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Xing Z, Zhu Z, Jiang Z, Zhao J, Chen Q, Xing W, Pan L, Zeng Y, Liu A, Ding J. Automatic Urinary Stone Detection System for Abdominal Non-Enhanced CT Images Reduces the Burden on Radiologists. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:444-454. [PMID: 38343222 PMCID: PMC11031534 DOI: 10.1007/s10278-023-00946-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 10/18/2023] [Accepted: 10/18/2023] [Indexed: 04/20/2024]
Abstract
To develop a fully automatic urinary stone detection system (kidney, ureter, and bladder) and to test it in a real clinical environment. The local institutional review board approved this retrospective single-center study that used non-enhanced abdominopelvic CT scans from patients admitted urology (uPatients) and emergency (ePatients). The uPatients were randomly divided into training and validation sets in a ratio of 3:1. We designed a cascade urinary stone map location-feature pyramid networks (USm-FPNs) and innovatively proposed a ureter distance heatmap method to estimate the ureter position on non-enhanced CT to further reduce the false positives. The performances of the system were compared using the free-response receiver operating characteristic curve and the precision-recall curve. This study included 811 uPatients and 356 ePatients. At stone level, the cascade detector USm-FPNs has the mean of false positives per scan (mFP) 1.88 with the sensitivity 0.977 in validation set, and mFP was further reduced to 1.18 with the sensitivity 0.977 after combining the ureter distance heatmap. At patient level, the sensitivity and precision were as high as 0.995 and 0.990 in validation set, respectively. In a real clinical set of ePatients (27.5% of patients contain stones), the mFP was 1.31 with as high as sensitivity 0.977, and the diagnostic time reduced by > 20% with the system help. A fully automatic detection system for entire urinary stones on non-enhanced CT scans was proposed and reduces obviously the burden on junior radiologists without compromising sensitivity in real emergency data.
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Affiliation(s)
- Zhaoyu Xing
- Department of Urology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, China
| | - Zuhui Zhu
- Department of Radiology, Nantong Hospital of Traditional Chinese Medicine, Nantong, Jiangsu, China
| | - Zhenxing Jiang
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, China
| | - Jingshi Zhao
- Department of Radiology, Fourth Affiliated Hospital of Harbin Medical University, Harbin, China
| | - Qin Chen
- Department of Radiology, People's Hospital of Pengzhou, Chengdu, Sichuan, China
| | - Wei Xing
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, China
| | - Liang Pan
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, China
| | - Yan Zeng
- Department of Research Center, Shanghai United Imaging Intelligence Co. Ltd, Shanghai, China.
| | - Aie Liu
- Department of Research Center, Shanghai United Imaging Intelligence Co. Ltd, Shanghai, China.
| | - Jiule Ding
- Department of Radiology, Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, China.
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Tong X, Wang S, Zhang J, Fan Y, Liu Y, Wei W. Automatic Osteoporosis Screening System Using Radiomics and Deep Learning from Low-Dose Chest CT Images. Bioengineering (Basel) 2024; 11:50. [PMID: 38247927 PMCID: PMC10813496 DOI: 10.3390/bioengineering11010050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 12/21/2023] [Accepted: 12/29/2023] [Indexed: 01/23/2024] Open
Abstract
OBJECTIVE Develop two fully automatic osteoporosis screening systems using deep learning (DL) and radiomics (Rad) techniques based on low-dose chest CT (LDCT) images and evaluate their diagnostic effectiveness. METHODS In total, 434 patients who underwent LDCT and bone mineral density (BMD) examination were retrospectively enrolled and divided into the development set (n = 333) and temporal validation set (n = 101). An automatic thoracic vertebra cancellous bone (TVCB) segmentation model was developed. The Dice similarity coefficient (DSC) was used to evaluate the segmentation performance. Furthermore, the three-class Rad and DL models were developed to distinguish osteoporosis, osteopenia, and normal bone mass. The diagnostic performance of these models was evaluated using the receiver operating characteristic (ROC) curve and decision curve analysis (DCA). RESULTS The automatic segmentation model achieved excellent segmentation performance, with a mean DSC of 0.96 ± 0.02 in the temporal validation set. The Rad model was used to identify osteoporosis, osteopenia, and normal BMD in the temporal validation set, with respective area under the receiver operating characteristic curve (AUC) values of 0.943, 0.801, and 0.932. The DL model achieved higher AUC values of 0.983, 0.906, and 0.969 for the same categories in the same validation set. The Delong test affirmed that both models performed similarly in BMD assessment. However, the accuracy of the DL model is 81.2%, which is better than the 73.3% accuracy of the Rad model in the temporal validation set. Additionally, DCA indicated that the DL model provided a greater net benefit compared to the Rad model across the majority of the reasonable threshold probabilities Conclusions: The automated segmentation framework we developed can accurately segment cancellous bone on low-dose chest CT images. These predictive models, which are based on deep learning and radiomics, provided comparable diagnostic performance in automatic BMD assessment. Nevertheless, it is important to highlight that the DL model demonstrates higher accuracy and precision than the Rad model.
