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Malik M, Chong B, Fernandez J, Shim V, Kasabov NK, Wang A. Stroke Lesion Segmentation and Deep Learning: A Comprehensive Review. Bioengineering (Basel) 2024; 11:86. [PMID: 38247963 PMCID: PMC10813717 DOI: 10.3390/bioengineering11010086] [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: 12/18/2023] [Revised: 01/05/2024] [Accepted: 01/15/2024] [Indexed: 01/23/2024] Open
Abstract
Stroke is a medical condition that affects around 15 million people annually. Patients and their families can face severe financial and emotional challenges as it can cause motor, speech, cognitive, and emotional impairments. Stroke lesion segmentation identifies the stroke lesion visually while providing useful anatomical information. Though different computer-aided software are available for manual segmentation, state-of-the-art deep learning makes the job much easier. This review paper explores the different deep-learning-based lesion segmentation models and the impact of different pre-processing techniques on their performance. It aims to provide a comprehensive overview of the state-of-the-art models and aims to guide future research and contribute to the development of more robust and effective stroke lesion segmentation models.
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Affiliation(s)
- Mishaim Malik
- Auckland Bioengineering Institute, The University of Auckland, Auckland 1010, New Zealand; (M.M.); (B.C.); (N.K.K.)
| | - Benjamin Chong
- Auckland Bioengineering Institute, The University of Auckland, Auckland 1010, New Zealand; (M.M.); (B.C.); (N.K.K.)
- Faculty of Medical and Health Sciences, The University of Auckland, Auckland 1010, New Zealand
- Centre for Brain Research, The University of Auckland, Auckland 1010, New Zealand
| | - Justin Fernandez
- Auckland Bioengineering Institute, The University of Auckland, Auckland 1010, New Zealand; (M.M.); (B.C.); (N.K.K.)
- Centre for Brain Research, The University of Auckland, Auckland 1010, New Zealand
- Mātai Medical Research Institute, Gisborne 4010, New Zealand
| | - Vickie Shim
- Auckland Bioengineering Institute, The University of Auckland, Auckland 1010, New Zealand; (M.M.); (B.C.); (N.K.K.)
- Mātai Medical Research Institute, Gisborne 4010, New Zealand
| | - Nikola Kirilov Kasabov
- Auckland Bioengineering Institute, The University of Auckland, Auckland 1010, New Zealand; (M.M.); (B.C.); (N.K.K.)
- Knowledge Engineering and Discovery Research Innovation, School of Engineering, Computer and Mathematical Sciences, Auckland University of Technology, Auckland 1010, New Zealand
- Institute for Information and Communication Technologies, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria
- Knowledge Engineering Consulting Ltd., Auckland 1071, New Zealand
| | - Alan Wang
- Auckland Bioengineering Institute, The University of Auckland, Auckland 1010, New Zealand; (M.M.); (B.C.); (N.K.K.)
- Faculty of Medical and Health Sciences, The University of Auckland, Auckland 1010, New Zealand
- Centre for Brain Research, The University of Auckland, Auckland 1010, New Zealand
- Mātai Medical Research Institute, Gisborne 4010, New Zealand
- Medical Imaging Research Centre, The University of Auckland, Auckland 1010, New Zealand
- Centre for Co-Created Ageing Research, The University of Auckland, Auckland 1010, New Zealand
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Offersen CM, Sørensen J, Sheng K, Carlsen JF, Langkilde AR, Pai A, Truelsen TC, Nielsen MB. Artificial Intelligence for Automated DWI/FLAIR Mismatch Assessment on Magnetic Resonance Imaging in Stroke: A Systematic Review. Diagnostics (Basel) 2023; 13:2111. [PMID: 37371006 DOI: 10.3390/diagnostics13122111] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 06/15/2023] [Accepted: 06/16/2023] [Indexed: 06/29/2023] Open
Abstract
We conducted this Systematic Review to create an overview of the currently existing Artificial Intelligence (AI) methods for Magnetic Resonance Diffusion-Weighted Imaging (DWI)/Fluid-Attenuated Inversion Recovery (FLAIR)-mismatch assessment and to determine how well DWI/FLAIR mismatch algorithms perform compared to domain experts. We searched PubMed Medline, Ovid Embase, Scopus, Web of Science, Cochrane, and IEEE Xplore literature databases for relevant studies published between 1 January 2017 and 20 November 2022, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We assessed the included studies using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. Five studies fit the scope of this review. The area under the curve ranged from 0.74 to 0.90. The sensitivity and specificity ranged from 0.70 to 0.85 and 0.74 to 0.84, respectively. Negative predictive value, positive predictive value, and accuracy ranged from 0.55 to 0.82, 0.74 to 0.91, and 0.73 to 0.83, respectively. In a binary classification of ±4.5 h from stroke onset, the surveyed AI methods performed equivalent to or even better than domain experts. However, using the relation between time since stroke onset (TSS) and increasing visibility of FLAIR hyperintensity lesions is not recommended for the determination of TSS within the first 4.5 h. An AI algorithm on DWI/FLAIR mismatch assessment focused on treatment eligibility, outcome prediction, and consideration of patient-specific data could potentially increase the proportion of stroke patients with unknown onset who could be treated with thrombolysis.
