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Koska IO, Selver MA, Gelal F, Uluc ME, Çetinoğlu YK, Yurttutan N, Serindere M, Dicle O. Voxel level dense prediction of acute stroke territory in DWI using deep learning segmentation models and image enhancement strategies. Jpn J Radiol 2024; 42:962-972. [PMID: 38727961 DOI: 10.1007/s11604-024-01582-8] [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: 12/16/2023] [Accepted: 04/23/2024] [Indexed: 08/31/2024]
Abstract
PURPOSE To build a stroke territory classifier model in DWI by designing the problem as a multiclass segmentation task by defining each stroke territory as distinct segmentation targets and leveraging the guidance of voxel wise dense predictions. MATERIALS AND METHODS Retrospective analysis of DWI images of 218 consecutive acute anterior or posterior ischemic stroke patients examined between January 2017 to April 2020 in a single center was carried out. Each stroke area was defined as distinct segmentation target with different class labels. U-Net based network was trained followed by majority voting of the voxel wise predictions of the model to transform them into patient level stroke territory classes. Effects of bias field correction and registration to a common space were explored. RESULTS Of the 218 patients included in this study, 141 (65%) were anterior stroke, and 77 were posterior stroke (35%) whereas 117 (53%) were male and 101 (47%) were female. The model built with original images reached 0.77 accuracy, while the model built with N4 bias corrected images reached 0.80 and the model built with images which were N4 bias corrected and then registered into a common space reached 0.83 accuracy values. CONCLUSION Voxel wise dense prediction coupled with bias field correction to eliminate artificial signal increase and registration to a common space help models for better performance than using original images. Knowing the properties of target domain while designing deep learning models is important for the overall success of these models.
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Affiliation(s)
- Ilker Ozgur Koska
- Department of Radiology, Behçet Uz Children's Hospital, Izmir, Turkey.
- Dokuz Eylül University The Graduate School of Natural and Applied Sciences, Dokuz Eylül Universtiy, Izmir, Turkey.
| | - M Alper Selver
- Engineering Faculty, Department of Electrical and Electronics Engineering, Dokuz Eylül University, Izmir, 35390, Turkey
- İzmir Health Technologies Development and Accelerator (BioIzmir), Dokuz Eylül University, Izmir, Turkey
| | - Fazil Gelal
- Department of Radiology, Izmir Katip Celebi University Ataturk Training and Research Hospital, Basin Sitesi, Izmir, 35360, Turkey
| | - Muhsin Engin Uluc
- Department of Radiology, Izmir Katip Celebi University Ataturk Training and Research Hospital, Basin Sitesi, Izmir, 35360, Turkey
| | | | - Nursel Yurttutan
- Department of Radiology, Kahramanmaraş Sütçü İmam University Hospital, Kahramanmaraş, Turkey
| | - Mehmet Serindere
- Department of Radiology, Hatay Training and Research Hospital, Güzelburç/Hatay, Turkey
| | - Oğuz Dicle
- Medicine Faculty, Department of Radiology, Dokuz Eylül University, 15 Temmuz Sağlık Sanat Yerleşkesi/İnciraltı, İzmir, 35340, Turkey
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Idesis S, Allegra M, Vohryzek J, Sanz Perl Y, Faskowitz J, Sporns O, Corbetta M, Deco G. A low dimensional embedding of brain dynamics enhances diagnostic accuracy and behavioral prediction in stroke. Sci Rep 2023; 13:15698. [PMID: 37735201 PMCID: PMC10514061 DOI: 10.1038/s41598-023-42533-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 09/11/2023] [Indexed: 09/23/2023] Open
Abstract
Large-scale brain networks reveal structural connections as well as functional synchronization between distinct regions of the brain. The latter, referred to as functional connectivity (FC), can be derived from neuroimaging techniques such as functional magnetic resonance imaging (fMRI). FC studies have shown that brain networks are severely disrupted by stroke. However, since FC data are usually large and high-dimensional, extracting clinically useful information from this vast amount of data is still a great challenge, and our understanding of the functional consequences of stroke remains limited. Here, we propose a dimensionality reduction approach to simplify the analysis of this complex neural data. By using autoencoders, we find a low-dimensional representation encoding the fMRI data which preserves the typical FC anomalies known to be present in stroke patients. By employing the latent representations emerging from the autoencoders, we enhanced patients' diagnostics and severity classification. Furthermore, we showed how low-dimensional representation increased the accuracy of recovery prediction.