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Affiliation(s)
| | | | | | | | | | - Wei Wei
- Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian 116014, China (S.W.); (Y.F.)
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Lo CM, Hung PH. Predictive stroke risk model with vision transformer-based Doppler features. Med Phys 2024; 51:126-138. [PMID: 38043124 DOI: 10.1002/mp.16861] [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: 04/08/2023] [Revised: 11/17/2023] [Accepted: 11/17/2023] [Indexed: 12/05/2023] Open
Abstract
BACKGROUND Acute stroke is the leading cause of death and disability globally, with an estimated 16 million cases each year. The progression of carotid stenosis reduces blood flow to the intracranial vasculature, causing stroke. Early recognition of ischemic stroke is crucial for disease treatment and management. PURPOSE A computer-aided diagnosis (CAD) system was proposed in this study to rapidly evaluate ischemic stroke in carotid color Doppler (CCD). METHODS Based on the ground truth from the clinical examination report, the vision transformer (ViT) features extracted from all CCD images (513 stroke and 458 normal images) were combined in machine learning classifiers to generate the likelihood of ischemic stroke for each image. The pretrained weights from ImageNet reduced the time-consuming training process. The accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve were calculated to evaluate the stroke prediction model. The chi-square test, DeLong test, and Bonferroni correction for multiple comparisons were applied to deal with the type-I error. Only p values equal to or less than 0.00125 were considered to be statistically significant. RESULTS The proposed CAD system achieved an accuracy of 89%, a sensitivity of 94%, a specificity of 84%, and an area under the receiver operating characteristic curve of 0.95, outperforming the convolutional neural networks AlexNet (82%, p < 0.001), Inception-v3 (78%, p < 0.001), ResNet101 (84%, p < 0.001), and DenseNet201 (85%, p < 0.01). The computational time in model training was only 30 s, which would be efficient and practical in clinical use. CONCLUSIONS The experiment shows the promising use of CCD images in stroke estimation. Using the pretrained ViT architecture, the image features can be automatically and efficiently generated without human intervention. The proposed CAD system provides a rapid and reliable suggestion for diagnosing ischemic stroke.
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Affiliation(s)
- Chung-Ming Lo
- Graduate Institute of Library, Information and Archival Studies, National Chengchi University, Taipei, Taiwan
| | - Peng-Hsiang Hung
- Department of Radiology, Mackay Memorial Hospital, Taipei, Taiwan
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Zhou J, Chen Y, Xia N, Zhao B, Wei Y, Yang Y, Liu J. Predicting the formation of mixed pattern hemorrhages in ruptured middle cerebral artery aneurysms based on a decision tree model: A multicenter study. Clin Neurol Neurosurg 2023; 234:108016. [PMID: 37862728 DOI: 10.1016/j.clineuro.2023.108016] [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: 08/21/2023] [Revised: 10/13/2023] [Accepted: 10/15/2023] [Indexed: 10/22/2023]
Abstract
OBJECTIVE Mixed-pattern hemorrhages (MPH) commonly occur in ruptured middle cerebral artery (MCA) aneurysms and are associated with poor clinical outcomes. This study aimed to predict the formation of MPH in a multicenter database of MCA aneurysms using a decision tree model. METHODS We retrospectively reviewed patients with ruptured MCA aneurysms between January 2009 and June 2020. The MPH was defined as subarachnoid hemorrhages with intracranial hematomas and/or intraventricular hemorrhages and/or subdural hematomas. Univariate and multivariate logistic regression analyses were used to explore the prediction factors of the formation of MPH. Based on these prediction factors, a decision tree model was developed to predict the formation of MPH. Additional independent datasets were used for external validation. RESULTS We enrolled 436 patients with ruptured MCA aneurysms detected by computed tomography angiography; 285 patients had MPH (65.4%). A multivariate logistic regression analysis showed that age, aneurysm size, multiple aneurysms, and the presence of a daughter dome were the independent prediction factors of the formation of MPH. The areas under receiver operating characteristic curves of the decision tree model in the training, internal, and external validation cohorts were 0.951, 0.927, and 0.901, respectively. CONCLUSION Age, aneurysm size, the presence of a daughter dome, and multiple aneurysms were the independent prediction factors of the formation of MPH. The decision tree model is a useful visual triage tool to predict the formation of MPH that could facilitate the management of unruptured aneurysms in routine clinical work.