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Affiliation(s)
- Cecilie Mørck Offersen
- Department of Radiology, Copenhagen University Hospital Rigshospitalet, 2100 Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Jens Sørensen
- Department of Radiology, Copenhagen University Hospital Rigshospitalet, 2100 Copenhagen, Denmark
| | - Kaining Sheng
- Department of Radiology, Copenhagen University Hospital Rigshospitalet, 2100 Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Jonathan Frederik Carlsen
- Department of Radiology, Copenhagen University Hospital Rigshospitalet, 2100 Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Annika Reynberg Langkilde
- Department of Radiology, Copenhagen University Hospital Rigshospitalet, 2100 Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Akshay Pai
- Department of Radiology, Copenhagen University Hospital Rigshospitalet, 2100 Copenhagen, Denmark
- Cerebriu A/S, 1127 Copenhagen, Denmark
| | - Thomas Clement Truelsen
- Department of Clinical Medicine, University of Copenhagen, 2200 Copenhagen, Denmark
- Department of Neurology, Copenhagen University Hospital Rigshospitalet, 2100 Copenhagen, Denmark
| | - Michael Bachmann Nielsen
- Department of Radiology, Copenhagen University Hospital Rigshospitalet, 2100 Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, 2200 Copenhagen, Denmark
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Chen S, Duan J, Wang H, Wang R, Li J, Qi M, Duan Y, Qi S. Automatic detection of stroke lesion from diffusion-weighted imaging via the improved YOLOv5. Comput Biol Med 2022; 150:106120. [PMID: 36179511 DOI: 10.1016/j.compbiomed.2022.106120] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2022] [Revised: 08/31/2022] [Accepted: 09/17/2022] [Indexed: 11/30/2022]
Abstract
BACKGROUND AND OBJECTIVE Stroke is the second most deadly disease globally and seriously endangers people's lives and health. The automatic detection of stroke lesions from diffusion-weighted imaging (DWI) can improve the diagnosis. Recently, automatic detection methods based on YOLOv5 have been utilized in medical images. However, most of them barely capture the stroke lesions because of their small size and fuzzy boundaries. METHODS To address this problem, a novel method for tracing the edge of the stroke lesion based on YOLOv5 (TE-YOLOv5) is proposed. Specifically, we constantly update the high-level features of the lesion using an aggregate pool (AP) module. Conversely, we feed the extracted feature into the reverse attention (RA) module to trace the edge relationship promptly. Overall, 1681 DWI images of 319 stroke patients have been collected, and experienced radiologists have marked the lesions. DWI images were randomly split into the training and test set at a ratio of 8:2. TE-YOLOv5 has been compared with the related models, and a detailed ablation analysis has been conducted to clarify the role of the RA and AP modules. RESULTS TE-YOLOv5 outperforms its counterparts and achieves competitive performance with a precision of 81.5%, a recall of 75.8%, and a mAP@0.5 of 80.7% (mean average precision while the intersection over union is 0.5) under the same backbone. At the patient level, the positive finding rate can reach 98.51%, while the confidence is set at 80.0%. After ablating RA, the mAP@0.5 decreases to 79.6%; after ablating RA and AP, the mAP@0.5 decreases to 78.1%. CONCLUSIONS The proposed TE-YOLOv5 can automatically and effectively detect stroke lesions from DWI images, especially for those with an extremely small size and blurred boundaries. AP and RA modules can aggregate multi-layer high-level features and concurrently track the edge relationship of stroke lesions. These detection methods might help radiologists improve stroke diagnosis and have great application potential in clinical practice.