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Affiliation(s)
- Sebastian Idesis
- Center for Brain and Cognition (CBC), Department of Information Technologies and Communications (DTIC), Pompeu Fabra University, Edifici Mercè Rodoreda, Carrer Trias i Fargas 25-27, 08005, Barcelona, Catalonia, Spain.
| | - Michele Allegra
- Padova Neuroscience Center (PNC), University of Padova, via Orus 2/B, 35129, Padua, Italy
- Department of Physics and Astronomy "G. Galilei", University of Padova, via Marzolo 8, 35131, Padua, Italy
| | - Jakub Vohryzek
- Center for Brain and Cognition (CBC), Department of Information Technologies and Communications (DTIC), Pompeu Fabra University, Edifici Mercè Rodoreda, Carrer Trias i Fargas 25-27, 08005, Barcelona, Catalonia, Spain
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK
| | - Yonatan Sanz Perl
- Center for Brain and Cognition (CBC), Department of Information Technologies and Communications (DTIC), Pompeu Fabra University, Edifici Mercè Rodoreda, Carrer Trias i Fargas 25-27, 08005, Barcelona, Catalonia, Spain
- Universidad de San Andrés, Buenos Aires, Argentina
- National Scientific and Technical Research Council, Buenos Aires, Argentina
- Institut du Cerveau et de la Moelle Épinière, ICM, Paris, France
| | - Joshua Faskowitz
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, 47405, USA
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, 47405, USA
| | - Maurizio Corbetta
- Padova Neuroscience Center (PNC), University of Padova, via Orus 2/B, 35129, Padua, Italy
- Department of Neuroscience, University of Padova, via Giustiniani 5, 35128, Padua, Italy
- Veneto Institute of Molecular Medicine (VIMM), via Orus 2/B, 35129, Padua, Italy
| | - Gustavo Deco
- Center for Brain and Cognition (CBC), Department of Information Technologies and Communications (DTIC), Pompeu Fabra University, Edifici Mercè Rodoreda, Carrer Trias i Fargas 25-27, 08005, Barcelona, Catalonia, Spain
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3
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Zoetmulder R, Baak L, Khalili N, Marquering HA, Wagenaar N, Benders M, van der Aa NE, Išgum I. Brain segmentation in patients with perinatal arterial ischemic stroke. Neuroimage Clin 2023; 38:103381. [PMID: 36965456 PMCID: PMC10074207 DOI: 10.1016/j.nicl.2023.103381] [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: 11/02/2022] [Revised: 02/20/2023] [Accepted: 03/14/2023] [Indexed: 03/19/2023]
Abstract
BACKGROUND Perinatal arterial ischemic stroke (PAIS) is associated with adverse neurological outcomes. Quantification of ischemic lesions and consequent brain development in newborn infants relies on labor-intensive manual assessment of brain tissues and ischemic lesions. Hence, we propose an automatic method utilizing convolutional neural networks (CNNs) to segment brain tissues and ischemic lesions in MRI scans of infants suffering from PAIS. MATERIALS AND METHODS This single-center retrospective study included 115 patients with PAIS that underwent MRI after the stroke onset (baseline) and after three months (follow-up). Nine baseline and 12 follow-up MRI scans were manually annotated to provide reference segmentations (white matter, gray matter, basal ganglia and thalami, brainstem, ventricles, extra-ventricular cerebrospinal fluid, and cerebellum, and additionally on the baseline scans the ischemic lesions). Two CNNs were trained to perform automatic segmentation on the baseline and follow-up MRIs, respectively. Automatic segmentations were quantitatively evaluated using the Dice coefficient (DC) and the mean surface distance (MSD). Volumetric agreement between segmentations that were manually and automatically obtained was computed. Moreover, the scan quality and automatic segmentations were qualitatively evaluated in a larger set of MRIs without manual annotation by two experts. In addition, the scan quality was qualitatively evaluated in these scans to establish its impact on the automatic segmentation performance. RESULTS Automatic brain tissue segmentation led to a DC and MSD between 0.78-0.92 and 0.18-1.08 mm for baseline, and between 0.88-0.95 and 0.10-0.58 mm for follow-up scans, respectively. For the ischemic lesions at baseline the DC and MSD were between 0.72-0.86 and 1.23-2.18 mm, respectively. Volumetric measurements indicated limited oversegmentation of the extra-ventricular cerebrospinal fluid in both the follow-up and baseline scans, oversegmentation of the ischemic lesions in the left hemisphere, and undersegmentation of the ischemic lesions in the right hemisphere. In scans without imaging artifacts, brain tissue segmentation was graded as excellent in more than 85% and 91% of cases, respectively for the baseline and follow-up scans. For the ischemic lesions at baseline, this was in 61% of cases. CONCLUSIONS Automatic segmentation of brain tissue and ischemic lesions in MRI scans of patients with PAIS is feasible. The method may allow evaluation of the brain development and efficacy of treatment in large datasets.
