (AIS) lesion. METHODS This study retrospectively enrolled a total of 212 patients having AIS. Four combos, including ADC-ADC-ADC (AAA), DWI-ADC-ADC (DAA), DWI-DWI-ADC (DDA), and DWI-DWI-DWI (DDD), were used as input images, respectively. Three ADC thresholds including 0.6, 0.8 and 1.8 × 10-3 mm2/s were applied. Dice similarity coefficient (DSC) was used to evaluate the segmentation performance of U-Nets. Nonparametric Kruskal-Wallis test with Tukey-Kramer post-hoc tests were used for comparison. A p < .05 was considered statistically significant. RESULTS The DSC significantly varied among different combos of images and different ADC thresholds. Hybrid U-Nets outperformed uniform U-Nets at ADC thresholds of 0.6 × 10-3 mm2/s and 0.8 × 10-3 mm2/s (p < .001). The U-Net with imaging combo of DDD had segmentation performance similar to hybrid U-Nets at an ADC threshold of 1.8 × 10-3 mm2/s (p = .062 to 1). The U-Net using the imaging combo of DAA at the ADC threshold of 0.6 × 10-3 mm2/s achieved the highest DSC in the segmentation of AIS lesion. CONCLUSIONS The segmentation performance of U-Net for AIS varies among the input imaging combos and ADC thresholds. The U-Net is optimized by choosing the imaging combo of DAA at an ADC threshold of 0.6 × 10-3 mm2/s in segmentating AIS lesion with highest DSC. KEY POINTS • Segmentation performance of U-Net for AIS differs among input imaging combos. • Segmentation performance of U-Net for AIS differs among ADC thresholds. • U-Net is optimized using DAA with ADC = 0.6 × 10-3 mm2/s.
Collapse
Affiliation(s)
- Ya-Hui Li
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan, Republic of China
- Department of Medical Imaging, China Medical University Hsinchu Hospital, No. 199, Sec. 1, Xinglong Rd., Zhubei City, Hsinchu County 302, Hsinchu, Taiwan, Republic of China
| | - Shao-Chieh Lin
- Department of Medical Imaging, China Medical University Hsinchu Hospital, No. 199, Sec. 1, Xinglong Rd., Zhubei City, Hsinchu County 302, Hsinchu, Taiwan, Republic of China
- Ph.D. Program in Electrical and Communication Engineering, Feng Chia University, Taichung, Taiwan, Republic of China
| | - Hsiao-Wen Chung
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan, Republic of China
- Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan, Republic of China
| | - Chia-Ching Chang
- Department of Medical Imaging, China Medical University Hsinchu Hospital, No. 199, Sec. 1, Xinglong Rd., Zhubei City, Hsinchu County 302, Hsinchu, Taiwan, Republic of China
- Department of Management Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan, Republic of China
| | - Hsu-Hsia Peng
- Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan, Republic of China
| | - Teng-Yi Huang
- Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, Republic of China
| | - Wu-Chung Shen
- Department of Radiology, School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan, Republic of China
- Department of Medical Imaging, Medical University Hospital, Taichung, Taiwan, Republic of China
| | - Chon-Haw Tsai
- Department of Neurology, China Medical University Hospital, Taichung, Taiwan, Republic of China
| | - Yu-Chien Lo
- Department of Medical Imaging, Medical University Hospital, Taichung, Taiwan, Republic of China
| | - Tung-Yang Lee
- Cheng Ching Hospital, Taichung, Taiwan, Republic of China
- Master's Program of Biomedical Informatics and Biomedical Engineering, Feng Chia University, Taichung, Taiwan, Republic of China
| | - Cheng-Hsuan Juan
- Cheng Ching Hospital, Taichung, Taiwan, Republic of China
- Master's Program of Biomedical Informatics and Biomedical Engineering, Feng Chia University, Taichung, Taiwan, Republic of China
| | - Cheng-En Juan
- Master's Program of Biomedical Informatics and Biomedical Engineering, Feng Chia University, Taichung, Taiwan, Republic of China
| | - Hing-Chiu Chang
- Department of Biomedical Engineering, The Chinese University of Hong Kong, ERB1112, 11/F, William M.W. Mong Engineering Building, Shatin, N.T, Hong Kong.
- Multi-Scale Medical Robotics Center, The Chinese University of Hong Kong, Shatin, N.T, Hong Kong.
| | - Yi-Jui Liu
- Department of Automatic Control Engineering, Feng Chia University, No. 100 Wenhwa Rd., Seatwen, 40724, Taichung, Taiwan, Republic of China.
| | - Chun-Jung Juan
- Department of Medical Imaging, China Medical University Hsinchu Hospital, No. 199, Sec. 1, Xinglong Rd., Zhubei City, Hsinchu County 302, Hsinchu, Taiwan, Republic of China.
- Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan, Republic of China.
- Department of Radiology, School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan, Republic of China.
- Department of Medical Imaging, Medical University Hospital, Taichung, Taiwan, Republic of China.
- Department of Biomedical Engineering, National Defense Medical Center, Taipei, Taiwan, Republic of China.