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Affiliation(s)
- Jiafeng Zhou
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325000, China
| | - Yongchun Chen
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325000, China
| | - Nengzhi Xia
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325000, China
| | - Bing Zhao
- Department of Neurosurgery, Renji Hospital Shanghai Jiaotong University School of Medicine Shanghai, 200127, China
| | - Yuguo Wei
- GE Healthcare, Precision Health Institution, Hangzhou, Zhejiang, China
| | - Yunjun Yang
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325000, China
| | - Jinjin Liu
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325000, China.
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Bizjak Ž, Špiclin Ž. A Systematic Review of Deep-Learning Methods for Intracranial Aneurysm Detection in CT Angiography. Biomedicines 2023; 11:2921. [PMID: 38001922 PMCID: PMC10669551 DOI: 10.3390/biomedicines11112921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 10/26/2023] [Accepted: 10/27/2023] [Indexed: 11/26/2023] Open
Abstract
Background: Subarachnoid hemorrhage resulting from cerebral aneurysm rupture is a significant cause of morbidity and mortality. Early identification of aneurysms on Computed Tomography Angiography (CTA), a frequently used modality for this purpose, is crucial, and artificial intelligence (AI)-based algorithms can improve the detection rate and minimize the intra- and inter-rater variability. Thus, a systematic review and meta-analysis were conducted to assess the diagnostic accuracy of deep-learning-based AI algorithms in detecting cerebral aneurysms using CTA. Methods: PubMed (MEDLINE), Embase, and the Cochrane Library were searched from January 2015 to July 2023. Eligibility criteria involved studies using fully automated and semi-automatic deep-learning algorithms for detecting cerebral aneurysms on the CTA modality. Eligible studies were assessed using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines and the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool. A diagnostic accuracy meta-analysis was conducted to estimate pooled lesion-level sensitivity, size-dependent lesion-level sensitivity, patient-level specificity, and the number of false positives per image. An enhanced FROC curve was utilized to facilitate comparisons between the studies. Results: Fifteen eligible studies were assessed. The findings indicated that the methods exhibited high pooled sensitivity (0.87, 95% confidence interval: 0.835 to 0.91) in detecting intracranial aneurysms at the lesion level. Patient-level sensitivity was not reported due to the lack of a unified patient-level sensitivity definition. Only five studies involved a control group (healthy subjects), whereas two provided information on detection specificity. Moreover, the analysis of size-dependent sensitivity reported in eight studies revealed that the average sensitivity for small aneurysms (<3 mm) was rather low (0.56). Conclusions: The studies included in the analysis exhibited a high level of accuracy in detecting intracranial aneurysms larger than 3 mm in size. Nonetheless, there is a notable gap that necessitates increased attention and research focus on the detection of smaller aneurysms, the use of a common test dataset, and an evaluation of a consistent set of performance metrics.