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Affiliation(s)
- Riaan Zoetmulder
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, Location University of Amsterdam, Amsterdam, the Netherlands; Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Location University of Amsterdam, Amsterdam, the Netherlands; Informatics Institute, University of Amsterdam, Amsterdam, the Netherlands
| | - Lisanne Baak
- Department of Neonatology and Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Nadieh Khalili
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Henk A Marquering
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, Location University of Amsterdam, Amsterdam, the Netherlands; Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Location University of Amsterdam, Amsterdam, the Netherlands
| | - Nienke Wagenaar
- Department of Neonatology and Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Manon Benders
- Department of Neonatology and Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Niek E van der Aa
- Department of Neonatology and Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Ivana Išgum
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, Location University of Amsterdam, Amsterdam, the Netherlands; Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Location University of Amsterdam, Amsterdam, the Netherlands; Informatics Institute, University of Amsterdam, Amsterdam, the Netherlands; Amsterdam Cardiovascular Sciences, Amsterdam, the Netherlands.
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4
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CT-Based Automatic Spine Segmentation Using Patch-Based Deep Learning. INT J INTELL SYST 2023. [DOI: 10.1155/2023/2345835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Abstract
CT vertebral segmentation plays an essential role in various clinical applications, such as computer-assisted surgical interventions, assessment of spinal abnormalities, and vertebral compression fractures. Automatic CT vertebral segmentation is challenging due to the overlapping shadows of thoracoabdominal structures such as the lungs, bony structures such as the ribs, and other issues such as ambiguous object borders, complicated spine architecture, patient variability, and fluctuations in image contrast. Deep learning is an emerging technique for disease diagnosis in the medical field. This study proposes a patch-based deep learning approach to extract the discriminative features from unlabeled data using a stacked sparse autoencoder (SSAE). 2D slices from a CT volume are divided into overlapping patches fed into the model for training. A random under sampling (RUS)-module is applied to balance the training data by selecting a subset of the majority class. SSAE uses pixel intensities alone to learn high-level features to recognize distinctive features from image patches. Each image is subjected to a sliding window operation to express image patches using autoencoder high-level features, which are then fed into a sigmoid layer to classify whether each patch is a vertebra or not. We validate our approach on three diverse publicly available datasets: VerSe, CSI-Seg, and the Lumbar CT dataset. Our proposed method outperformed other models after configuration optimization by achieving 89.9% in precision, 90.2% in recall, 98.9% in accuracy, 90.4% in F-score, 82.6% in intersection over union (IoU), and 90.2% in Dice coefficient (DC). The results of this study demonstrate that our model’s performance consistency using a variety of validation strategies is flexible, fast, and generalizable, making it suited for clinical application.
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Cui L, Fan Z, Yang Y, Liu R, Wang D, Feng Y, Lu J, Fan Y. Deep Learning in Ischemic Stroke Imaging Analysis: A Comprehensive Review. BIOMED RESEARCH INTERNATIONAL 2022; 2022:2456550. [PMID: 36420096 PMCID: PMC9678444 DOI: 10.1155/2022/2456550] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 09/27/2022] [Accepted: 10/20/2022] [Indexed: 09/15/2023]
Abstract
Ischemic stroke is a cerebrovascular disease with a high morbidity and mortality rate, which poses a serious challenge to human health and life. Meanwhile, the management of ischemic stroke remains highly dependent on manual visual analysis of noncontrast computed tomography (CT) or magnetic resonance imaging (MRI). However, artifacts and noise of the equipment as well as the radiologist experience play a significant role on diagnostic accuracy. To overcome these defects, the number of computer-aided diagnostic (CAD) methods for ischemic stroke is increasing substantially during the past decade. Particularly, deep learning models with massive data learning capabilities are recognized as powerful auxiliary tools for the acute intervention and guiding prognosis of ischemic stroke. To select appropriate interventions, facilitate clinical practice, and improve the clinical outcomes of patients, this review firstly surveys the current state-of-the-art deep learning technology. Then, we summarized the major applications in acute ischemic stroke imaging, particularly in exploring the potential function of stroke diagnosis and multimodal prognostication. Finally, we sketched out the current problems and prospects.