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, Republic of China.
| |
Collapse
|
5
|
Rosbach N, Fischer S, Koch V, Vogl TJ, Bochennek K, Lehrnbecher T, Mahmoudi S, Grünewald L, Grünwald F, Bernatz S. Correlation of mean apparent diffusion coefficient (ADC) and maximal standard uptake value (SUVmax) evaluated by diffusion-weighted MRI and 18F-FDG-PET/CT in children with Hodgkin lymphoma: a feasibility study. Radiol Oncol 2023; 57:150-157. [PMID: 37341195 DOI: 10.2478/raon-2023-0021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 03/27/2023] [Indexed: 06/22/2023] Open
Abstract
BACKGROUND The objective was to analyse if magnetic resonance imaging (MRI) can act as a non-radiation exposure surrogate for (18)F-Fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) in children with histologically confirmed Hodgkin lymphoma (HL) before treatment. This was done by analysing a potential correlation between apparent diffusion coefficient (ADC) in MRI and the maximum standardized uptake value (SUVmax) in FDG-PET/CT. PATIENTS AND METHODS Seventeen patients (six female, eleven male, median age: 16 years, range: 12-20 years) with histologically confirmed HL were retrospectively analysed. The patients underwent both MRI and (18)F-FDG PET/CT before the start of treatment. (18)F-FDG PET/CT data and correlating ADC maps in MRI were collected. For each HL-lesion two readers independently evaluated the SUVmax and correlating meanADC. RESULTS The seventeen patients had a total of 72 evaluable lesions of HL and there was no significant difference in the number of lesions between male and female patients (median male: 15, range: 12-19 years, median female: 17 range: 12-18 years, p = 0.021). The mean duration between MRI and PET/CT was 5.9 ± 5.3 days. The inter-reader agreement as assessed by the intraclass correlation coefficient (ICC) was excellent (ICC = 0.98, 95% CI: 0.97-0.99). The correlated SUVmax and meanADC of all 17 patients (ROIs n = 72) showed a strong negative correlation of -0.75 (95% CI: -0.84, - -0.63, p = 0.001). Analysis revealed a difference in the correlations of the examination fields. The correlated SUVmax and meanADC showed a strong correlation at neck and thoracal examinations (neck: -0.83, 95% CI: -0.93, - -0.63, p < 0.0001, thoracal: -0.82, 95% CI: -0.91, - -0.64, p < 0.0001) and a fair correlation at abdominal examinations of -0.62 (95% CI: -0.83, - -0.28, p = 0.001). CONCLUSIONS SUVmax and meanADC showed a strong negative correlation in paediatric HL lesions. The assessment seemed robust according to inter-reader agreements. Our results suggest that ADC maps and meanADC have the potential to replace PET/CT in the analysis of disease activity in paediatric Hodgkin lymphoma patients. This may help reduce the number of PET/CT examinations and decrease radiation exposure to children.
Collapse
Affiliation(s)
- Nicolas Rosbach
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
| | - Sebastian Fischer
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
| | - Vitali Koch
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
| | - Thomas J Vogl
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
| | - Konrad Bochennek
- Division of Paediatric Haematology and Oncology, Hospital for Children and Adolescents, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
| | - Thomas Lehrnbecher
- Division of Paediatric Haematology and Oncology, Hospital for Children and Adolescents, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
| | - Scherwin Mahmoudi
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
| | - Leon Grünewald
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
| | - Frank Grünwald
- Department of Nuclear Medicine, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
| | - Simon Bernatz
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
| |
Collapse
|
6
|
Hsu K, Yuh DY, Lin SC, Lyu PS, Pan GX, Zhuang YC, Chang CC, Peng HH, Lee TY, Juan CH, Juan CE, Liu YJ, Juan CJ. Improving performance of deep learning models using 3.5D U-Net via majority voting for tooth segmentation on cone beam computed tomography. Sci Rep 2022; 12:19809. [PMID: 36396696 PMCID: PMC9672125 DOI: 10.1038/s41598-022-23901-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Accepted: 11/07/2022] [Indexed: 11/18/2022] Open
Abstract
Deep learning allows automatic segmentation of teeth on cone beam computed tomography (CBCT). However, the segmentation performance of deep learning varies among different training strategies. Our aim was to propose a 3.5D U-Net to improve the performance of the U-Net in segmenting teeth on CBCT. This study retrospectively enrolled 24 patients who received CBCT. Five U-Nets, including 2Da U-Net, 2Dc U-Net, 2Ds U-Net, 2.5Da U-Net, 3D U-Net, were trained to segment the teeth. Four additional U-Nets, including 2.5Dv U-Net, 3.5Dv5 U-Net, 3.5Dv4 U-Net, and 3.5Dv3 U-Net, were obtained using majority voting. Mathematical morphology operations including erosion and dilation (E&D) were applied to remove diminutive noise speckles. Segmentation performance was evaluated by fourfold cross validation using Dice similarity coefficient (DSC), accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV). Kruskal-Wallis test with post hoc analysis using Bonferroni correction was used for group comparison. P < 0.05 was considered statistically significant. Performance of U-Nets significantly varies among different training strategies for teeth segmentation on CBCT (P < 0.05). The 3.5Dv5 U-Net and 2.5Dv U-Net showed DSC and PPV significantly higher than any of five originally trained U-Nets (all P < 0.05). E&D significantly improved the DSC, accuracy, specificity, and PPV (all P < 0.005). The 3.5Dv5 U-Net achieved highest DSC and accuracy among all U-Nets. The segmentation performance of the U-Net can be improved by majority voting and E&D. Overall speaking, the 3.5Dv5 U-Net achieved the best segmentation performance among all U-Nets.
