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Mohapatra S, Lee TH, Sahoo PK, Wu CY. Localization of early infarction on non-contrast CT images in acute ischemic stroke with deep learning approach. Sci Rep 2023; 13:19442. [PMID: 37945734 PMCID: PMC10636036 DOI: 10.1038/s41598-023-45573-7] [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: 06/17/2023] [Accepted: 10/21/2023] [Indexed: 11/12/2023] Open
Abstract
Localization of early infarction on first-line Non-contrast computed tomogram (NCCT) guides prompt treatment to improve stroke outcome. Our previous study has shown a good performance in the identification of ischemic injury on NCCT. In the present study, we developed a deep learning (DL) localization model to help localize the early infarction sign on NCCT. This retrospective study included consecutive 517 ischemic stroke (IS) patients who received NCCT within 12 h after stroke onset. A total of 21,436 infarction patches and 20,391 non-infarction patches were extracted from the slice pool of 1,634 NCCT according to brain symmetricity property. The generated patches were fed into different pretrained convolutional neural network (CNN) models such as Visual Geometry Group 16 (VGG16), GoogleNet, Residual Networks 50 (ResNet50), Inception-ResNet-v2 (IR-v2), Inception-v3 and Inception-v4. The selected VGG16 model could detect the early infarction in both supratentorial and infratentorial regions to achieve an average area under curve (AUC) 0.73 after extensive customization. The properly tuned-VGG16 model could identify the early infarction in the cortical, subcortical and cortical plus subcortical areas of supratentorial region with the mean AUC > 0.70. Further, the model could attain 95.6% of accuracy on recognizing infarction lesion in 494 out of 517 IS patients.
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Affiliation(s)
- Sulagna Mohapatra
- Department of Computer Science and Information Engineering, Chang Gung University, 259, Wen-Hwa 1st Road, Guishan, Taoyuan, 33302, Taiwan
| | - Tsong-Hai Lee
- Department of Neurology, Chang Gung Memorial Hospital, Linkou Medical Center, No. 5, Fu-Hsing street, Guishan, Taoyuan, 333, Taiwan
- College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Prasan Kumar Sahoo
- Department of Computer Science and Information Engineering, Chang Gung University, 259, Wen-Hwa 1st Road, Guishan, Taoyuan, 33302, Taiwan.
- Department of Neurology, Chang Gung Memorial Hospital, Linkou Medical Center, No. 5, Fu-Hsing street, Guishan, Taoyuan, 333, Taiwan.
| | - Ching-Yi Wu
- Department of Occupational Therapy and Graduate Institute of Behavioral Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan
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Xu B, Fan Y, Liu J, Zhang G, Wang Z, Li Z, Guo W, Tang X. CHSNet: Automatic lesion segmentation network guided by CT image features for acute cerebral hemorrhage. Comput Biol Med 2023; 164:107334. [PMID: 37573720 DOI: 10.1016/j.compbiomed.2023.107334] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 07/28/2023] [Accepted: 08/07/2023] [Indexed: 08/15/2023]
Abstract
Stroke is a cerebrovascular disease that can lead to severe sequelae such as hemiplegia and mental retardation with a mortality rate of up to 40%. In this paper, we proposed an automatic segmentation network (CHSNet) to segment the lesions in cranial CT images based on the characteristics of acute cerebral hemorrhage images, such as high density, multi-scale, and variable location, and realized the three-dimensional (3D) visualization and localization of the cranial lesions after the segmentation was completed. To enhance the feature representation of high-density regions, and capture multi-scale and up-down information on the target location, we constructed a convolutional neural network with encoding-decoding backbone, Res-RCL module, Atrous Spatial Pyramid Pooling, and Attention Gate. We collected images of 203 patients with acute cerebral hemorrhage, constructed a dataset containing 5998 cranial CT slices, and conducted comparative and ablation experiments on the dataset to verify the effectiveness of our model. Our model achieved the best results on both test sets with different segmentation difficulties, test1: Dice = 0.918, IoU = 0.853, ASD = 0.476, RVE = 0.113; test2: Dice = 0.716, IoU = 0.604, ASD = 5.402, RVE = 1.079. Based on the segmentation results, we achieved 3D visualization and localization of hemorrhage in CT images of stroke patients. The study has important implications for clinical adjuvant diagnosis.