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Affiliation(s)
- Liyuan Cui
- School of Medical Imaging, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Zhiyuan Fan
- Centre of Intelligent Medical Technology and Equipment, Binjiang Institute of Zhejiang University, Hangzhou, Zhejiang, China
| | - Yingjian Yang
- School of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Rui Liu
- School of Medical Imaging, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Dajiang Wang
- School of Medical Imaging, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Yingying Feng
- School of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Jiahui Lu
- School of Medical Imaging, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Yifeng Fan
- School of Medical Imaging, Hangzhou Medical College, Hangzhou, Zhejiang, China
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6
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Sreejith S, Subramanian R, Karthik S. Using patching asymmetric regions to assess ischemic stroke lesion in neuro imaging. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-212457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Ischemic stroke is a universal ailment that endangers the life of patients and makes them bedridden until death. Over a decade, doctors and radiologists have been dissecting patient status straightforwardly from the printouts of the slice images delivered by different diagnostic imaging modalities. Computed Tomography (CT) is a frequently used imaging strategy for therapeutic analysis and neuroanatomical investigations. The main objective of the paper is to develop a simple technique with less architectural complication and power consumption. The proposed work is to section the ischemic stroke lesion more efficiently from multi-succession CT images using patching the asymmetric region. The Hough transform segment and extracts the features from the asymmetric region of the CT image and finally, the random forest is implemented to classify the unusual tissues from the CT image dependent on their pathological properties. RF classifier has been trained for different parts of the cerebrum for fragmenting the stroke lesion. The acquired outcomes produce better segmentation accuracy when compared with different strategies. The overall efficiency of the proposed method determines the Ischemic stroke with an accuracy of 95% with an RF classifier. Hence this method can be used in the segmentation process of stroke lesions.
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Affiliation(s)
- S. Sreejith
- Department of Electronics & Communication Engineering, SNS College of Technology, Coimbatore, Tamilnadu, India
| | - R. Subramanian
- Department of Electrical & Electronics Engineering, SNS College of Technology, Coimbatore, Tamilnadu, India
| | - S. Karthik
- Department of Computer Science & Engineering, SNS College of Technology, Coimbatore, Tamilnadu, India
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7
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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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8
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Soltanpour M, Greiner R, Boulanger P, Buck B. Improvement of automatic ischemic stroke lesion segmentation in CT perfusion maps using a learned deep neural network. Comput Biol Med 2021; 137:104849. [PMID: 34530336 DOI: 10.1016/j.compbiomed.2021.104849] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 08/11/2021] [Accepted: 09/04/2021] [Indexed: 10/20/2022]
Abstract
Acute ischemic stroke is one of the leading causes of death and long-term disability worldwide. It occurs when a blood clot blocks an artery that supplies blood to the brain tissue. Segmentation of acute ischemic stroke lesions plays a vital role to improve diagnosis, outcome assessment, and treatment planning. The current standard approach of ischemic stroke lesion segmentation is simply thresholding the Computed Tomography Perfusion (CTP) maps, i.e., quantitative feature maps created by summarizing CTP time sequence scans. However, this approach is not precise enough (its Dice similarity score is only around 50%) to be used in practice. Numerous machine learning-based techniques have recently been proposed to improve the accuracy of ischemic stroke lesion segmentation. Although they have achieved remarkable results, they still need to be improved before they can be used in actual practice. This paper presents a novel deep learning-based technique, MutiRes U-Net, for the segmentation of ischemic stroke lesions in CTP maps. MultiRes U-Net is a modified version of the original U-Net that is re-designed to be robust to segment the objects in different scales and unusual appearances. Additionally, in this paper, we propose to enrich the input CTP maps by using their contra-lateral and corresponding Tmax images. We evaluated the proposed method using the ISLES challenge 2018 dataset. As compared to the state-of-the-art methods, the results show an improvement in segmentation task accuracy. The dice similarity score (DSC) was 68%, the Jaccard score was 57.13%, and the mean absolute volume error was 22.62(ml).