Collapse
Affiliation(s)
- Kang Hsu
- grid.260565.20000 0004 0634 0356Department of Periodontology, School of Dentistry, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC ,grid.260565.20000 0004 0634 0356School of Dentistry and Graduate Institute of Dental Science, National Defense Medical Center, Taipei, Taiwan, ROC
| | - Da-Yo Yuh
- grid.260565.20000 0004 0634 0356Department of Periodontology, School of Dentistry, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, ROC
| | - Shao-Chieh Lin
- Department of Medical Imaging, Xinglong Rd, China Medical University Hsinchu Hospital, 199, Sec. 1, Zhubei, 302 Hsinchu Taiwan, ROC ,grid.411298.70000 0001 2175 4846Ph.D. Program in Electrical and Communication Engineering, Feng Chia University, Taichung, Taiwan, ROC
| | - Pin-Sian Lyu
- Department of Medical Imaging, Xinglong Rd, China Medical University Hsinchu Hospital, 199, Sec. 1, Zhubei, 302 Hsinchu Taiwan, ROC ,grid.411298.70000 0001 2175 4846Department of Automatic Control Engineering, Feng Chia University, No. 100 Wenhwa Rd., Seatwen, 40724 Taichung Taiwan, ROC
| | - Guan-Xin Pan
- Department of Medical Imaging, Xinglong Rd, China Medical University Hsinchu Hospital, 199, Sec. 1, Zhubei, 302 Hsinchu Taiwan, ROC ,grid.411298.70000 0001 2175 4846Master’s Program of Biomedical Informatics and Biomedical Engineering, Feng Chia University, Taichung, Taiwan, ROC
| | - Yi-Chun Zhuang
- Department of Medical Imaging, Xinglong Rd, China Medical University Hsinchu Hospital, 199, Sec. 1, Zhubei, 302 Hsinchu Taiwan, ROC ,grid.411298.70000 0001 2175 4846Master’s Program of Biomedical Informatics and Biomedical Engineering, Feng Chia University, Taichung, Taiwan, ROC
| | - Chia-Ching Chang
- Department of Medical Imaging, Xinglong Rd, China Medical University Hsinchu Hospital, 199, Sec. 1, Zhubei, 302 Hsinchu Taiwan, ROC ,grid.260539.b0000 0001 2059 7017Department of Management Science, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
| | - Hsu-Hsia Peng
- grid.38348.340000 0004 0532 0580Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan, ROC
| | - Tung-Yang Lee
- grid.411298.70000 0001 2175 4846Master’s Program of Biomedical Informatics and Biomedical Engineering, Feng Chia University, Taichung, Taiwan, ROC ,grid.413844.e0000 0004 0638 8798Cheng Ching Hospital, Taichung, Taiwan, ROC
| | - Cheng-Hsuan Juan
- Department of Medical Imaging, Xinglong Rd, China Medical University Hsinchu Hospital, 199, Sec. 1, Zhubei, 302 Hsinchu Taiwan, ROC ,grid.411298.70000 0001 2175 4846Master’s Program of Biomedical Informatics and Biomedical Engineering, Feng Chia University, Taichung, Taiwan, ROC ,grid.413844.e0000 0004 0638 8798Cheng Ching Hospital, Taichung, Taiwan, ROC
| | - Cheng-En Juan
- grid.411298.70000 0001 2175 4846Department of Automatic Control Engineering, Feng Chia University, No. 100 Wenhwa Rd., Seatwen, 40724 Taichung Taiwan, ROC
| | - Yi-Jui Liu
- grid.411298.70000 0001 2175 4846Department of Automatic Control Engineering, Feng Chia University, No. 100 Wenhwa Rd., Seatwen, 40724 Taichung Taiwan, ROC
| | - Chun-Jung Juan
- Department of Medical Imaging, Xinglong Rd, China Medical University Hsinchu Hospital, 199, Sec. 1, Zhubei, 302 Hsinchu Taiwan, ROC ,grid.38348.340000 0004 0532 0580Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan, ROC ,grid.254145.30000 0001 0083 6092Department of Radiology, School of Medicine, College of Medicine, China Medical University, Taichung, Taiwan, ROC ,grid.411508.90000 0004 0572 9415Department of Medical Imaging, China Medical University Hospital, Taichung, Taiwan, ROC ,grid.260565.20000 0004 0634 0356Department of Biomedical Engineering, National Defense Medical Center, Taipei, Taiwan, ROC ,grid.19188.390000 0004 0546 0241Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan, ROC
| |
Collapse
|