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Affiliation(s)
- Shannan Chen
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China; Key Lab of Advanced Design and Intelligent Computing, Ministry of Education, Dalian University, Dalian, China.
| | - Jinfeng Duan
- Department of General Surgery, 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.
| | - Jinze Li
- Department of Radiology, General Hospital of Northern Theater Command, Shenyang, China.
| | - Miao Qi
- Department of Radiology, General Hospital of Northern Theater Command, 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.
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Alis D, Alis C, Yergin M, Topel C, Asmakutlu O, Bagcilar O, Senli YD, Ustundag A, Salt V, Dogan SN, Velioglu M, Selcuk HH, Kara B, Ozer C, Oksuz I, Kizilkilic O, Karaarslan E. A joint convolutional-recurrent neural network with an attention mechanism for detecting intracranial hemorrhage on noncontrast head CT. Sci Rep 2022; 12:2084. [PMID: 35136123 PMCID: PMC8826390 DOI: 10.1038/s41598-022-05872-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 12/30/2021] [Indexed: 12/27/2022] Open
Abstract
To investigate the performance of a joint convolutional neural networks-recurrent neural networks (CNN-RNN) using an attention mechanism in identifying and classifying intracranial hemorrhage (ICH) on a large multi-center dataset; to test its performance in a prospective independent sample consisting of consecutive real-world patients. All consecutive patients who underwent emergency non-contrast-enhanced head CT in five different centers were retrospectively gathered. Five neuroradiologists created the ground-truth labels. The development dataset was divided into the training and validation set. After the development phase, we integrated the deep learning model into an independent center’s PACS environment for over six months for assessing the performance in a real clinical setting. Three radiologists created the ground-truth labels of the testing set with a majority voting. A total of 55,179 head CT scans of 48,070 patients, 28,253 men (58.77%), with a mean age of 53.84 ± 17.64 years (range 18–89) were enrolled in the study. The validation sample comprised 5211 head CT scans, with 991 being annotated as ICH-positive. The model's binary accuracy, sensitivity, and specificity on the validation set were 99.41%, 99.70%, and 98.91, respectively. During the prospective implementation, the model yielded an accuracy of 96.02% on 452 head CT scans with an average prediction time of 45 ± 8 s. The joint CNN-RNN model with an attention mechanism yielded excellent diagnostic accuracy in assessing ICH and its subtypes on a large-scale sample. The model was seamlessly integrated into the radiology workflow. Though slightly decreased performance, it provided decisions on the sample of consecutive real-world patients within a minute.
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Affiliation(s)
- Deniz Alis
- Radiology Department, Acibadem Mehmet Ali Aydinlar University School of Medicine, Istanbul, Turkey
| | - Ceren Alis
- Neurology Department, Istanbul Istinye State Hospital, Istanbul, Turkey.