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Affiliation(s)
- Žiga Bizjak
- Laboratory of Imaging Technologies, Faculty of Electrical Engineering, University of Ljubljana, 1000 Ljubljana, Slovenia
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Ayoub M, Liao Z, Hussain S, Li L, Zhang CWJ, Wong KKL. End to end vision transformer architecture for brain stroke assessment based on multi-slice classification and localization using computed tomography. Comput Med Imaging Graph 2023; 109:102294. [PMID: 37713999 DOI: 10.1016/j.compmedimag.2023.102294] [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: 06/14/2023] [Revised: 08/30/2023] [Accepted: 08/30/2023] [Indexed: 09/17/2023]
Abstract
BACKGROUND Brain stroke is a leading cause of disability and death worldwide, and early diagnosis and treatment are critical to improving patient outcomes. Current stroke diagnosis methods are subjective and prone to errors, as radiologists rely on manual selection of the most important CT slice. This highlights the need for more accurate and reliable automated brain stroke diagnosis and localization methods to improve patient outcomes. PURPOSE In this study, we aimed to enhance the vision transformer architecture for the multi-slice classification of CT scans of each patient into three categories, including Normal, Infarction, Hemorrhage, and patient-wise stroke localization, based on end-to-end vision transformer architecture. This framework can provide an automated, objective, and consistent approach to stroke diagnosis and localization, enabling personalized treatment plans based on the location and extent of the stroke. METHODS We modified the Vision Transformer (ViT) in combination with neural network layers for the multi-slice classification of brain CT scans of each patient into normal, infarction, and hemorrhage classes. For stroke localization, we used the ViT architecture and convolutional neural network layers to detect stroke and localize it by bounding boxes for infarction and hemorrhage regions in a patient-wise manner based on multi slices. RESULTS Our proposed framework achieved an overall accuracy of 87.51% in classifying brain CT scan slices and showed high precision in localizing the stroke patient-wise. Our results demonstrate the potential of our method for accurate and reliable stroke diagnosis and localization. CONCLUSION Our study enhanced ViT architecture for automated stroke diagnosis and localization using brain CT scans, which could have significant implications for stroke management and treatment. The use of deep learning algorithms can provide a more objective and consistent approach to stroke diagnosis and potentially enable personalized treatment plans based on the location and extent of the stroke. Further studies are needed to validate our method on larger and more diverse datasets and to explore its clinical utility in real-world settings.
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Affiliation(s)
- Muhammad Ayoub
- School of Computer Science and Engineering, Central South University, Changsha 410017, Hunan, China
| | - Zhifang Liao
- School of Computer Science and Engineering, Central South University, Changsha 410017, Hunan, China
| | - Shabir Hussain
- Department of Computer Science, National College of Business Administration and Economics, Lahore, Punjab, 05499, Pakistan
| | - Lifeng Li
- Department of Radiology, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha 410017, China
| | - Chris W J Zhang
- Department of Mechanical Engineering, College of Engineering, University of Saskatchewan, S7N 5A9 Saskatoon, SK, Canada
| | - Kelvin K L Wong
- Department of Mechanical Engineering, College of Engineering, University of Saskatchewan, S7N 5A9 Saskatoon, SK, Canada.
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Lyu Z, King K, Rezaeitaleshmahalleh M, Pienta D, Mu N, Zhao C, Zhou W, Jiang J. Deep-learning-based image segmentation for image-based computational hemodynamic analysis of abdominal aortic aneurysms: a comparison study. Biomed Phys Eng Express 2023; 9:067001. [PMID: 37625388 DOI: 10.1088/2057-1976/acf3ed] [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: 06/01/2023] [Accepted: 08/25/2023] [Indexed: 08/27/2023]
Abstract
Computational hemodynamics is increasingly being used to quantify hemodynamic characteristics in and around abdominal aortic aneurysms (AAA) in a patient-specific fashion. However, the time-consuming manual annotation hinders the clinical translation of computational hemodynamic analysis. Thus, we investigate the feasibility of using deep-learning-based image segmentation methods to reduce the time required for manual segmentation. Two of the latest deep-learning-based image segmentation methods, ARU-Net and CACU-Net, were used to test the feasibility of automated computer model creation for computational hemodynamic analysis. Morphological features and hemodynamic metrics of 30 computed tomography angiography (CTA) scans were compared between pre-dictions and manual models. The DICE score for both networks was 0.916, and the correlation value was above 0.95, indicating their ability to generate models comparable to human segmentation. The Bland-Altman analysis shows a good agreement between deep learning and manual segmentation results. Compared with manual (computational hemodynamics) model recreation, the time for automated computer model generation was significantly reduced (from ∼2 h to ∼10 min). Automated image segmentation can significantly reduce time expenses on the recreation of patient-specific AAA models. Moreover, our study showed that both CACU-Net and ARU-Net could accomplish AAA segmentation, and CACU-Net outperformed ARU-Net in terms of accuracy and time-saving.