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Affiliation(s)
- Bohao Xu
- School of Medical Technology, Beijing Institute of Technology, Beijing, 100081, China
| | - Yingwei Fan
- School of Medical Technology, Beijing Institute of Technology, Beijing, 100081, China
| | - Jingming Liu
- Emergency Department, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Guobin Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Zhiping Wang
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100050, China
| | - Zhili Li
- BECHOICE (Beijing) Science and Technology Development Ltd., Beijing, 100050, China
| | - Wei Guo
- Emergency Department, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China.
| | - Xiaoying Tang
- School of Medical Technology, Beijing Institute of Technology, Beijing, 100081, China.
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Zhang X, Liu Y, Guo S, Song Z. EG-Unet: Edge-Guided cascaded networks for automated frontal brain segmentation in MR images. Comput Biol Med 2023; 158:106891. [PMID: 37044048 DOI: 10.1016/j.compbiomed.2023.106891] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 03/07/2023] [Accepted: 04/01/2023] [Indexed: 04/05/2023]
Abstract
Accurate segmentation of frontal lobe areas on magnetic resonance imaging (MRI) can assist in diagnosing and managing idiopathic normal-pressure hydrocephalus. However, frontal lobe segmentation is challenging due to the complexity of the degree and shape of damage and the ambiguity of the boundaries of frontal lobe sites. Therefore, to extract the rich edge information and feature representation of the frontal lobe, this paper designs an edge guidance (EG) module to enhance the representation of edge features. Accordingly, an edge-guided cascade network framework (EG-Net) is proposed to segment frontal lobe parts automatically. Two-dimensional MRI slice images are fed into the edge generation and segmentation networks. First, the edge generation network extracts the edge information from the input image. Then, the edge information is sent to the EG module to generate an edge attention map for feature representation enhancement. Meanwhile, multi-scale attentional convolution (MSA) is utilized in the feature coding stage of the segmentation network to obtain feature responses from different perceptual fields in the coding stage and enrich the spatial context information. Besides, the feature fusion module is employed to selectively aggregate the multi-scale features in the coding stage with the edge features output by the EG module. Finally, the two components are fused, and a decoder recovers the spatial information to generate the final prediction results. An extensive quantitative comparison is performed on a publicly available brain MRI dataset (MICCAI 2012) to evaluate the effectiveness of the proposed algorithm. The experimental results indicate that the proposed method achieves an average DICE score of 95.77% compared to some advanced methods, which is 4.96% better than the classical U-Net. The results demonstrate the potential of the proposed EG-Net in improving the accuracy of frontal edge pixel classification through edge guidance.
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Affiliation(s)
- Xiufeng Zhang
- Mechanical and Electrical Engineering, Dalian Minzu University, Liaohe West Road 18, Dalian, China
| | - Yansong Liu
- Mechanical and Electrical Engineering, Dalian Minzu University, Liaohe West Road 18, Dalian, China.
| | - Shengjin Guo
- Mechanical and Electrical Engineering, Dalian Minzu University, Liaohe West Road 18, Dalian, China
| | - Zhao Song
- Shenzhen Hospital, Southern Medical University, Xinhu Road 1333, Shenzhen, China
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Splenic CT radiomics nomogram predicting the risk of upper gastrointestinal hemorrhage in cirrhosis. JOURNAL OF RADIATION RESEARCH AND APPLIED SCIENCES 2023. [DOI: 10.1016/j.jrras.2022.100486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
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Ji P, Chen D, Wei L. Diffusion tensor imaging combined with nerve fiber bundle tracing in acute cerebral infarction. JOURNAL OF RADIATION RESEARCH AND APPLIED SCIENCES 2023. [DOI: 10.1016/j.jrras.2022.100504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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Gava UA, D'Agata F, Tartaglione E, Renzulli R, Grangetto M, Bertolino F, Santonocito A, Bennink E, Vaudano G, Boghi A, Bergui M. Neural network-derived perfusion maps: A model-free approach to computed tomography perfusion in patients with acute ischemic stroke. Front Neuroinform 2023; 17:852105. [PMID: 36970658 PMCID: PMC10034033 DOI: 10.3389/fninf.2023.852105] [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: 01/10/2022] [Accepted: 02/24/2023] [Indexed: 03/29/2023] Open
Abstract
Objective In this study, we investigate whether a Convolutional Neural Network (CNN) can generate informative parametric maps from the pre-processed CT perfusion data in patients with acute ischemic stroke in a clinical setting. Methods The CNN training was performed on a subset of 100 pre-processed perfusion CT dataset, while 15 samples were kept for testing. All the data used for the training/testing of the network and for generating ground truth (GT) maps, using a state-of-the-art deconvolution algorithm, were previously pre-processed using a pipeline for motion correction and filtering. Threefold cross validation had been used to estimate the performance of the model on unseen data, reporting Mean Squared Error (MSE). Maps accuracy had been checked through manual segmentation of infarct core and total hypo-perfused regions on both CNN-derived and GT maps. Concordance among segmented lesions was assessed using the Dice Similarity Coefficient (DSC). Correlation and agreement among different perfusion analysis methods were evaluated using mean absolute volume differences, Pearson correlation coefficients, Bland-Altman analysis, and coefficient of repeatability across lesion volumes. Results The MSE was very low for two out of three maps, and low in the remaining map, showing good generalizability. Mean Dice scores from two different raters and the GT maps ranged from 0.80 to 0.87. Inter-rater concordance was high, and a strong correlation was found between lesion volumes of CNN maps and GT maps (0.99, 0.98, respectively). Conclusion The agreement between our CNN-based perfusion maps and the state-of-the-art deconvolution-algorithm perfusion analysis maps, highlights the potential of machine learning methods applied to perfusion analysis. CNN approaches can reduce the volume of data required by deconvolution algorithms to estimate the ischemic core, and thus might allow the development of novel perfusion protocols with lower radiation dose deployed to the patient.