| | - Mert Yergin
- Department of Software Engineering and Applied Sciences, Bahcesehir University, Istanbul, Turkey
| | - Cagdas Topel
- Department of Radiology, Istanbul Mehmet Akif Ersoy Thoracic and Cardiovascular Surgery Training and Research Hospital, Halkali, Istanbul, Turkey
| | - Ozan Asmakutlu
- Department of Radiology, Istanbul Mehmet Akif Ersoy Thoracic and Cardiovascular Surgery Training and Research Hospital, Halkali, Istanbul, Turkey
| | - Omer Bagcilar
- Radiology Department, Istanbul Silivri State Hospital, Istanbul, Turkey
| | - Yeseren Deniz Senli
- Radiology Department, Cerrahpaşa Medical Faculty, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Ahmet Ustundag
- Radiology Department, Cerrahpaşa Medical Faculty, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Vefa Salt
- Radiology Department, Cerrahpaşa Medical Faculty, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | | | - Murat Velioglu
- Radiology Department, Istanbul Fatih Sultan Mehmet Training and Research Hospital, Istanbul, Turkey
| | - Hakan Hatem Selcuk
- Radiology Department, Istanbul Bakırköy Sadi Konuk Training and Research Hospital, Istanbul, Turkey
| | - Batuhan Kara
- Radiology Department, Istanbul Bakırköy Sadi Konuk Training and Research Hospital, Istanbul, Turkey
| | - Caner Ozer
- Computer Engineering Department, Istanbul Technical University, Istanbul, Turkey
| | - Ilkay Oksuz
- Computer Engineering Department, Istanbul Technical University, Istanbul, Turkey
| | - Osman Kizilkilic
- Radiology Department, Cerrahpaşa Medical Faculty, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Ercan Karaarslan
- Radiology Department, Acibadem Mehmet Ali Aydinlar University School of Medicine, Istanbul, Turkey
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5
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Inamdar MA, Raghavendra U, Gudigar A, Chakole Y, Hegde A, Menon GR, Barua P, Palmer EE, Cheong KH, Chan WY, Ciaccio EJ, Acharya UR. A Review on Computer Aided Diagnosis of Acute Brain Stroke. SENSORS (BASEL, SWITZERLAND) 2021; 21:8507. [PMID: 34960599 PMCID: PMC8707263 DOI: 10.3390/s21248507] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 12/05/2021] [Accepted: 12/09/2021] [Indexed: 01/01/2023]
Abstract
Amongst the most common causes of death globally, stroke is one of top three affecting over 100 million people worldwide annually. There are two classes of stroke, namely ischemic stroke (due to impairment of blood supply, accounting for ~70% of all strokes) and hemorrhagic stroke (due to bleeding), both of which can result, if untreated, in permanently damaged brain tissue. The discovery that the affected brain tissue (i.e., 'ischemic penumbra') can be salvaged from permanent damage and the bourgeoning growth in computer aided diagnosis has led to major advances in stroke management. Abiding to the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines, we have surveyed a total of 177 research papers published between 2010 and 2021 to highlight the current status and challenges faced by computer aided diagnosis (CAD), machine learning (ML) and deep learning (DL) based techniques for CT and MRI as prime modalities for stroke detection and lesion region segmentation. This work concludes by showcasing the current requirement of this domain, the preferred modality, and prospective research areas.
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Affiliation(s)
- Mahesh Anil Inamdar
- Department of Mechatronics, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India;
| | - Udupi Raghavendra
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (Y.C.)
| | - Anjan Gudigar
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (Y.C.)
| | - Yashas Chakole
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (Y.C.)
| | - Ajay Hegde
- Department of Neurosurgery, Kasturba Medical College, Manipal Academy of Higher Education, Manipal 576104, India; (A.H.); (G.R.M.)
| | - Girish R. Menon
- Department of Neurosurgery, Kasturba Medical College, Manipal Academy of Higher Education, Manipal 576104, India; (A.H.); (G.R.M.)