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Affiliation(s)
| | - Russ Greiner
- Department of Computing Science, University of Alberta, Canada.
| | | | - Brian Buck
- Department of Medicine, University of Alberta, Canada.
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9
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Improving Ischemic Stroke Care With MRI and Deep Learning Artificial Intelligence. Top Magn Reson Imaging 2021; 30:187-195. [PMID: 34397968 DOI: 10.1097/rmr.0000000000000290] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
ABSTRACT Advanced magnetic resonance imaging has been used as selection criteria for both acute ischemic stroke treatment and secondary prevention. The use of artificial intelligence, and in particular, deep learning, to synthesize large amounts of data and to understand better how clinical and imaging data can be leveraged to improve stroke care promises a new era of stroke care. In this article, we review common deep learning model structures for stroke imaging, evaluation metrics for model performance, and studies that investigated deep learning application in acute ischemic stroke care and secondary prevention.
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10
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Shafaat O, Bernstock JD, Shafaat A, Yedavalli VS, Elsayed G, Gupta S, Sotoudeh E, Sair HI, Yousem DM, Sotoudeh H. Leveraging artificial intelligence in ischemic stroke imaging. J Neuroradiol 2021; 49:343-351. [PMID: 33984377 DOI: 10.1016/j.neurad.2021.05.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 05/02/2021] [Accepted: 05/03/2021] [Indexed: 11/30/2022]
Abstract
Artificial intelligence (AI) is having a disruptive and transformative effect on clinical medicine. Prompt clinical diagnosis and imaging are critical for minimizing the morbidity and mortality associated with ischemic strokes. Clinicians must understand the current strengths and limitations of AI to provide optimal patient care. Ischemic stroke is one of the medical fields that have been extensively evaluated by artificial intelligence. Presented herein is a review of artificial intelligence applied to clinical management of stroke, geared toward clinicians. In this review, we explain the basic concept of AI and machine learning. This review is without coding and mathematical details and targets the clinicians involved in stroke management without any computer or mathematics' background. Here the AI application in ischemic stroke is summarized and classified into stroke imaging (automated diagnosis of brain infarction, automated ASPECT score calculation, infarction segmentation), prognosis prediction, and patients' selection for treatment.
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Affiliation(s)
- Omid Shafaat
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 1800 Orleans St, Baltimore, MD 21287, USA.
| | - Joshua D Bernstock
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Hale Building, 60 Fenwood Road, Boston, MA 02115, USA.
| | - Amir Shafaat
- Department of Mechanical Engineering, Arak University of Technology, Daneshgah St, 38181-41167 Arak, Iran.
| | - Vivek S Yedavalli
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 1800 Orleans St, Baltimore, MD 21287, USA.
| | - Galal Elsayed
- Department of Neurosurgery, University of Alabama at Birmingham, 1960 6th Ave. S., Birmingham, AL 35233, USA.
| | - Saksham Gupta
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Hale Building, 60 Fenwood Road, Boston, MA 02115, USA.
| | - Ehsan Sotoudeh
- Department of Surgery, Iranian Hospital in Dubai, P.O.BOX: 2330, Al-Wasl Road, Dubai 2330, UAE.
| | - Haris I Sair
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 1800 Orleans St, Baltimore, MD 21287, USA; Radiology Artificial Intelligence Lab (RAIL), Malone Center for Engineering in Healthcare, Johns Hopkins University Whiting School of Engineering, 600 North Wolfe Street, Baltimore, MD 21287, USA.
| | - David M Yousem
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 1800 Orleans St, Baltimore, MD 21287, USA.
| | - Houman Sotoudeh
- Department of Radiology, University of Alabama at Birmingham, 619 19th St S, Birmingham, AL 35294, USA.