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Affiliation(s)
- Zonghan Lyu
- Biomedical Engineering, Michigan Technological University, Houghton, Michigan, MI, United States of America
- Joint Center for Biocomputing and Digital Health, Health Research Institute and Institute of Computing and Cybernetics, Michigan Technological University, Houghton, Michigan, MI, United States of America
| | - Kristin King
- Biomedical Engineering, Michigan Technological University, Houghton, Michigan, MI, United States of America
- Joint Center for Biocomputing and Digital Health, Health Research Institute and Institute of Computing and Cybernetics, Michigan Technological University, Houghton, Michigan, MI, United States of America
| | - Mostafa Rezaeitaleshmahalleh
- Biomedical Engineering, Michigan Technological University, Houghton, Michigan, MI, United States of America
- Joint Center for Biocomputing and Digital Health, Health Research Institute and Institute of Computing and Cybernetics, Michigan Technological University, Houghton, Michigan, MI, United States of America
| | - Drew Pienta
- Joint Center for Biocomputing and Digital Health, Health Research Institute and Institute of Computing and Cybernetics, Michigan Technological University, Houghton, Michigan, MI, United States of America
- Applied Computing, Michigan Technological University, Houghton, Michigan, MI, United States of America
| | - Nan Mu
- Biomedical Engineering, Michigan Technological University, Houghton, Michigan, MI, United States of America
- Joint Center for Biocomputing and Digital Health, Health Research Institute and Institute of Computing and Cybernetics, Michigan Technological University, Houghton, Michigan, MI, United States of America
| | - Chen Zhao
- Joint Center for Biocomputing and Digital Health, Health Research Institute and Institute of Computing and Cybernetics, Michigan Technological University, Houghton, Michigan, MI, United States of America
- Applied Computing, Michigan Technological University, Houghton, Michigan, MI, United States of America
| | - Weihua Zhou
- Joint Center for Biocomputing and Digital Health, Health Research Institute and Institute of Computing and Cybernetics, Michigan Technological University, Houghton, Michigan, MI, United States of America
- Applied Computing, Michigan Technological University, Houghton, Michigan, MI, United States of America
| | - Jingfeng Jiang
- Biomedical Engineering, Michigan Technological University, Houghton, Michigan, MI, United States of America
- Joint Center for Biocomputing and Digital Health, Health Research Institute and Institute of Computing and Cybernetics, Michigan Technological University, Houghton, Michigan, MI, United States of America
- Department of Radiology, Mayo Clinic, Rochester, Minnesota, MN, United States of America
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Irfan M, Malik KM, Ahmad J, Malik G. StrokeNet: An automated approach for segmentation and rupture risk prediction of intracranial aneurysm. Comput Med Imaging Graph 2023; 108:102271. [PMID: 37556901 DOI: 10.1016/j.compmedimag.2023.102271] [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: 03/08/2023] [Revised: 06/19/2023] [Accepted: 07/05/2023] [Indexed: 08/11/2023]
Abstract
Intracranial Aneurysms (IA) present a complex challenge for neurosurgeons as the risks associated with surgical intervention, such as Subarachnoid Hemorrhage (SAH) mortality and morbidity, may outweigh the benefits of aneurysmal occlusion in some cases. Hence, there is a critical need for developing techniques that assist physicians in assessing the risk of aneurysm rupture to determine which aneurysms require treatment. However, a reliable IA rupture risk prediction technique is currently unavailable. To address this issue, this study proposes a novel approach for aneurysm segmentation and multidisciplinary rupture prediction using 2D Digital Subtraction Angiography (DSA) images. The proposed method involves training a fully connected convolutional neural network (CNN) to segment aneurysm regions in DSA images, followed by extracting and fusing different features using a multidisciplinary approach, including deep features, geometrical features, Fourier descriptor, and shear pressure on the aneurysm wall. The proposed method also adopts a fast correlation-based filter approach to drop highly correlated features from the set of fused features. Finally, the selected fused features are passed through a Decision Tree classifier to predict the rupture severity of the associated aneurysm into four classes: Mild, Moderate, Severe, and Critical. The proposed method is evaluated on a newly developed DSA image dataset and on public datasets to assess its generalizability. The system's performance is also evaluated on DSA images annotated by expert neurosurgeons for the rupture risk assessment of the segmented aneurysm. The proposed system outperforms existing state-of-the-art segmentation methods, achieving an 85 % accuracy against annotated DSA images for the risk assessment of aneurysmal rupture.
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Affiliation(s)
- Muhammad Irfan
- SMILES LAB, Department of Computer Science and Engineering, Oakland University, Rochester, MI, 48309, USA
| | - Khalid Mahmood Malik
- SMILES LAB, Department of Computer Science and Engineering, Oakland University, Rochester, MI, 48309, USA.
| | - Jamil Ahmad
- Department of Computer Vision, Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, United Arab Emirates
| | - Ghaus Malik
- Executive Vice-Chair at Department of Neurosurgery, Henry Ford Health System, Detroit, MI, USA
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