| | - Prabal Barua
- School of Management & Enterprise, University of Southern Queensland, Toowoomba, QLD 4350, Australia;
- Faculty of Engineering and Information Technology, University of Technology, Sydney, NSW 2007, Australia
- Cogninet Brain Team, Cogninet Australia, Sydney, NSW 2010, Australia
| | - Elizabeth Emma Palmer
- School of Women’s and Children’s Health, University of New South Wales, Sydney, NSW 2052, Australia;
| | - Kang Hao Cheong
- Science, Mathematics and Technology Cluster, Singapore University of Technology and Design, Singapore 487372, Singapore;
| | - Wai Yee Chan
- Department of Biomedical Imaging, Research Imaging Centre, University of Malaya, Kuala Lumpur 59100, Malaysia;
| | - Edward J. Ciaccio
- Department of Medicine, Columbia University, New York, NY 10032, USA;
| | - U. Rajendra Acharya
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia;
- School of Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
- Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore 599491, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
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6
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Yeo M, Tahayori B, Kok HK, Maingard J, Kutaiba N, Russell J, Thijs V, Jhamb A, Chandra RV, Brooks M, Barras CD, Asadi H. Review of deep learning algorithms for the automatic detection of intracranial hemorrhages on computed tomography head imaging. J Neurointerv Surg 2021; 13:369-378. [PMID: 33479036 DOI: 10.1136/neurintsurg-2020-017099] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Revised: 12/05/2020] [Accepted: 12/09/2020] [Indexed: 02/06/2023]
Abstract
Artificial intelligence is a rapidly evolving field, with modern technological advances and the growth of electronic health data opening new possibilities in diagnostic radiology. In recent years, the performance of deep learning (DL) algorithms on various medical image tasks have continually improved. DL algorithms have been proposed as a tool to detect various forms of intracranial hemorrhage on non-contrast computed tomography (NCCT) of the head. In subtle, acute cases, the capacity for DL algorithm image interpretation support might improve the diagnostic yield of CT for detection of this time-critical condition, potentially expediting treatment where appropriate and improving patient outcomes. However, there are multiple challenges to DL algorithm implementation, such as the relative scarcity of labeled datasets, the difficulties in developing algorithms capable of volumetric medical image analysis, and the complex practicalities of deployment into clinical practice. This review examines the literature and the approaches taken in the development of DL algorithms for the detection of intracranial hemorrhage on NCCT head studies. Considerations in crafting such algorithms will be discussed, as well as challenges which must be overcome to ensure effective, dependable implementations as automated tools in a clinical setting.
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Affiliation(s)
- Melissa Yeo
- Melbourne Medical School, The University of Melbourne Faculty of Medicine Dentistry and Health Sciences, Melbourne, Victoria, Australia
| | - Bahman Tahayori
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia.,IBM Research Australia, Melbourne, Victoria, Australia
| | - Hong Kuan Kok
- Department of Radiology, Northern Health, Epping, Victoria, Australia.,School of Medicine, Deakin University Faculty of Health, Burwood, Victoria, Australia
| | - Julian Maingard
- School of Medicine, Deakin University Faculty of Health, Burwood, Victoria, Australia.,Interventional Neuroradiology Unit, Monash Health, Clayton, Victoria, Australia
| | - Numan Kutaiba
- Department of Radiology, Austin Health, Heidelberg, Victoria, Australia
| | - Jeremy Russell
- Department of Neurosurgery, Austin Health, Heidelberg, Victoria, Australia
| | - Vincent Thijs
- Stroke Theme, Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australia.,Department of Neurology, Austin Health, Heidelberg, Victoria, Australia
| | - Ashu Jhamb
- Department of Radiology, St Vincent's Hospital Melbourne Pty Ltd, Fitzroy, Victoria, Australia
| | - Ronil V Chandra
- Interventional Neuroradiology Unit, Monash Health, Clayton, Victoria, Australia.,Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia
| | - Mark Brooks
- Stroke Theme, Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australia.,Interventional Neuroradiology Service, Austin Health, Heidelberg, Victoria, Australia
| | - Christen D Barras
- School of Medicine, The University of Adelaide, Adelaide, South Australia, Australia.,South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia
| | - Hamed Asadi
- Stroke Theme, Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australia.,Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Victoria, Australia
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Karthik R, Radhakrishnan M, Rajalakshmi R, Raymann J. Delineation of ischemic lesion from brain MRI using attention gated fully convolutional network. Biomed Eng Lett 2020; 11:3-13. [PMID: 33747599 DOI: 10.1007/s13534-020-00178-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 10/16/2020] [Accepted: 10/24/2020] [Indexed: 11/30/2022] Open
Abstract
Precise delineation of the ischemic lesion from unimodal Magnetic Resonance Imaging (MRI) is a challenging task due to the subtle intensity difference between the lesion and normal tissues. Hence, multispectral MRI modalities are used for characterizing the properties of brain tissues. Traditional lesion detection methods rely on extracting significant hand-engineered features to differentiate normal and abnormal brain tissues. But the identification of those discriminating features is quite complex, as the degree of differentiation varies according to each modality. This can be addressed well by Convolutional Neural Networks (CNN) which supports automatic feature extraction. It is capable of learning the global features from images effectively for image classification. But it loses the context of local information among the pixels that need to be retained for segmentation. Also, it must provide more emphasis on the features of the lesion region for precise reconstruction. The major contribution of this work is the integration of attention mechanism with a Fully Convolutional Network (FCN) to segment ischemic lesion. This attention model is applied to learn and concentrate only on salient features of the lesion region by suppressing the details of other regions. Hence the proposed FCN with attention mechanism was able to segment ischemic lesion of varying size and shape. To study the effectiveness of attention mechanism, various experiments were carried out on ISLES 2015 dataset and a mean dice coefficient of 0.7535 was obtained. Experimental results indicate that there is an improvement of 5% compared to the existing works.