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Affiliation(s)
- Umberto A Gava
- Division of Neuroradiology, Molinette Hospital, Turin, Italy
- Department of Neurosciences, University of Turin, Turin, Italy
| | | | - Enzo Tartaglione
- Department of Computer Science, University of Turin, Turin, Italy
| | | | - Marco Grangetto
- Department of Computer Science, University of Turin, Turin, Italy
| | - Francesca Bertolino
- Division of Neuroradiology, Molinette Hospital, Turin, Italy
- Department of Neurosciences, University of Turin, Turin, Italy
| | | | - Edwin Bennink
- Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands
| | - Giacomo Vaudano
- Division of Neuroradiology, San Giovanni Bosco Hospital, Turin, Italy
| | - Andrea Boghi
- Division of Neuroradiology, San Giovanni Bosco Hospital, Turin, Italy
| | - Mauro Bergui
- Division of Neuroradiology, Molinette Hospital, Turin, Italy
- Department of Neurosciences, University of Turin, Turin, Italy
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Li Y, Liu Y, Hong Z, Wang Y, Lu X. Combining machine learning with radiomics features in predicting outcomes after mechanical thrombectomy in patients with acute ischemic stroke. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 225:107093. [PMID: 36055039 DOI: 10.1016/j.cmpb.2022.107093] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Revised: 08/25/2022] [Accepted: 08/25/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE Some patients with mechanical thrombectomy will have a poor prognosis. This study establishes a model for predicting the prognosis after mechanical thrombectomy in acute stroke based on diffusion-weighted imaging (DWI) omics characteristics. METHODS A total of 260 stroke patients receiving mechanical thrombectomy in our hospital were randomly divided into a training set (n = 182) and a test set (n = 78) in a 7:3 ratio. The regions of interest (ROI) of the imaging features of the DWI infarct area were extracted, and the minimum absolute contraction and selection operator regression model were used to screen the best radiomics features. A support vector machine classifier established the prediction model of the prognosis after mechanical thrombectomy of acute stroke based on the selected features. The prediction efficiency of the model was evaluated by the receiver operating characteristic (ROC) curve. RESULTS A total of 1936 radiomic features were extracted, and six features highly correlated with prognosis were screened after dimensionality reduction. Based on the DWI model, the ROC analysis showed that the area under the curve (AUC) for correct prediction in the training and test sets was 0.945 and 0.920, respectively. CONCLUSION The model based on the characteristics of radiomics and machine learning has high predictive efficiency for the prognosis of acute stroke after mechanical thrombectomy, which can be used to guide personalized clinical treatment.
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Affiliation(s)
- Yan Li
- Department of Neurosurgery, Cangzhou Central Hospital, Cangzhou Clinical Medical College of Hebei Medical University, Canzhou 061011, China.
| | - Yongchang Liu
- Department of Neurosurgery, Cangzhou Central Hospital, Cangzhou Clinical Medical College of Hebei Medical University, Canzhou 061011, China
| | - Zhen Hong
- Department of Neurosurgery, Cangzhou Central Hospital, Cangzhou Clinical Medical College of Hebei Medical University, Canzhou 061011, China
| | - Ying Wang
- Department of Neurosurgery, Cangzhou Central Hospital, Cangzhou Clinical Medical College of Hebei Medical University, Canzhou 061011, China
| | - Xiuling Lu
- Cangzhou Infectious Disease Hospital, Canzhou 061011, China
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