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Li Y, Lei Y, Wang P, Jiang M, Liu Y. Embedded stacked group sparse autoencoder ensemble with L1 regularization and manifold reduction. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2020.107003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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12
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Ghosh S, Bian J, Guo Y, Prosperi M. Deep propensity network using a sparse autoencoder for estimation of treatment effects. J Am Med Inform Assoc 2021; 28:1197-1206. [PMID: 33594415 DOI: 10.1093/jamia/ocaa346] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 11/22/2020] [Accepted: 12/28/2020] [Indexed: 12/11/2022] Open
Abstract
OBJECTIVE Drawing causal estimates from observational data is problematic, because datasets often contain underlying bias (eg, discrimination in treatment assignment). To examine causal effects, it is important to evaluate what-if scenarios-the so-called "counterfactuals." We propose a novel deep learning architecture for propensity score matching and counterfactual prediction-the deep propensity network using a sparse autoencoder (DPN-SA)-to tackle the problems of high dimensionality, nonlinear/nonparallel treatment assignment, and residual confounding when estimating treatment effects. MATERIALS AND METHODS We used 2 randomized prospective datasets, a semisynthetic one with nonlinear/nonparallel treatment selection bias and simulated counterfactual outcomes from the Infant Health and Development Program and a real-world dataset from the LaLonde's employment training program. We compared different configurations of the DPN-SA against logistic regression and LASSO as well as deep counterfactual networks with propensity dropout (DCN-PD). Models' performances were assessed in terms of average treatment effects, mean squared error in precision on effect's heterogeneity, and average treatment effect on the treated, over multiple training/test runs. RESULTS The DPN-SA outperformed logistic regression and LASSO by 36%-63%, and DCN-PD by 6%-10% across all datasets. All deep learning architectures yielded average treatment effects close to the true ones with low variance. Results were also robust to noise-injection and addition of correlated variables. Code is publicly available at https://github.com/Shantanu48114860/DPN-SAz. DISCUSSION AND CONCLUSION Deep sparse autoencoders are particularly suited for treatment effect estimation studies using electronic health records because they can handle high-dimensional covariate sets, large sample sizes, and complex heterogeneity in treatment assignments.
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Affiliation(s)
- Shantanu Ghosh
- Department of Computer and Information Science and Engineering, University of Florida, Gainesville, Florida, USA
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Yi Guo
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Mattia Prosperi
- Department of Epidemiology, College of Public Health and Health Professions & College of Medicine, University of Florida, Gainesville, Florida, USA
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13
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Gryska E, Schneiderman J, Björkman-Burtscher I, Heckemann RA. Automatic brain lesion segmentation on standard magnetic resonance images: a scoping review. BMJ Open 2021; 11:e042660. [PMID: 33514580 PMCID: PMC7849889 DOI: 10.1136/bmjopen-2020-042660] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Revised: 01/09/2021] [Accepted: 01/12/2021] [Indexed: 12/11/2022] Open
Abstract
OBJECTIVES Medical image analysis practices face challenges that can potentially be addressed with algorithm-based segmentation tools. In this study, we map the field of automatic MR brain lesion segmentation to understand the clinical applicability of prevalent methods and study designs, as well as challenges and limitations in the field. DESIGN Scoping review. SETTING Three databases (PubMed, IEEE Xplore and Scopus) were searched with tailored queries. Studies were included based on predefined criteria. Emerging themes during consecutive title, abstract, methods and whole-text screening were identified. The full-text analysis focused on materials, preprocessing, performance evaluation and comparison. RESULTS Out of 2990 unique articles identified through the search, 441 articles met the eligibility criteria, with an estimated growth rate of 10% per year. We present a general overview and trends in the field with regard to publication sources, segmentation principles used and types of lesions. Algorithms are predominantly evaluated by measuring the agreement of segmentation results with a trusted reference. Few articles describe measures of clinical validity. CONCLUSIONS The observed reporting practices leave room for improvement with a view to studying replication, method comparison and clinical applicability. To promote this improvement, we propose a list of recommendations for future studies in the field.