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Affiliation(s)
- R Karthik
- Centre for Cyber Physical Systems, Vellore Institute of Technology, Chennai, India
| | - Menaka Radhakrishnan
- Centre for Cyber Physical Systems, Vellore Institute of Technology, Chennai, India
| | - R Rajalakshmi
- School of Computing Sciences and Engineering, Vellore Institute of Technology, Chennai, Chennai, India
| | - Joel Raymann
- School of Computing Sciences and Engineering, Vellore Institute of Technology, Chennai, Chennai, India
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8
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Ischemic stroke lesion detection, characterization and classification in CT images with optimal features selection. Biomed Eng Lett 2020; 10:333-344. [PMID: 32864172 DOI: 10.1007/s13534-020-00158-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Revised: 05/05/2020] [Accepted: 05/13/2020] [Indexed: 01/16/2023] Open
Abstract
Ischemic stroke is the dominant disorder for mortality and morbidity. For immediate diagnosis and treatment plan of ischemic stroke, computed tomography (CT) images are used. This paper proposes a histogram bin based novel algorithm to segment the ischemic stroke lesion using CT and optimal feature group selection to classify normal and abnormal regions. Steps followed are pre-processing, segmentation, extracting texture features, feature ranking, feature grouping, classification and optimal feature group (FG) selection. The first order features, gray level run length matrix features, gray level co-occurrence matrix features and Hu's moment features are extracted. Classification is done using logistic regression (LR), support vector machine classifier (SVMC), random forest classifier (RFC) and neural network classifier (NNC). This proposed approach effectively detects ischemic stroke lesion with a classification accuracy of 88.77%, 97.86%, 99.79% and 99.79% obtained by the LR, SVMC, RFC and NNC when FG12 is opted, which is validated by fourfold cross validation.
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9
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Computational Image Analysis of Nonenhanced Computed Tomography for Acute Ischaemic Stroke: A Systematic Review. J Stroke Cerebrovasc Dis 2020; 29:104715. [DOI: 10.1016/j.jstrokecerebrovasdis.2020.104715] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Revised: 01/16/2020] [Accepted: 01/28/2020] [Indexed: 11/23/2022] Open
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10
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A deep supervised approach for ischemic lesion segmentation from multimodal MRI using Fully Convolutional Network. Appl Soft Comput 2019. [DOI: 10.1016/j.asoc.2019.105685] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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11
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Tchoketch Kebir S, Mekaoui S, Bouhedda M. A fully automatic methodology for MRI brain tumour detection and segmentation. THE IMAGING SCIENCE JOURNAL 2019. [DOI: 10.1080/13682199.2018.1545412] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Affiliation(s)
- S. Tchoketch Kebir
- Laboratory of Speech Communication and Signal Processing (LCPTS), USTHB, Algiers, Algeria
| | - S. Mekaoui
- Laboratory of Speech Communication and Signal Processing (LCPTS), USTHB, Algiers, Algeria
| | - M. Bouhedda
- Laboratory of Advanced Electronic Systems (LSEA), University of Medea, Medea, Algeria
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