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Affiliation(s)
- Emilia Gryska
- Medical Radiation Sciences, Goteborgs universitet Institutionen for kliniska vetenskaper, Goteborg, Sweden
| | - Justin Schneiderman
- Sektionen för klinisk neurovetenskap, Goteborgs Universitet Institutionen for Neurovetenskap och fysiologi, Goteborg, Sweden
| | | | - Rolf A Heckemann
- Medical Radiation Sciences, Goteborgs universitet Institutionen for kliniska vetenskaper, Goteborg, Sweden
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Zhu G, Jiang B, Chen H, Tong E, Xie Y, Faizy TD, Heit JJ, Zaharchuk G, Wintermark M. Artificial Intelligence and Stroke Imaging. Neuroimaging Clin N Am 2020; 30:479-492. [DOI: 10.1016/j.nic.2020.07.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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15
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Shiyam Sundar LK, Muzik O, Buvat I, Bidaut L, Beyer T. Potentials and caveats of AI in hybrid imaging. Methods 2020; 188:4-19. [PMID: 33068741 DOI: 10.1016/j.ymeth.2020.10.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 10/05/2020] [Accepted: 10/07/2020] [Indexed: 12/18/2022] Open
Abstract
State-of-the-art patient management frequently mandates the investigation of both anatomy and physiology of the patients. Hybrid imaging modalities such as the PET/MRI, PET/CT and SPECT/CT have the ability to provide both structural and functional information of the investigated tissues in a single examination. With the introduction of such advanced hardware fusion, new problems arise such as the exceedingly large amount of multi-modality data that requires novel approaches of how to extract a maximum of clinical information from large sets of multi-dimensional imaging data. Artificial intelligence (AI) has emerged as one of the leading technologies that has shown promise in facilitating highly integrative analysis of multi-parametric data. Specifically, the usefulness of AI algorithms in the medical imaging field has been heavily investigated in the realms of (1) image acquisition and reconstruction, (2) post-processing and (3) data mining and modelling. Here, we aim to provide an overview of the challenges encountered in hybrid imaging and discuss how AI algorithms can facilitate potential solutions. In addition, we highlight the pitfalls and challenges in using advanced AI algorithms in the context of hybrid imaging and provide suggestions for building robust AI solutions that enable reproducible and transparent research.
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Affiliation(s)
- Lalith Kumar Shiyam Sundar
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | | | - Irène Buvat
- Laboratoire d'Imagerie Translationnelle en Oncologie, Inserm, Institut Curie, Orsay, France
| | - Luc Bidaut
- College of Science, University of Lincoln, Lincoln, UK
| | - Thomas Beyer
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria.
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16
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Segato A, Marzullo A, Calimeri F, De Momi E. Artificial intelligence for brain diseases: A systematic review. APL Bioeng 2020; 4:041503. [PMID: 33094213 PMCID: PMC7556883 DOI: 10.1063/5.0011697] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Accepted: 09/09/2020] [Indexed: 12/15/2022] Open
Abstract
Artificial intelligence (AI) is a major branch of computer science that is fruitfully used for analyzing complex medical data and extracting meaningful relationships in datasets, for several clinical aims. Specifically, in the brain care domain, several innovative approaches have achieved remarkable results and open new perspectives in terms of diagnosis, planning, and outcome prediction. In this work, we present an overview of different artificial intelligent techniques used in the brain care domain, along with a review of important clinical applications. A systematic and careful literature search in major databases such as Pubmed, Scopus, and Web of Science was carried out using "artificial intelligence" and "brain" as main keywords. Further references were integrated by cross-referencing from key articles. 155 studies out of 2696 were identified, which actually made use of AI algorithms for different purposes (diagnosis, surgical treatment, intra-operative assistance, and postoperative assessment). Artificial neural networks have risen to prominent positions among the most widely used analytical tools. Classic machine learning approaches such as support vector machine and random forest are still widely used. Task-specific algorithms are designed for solving specific problems. Brain images are one of the most used data types. AI has the possibility to improve clinicians' decision-making ability in neuroscience applications. However, major issues still need to be addressed for a better practical use of AI in the brain. To this aim, it is important to both gather comprehensive data and build explainable AI algorithms.
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Affiliation(s)
- Alice Segato
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan 20133, Italy
| | - Aldo Marzullo
- Department of Mathematics and Computer Science, University of Calabria, Rende 87036, Italy
| | - Francesco Calimeri
- Department of Mathematics and Computer Science, University of Calabria, Rende 87036, Italy
| | - Elena De Momi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan 20133, Italy
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17
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Mecheter I, Alic L, Abbod M, Amira A, Ji J. MR Image-Based Attenuation Correction of Brain PET Imaging: Review of Literature on Machine Learning Approaches for Segmentation. J Digit Imaging 2020; 33:1224-1241. [PMID: 32607906 PMCID: PMC7573060 DOI: 10.1007/s10278-020-00361-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Recent emerging hybrid technology of positron emission tomography/magnetic resonance (PET/MR) imaging has generated a great need for an accurate MR image-based PET attenuation correction. MR image segmentation, as a robust and simple method for PET attenuation correction, has been clinically adopted in commercial PET/MR scanners. The general approach in this method is to segment the MR image into different tissue types, each assigned an attenuation constant as in an X-ray CT image. Machine learning techniques such as clustering, classification and deep networks are extensively used for brain MR image segmentation. However, only limited work has been reported on using deep learning in brain PET attenuation correction. In addition, there is a lack of clinical evaluation of machine learning methods in this application. The aim of this review is to study the use of machine learning methods for MR image segmentation and its application in attenuation correction for PET brain imaging. Furthermore, challenges and future opportunities in MR image-based PET attenuation correction are discussed.
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Affiliation(s)
- Imene Mecheter
- Department of Electronic and Computer Engineering, Brunel University London, Uxbridge, UK.
- Department of Electrical and Computer Engineering, Texas A & M University at Qatar, Doha, Qatar.
| | - Lejla Alic
- Magnetic Detection and Imaging Group, Faculty of Science and Technology, University of Twente, Enschede, Netherlands
| | - Maysam Abbod
- Department of Electronic and Computer Engineering, Brunel University London, Uxbridge, UK
| | - Abbes Amira
- Institute of Artificial Intelligence, De Montfort University, Leicester, UK
| | - Jim Ji
- Department of Electrical and Computer Engineering, Texas A & M University at Qatar, Doha, Qatar
- Department of Electrical and Computer Engineering, Texas A & M University, College Station, TX, USA
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18
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Brain MR Imaging Segmentation Using Convolutional Auto Encoder Network for PET Attenuation Correction. ACTA ACUST UNITED AC 2020. [DOI: 10.1007/978-3-030-55190-2_32] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/16/2023]
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19
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Chen L, Chen J. Deep Neural Network for Automatic Classification of Pathological Voice Signals. J Voice 2020; 36:288.e15-288.e24. [PMID: 32660846 DOI: 10.1016/j.jvoice.2020.05.029] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Revised: 05/17/2020] [Accepted: 05/26/2020] [Indexed: 10/23/2022]
Abstract
OBJECTIVES Computer-aided pathological voice detection is efficient for initial screening of pathological voice, and has received high academic and clinical attention. This paper proposes an automatic diagnosis method of pathological voice based on deep neural network (DNN). Other two classification models (support vector machines and random forests) were used to verify the effectiveness of DNN. METHODS In this paper, we extracted 12 Mel frequency cepstral coefficients of each voice sample as row features. The constructed DNN consists a two-layer stacked sparse autoencoders network and a softmax layer. The stacked sparse autoencoders layer can learn high-level features from raw Mel frequency cepstral coefficients features. Then, the softmax layer can diagnose pathological voice according to high-level features. The DNN and the other two comparison models used the same train set and test set for the experiment. RESULTS Experimental results reveal that the value of sensitivity, specificity, precision, accuracy, and F1 score of the DNN can reach 97.8%, 99.4%, 99.4%, 98.6%, and 98.4%, respectively. The five indexes of DNN classification results are at least 6.2%, 5%, 5.6%, 5.7%, and 6.2% higher than the comparison models (support vector machine and random forest). CONCLUSIONS The proposed DNN can learn advanced features from raw acoustic features, and distinguish pathological voice from healthy voice. To the extent of this preliminary study, future studies can further explore the application of DNN in other experiments and clinical practice.
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Affiliation(s)
- Lili Chen
- School of Mechatronics and Vehicle Engineering, Chongqing Jiaotong University, Chongqing, China; Chongqing Survey Institute, Chongqing, China.
| | - Junjiang Chen
- School of Mechatronics and Vehicle Engineering, Chongqing Jiaotong University, Chongqing, China
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20
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Gautam R, Sharma M. Prevalence and Diagnosis of Neurological Disorders Using Different Deep Learning Techniques: A Meta-Analysis. J Med Syst 2020; 44:49. [DOI: 10.1007/s10916-019-1519-7] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Accepted: 12/12/2019] [Indexed: 02/06/2023]
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21
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Li G, Han D, Wang C, Hu W, Calhoun VD, Wang YP. Application of deep canonically correlated sparse autoencoder for the classification of schizophrenia. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 183:105073. [PMID: 31525548 DOI: 10.1016/j.cmpb.2019.105073] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Revised: 08/27/2019] [Accepted: 09/06/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND AND OBJECTIVE Imaging genetics has been widely used to help diagnose and treat mental illness, e.g., schizophrenia, by combining magnetic resonance imaging of the brain and genomic information for comprehensive and systematic analysis. As a result, utilizing the correlation between magnetic resonance imaging of the brain and genomic information is becoming an important challenge. METHODS In this paper, the joint analysis of single nucleotide polymorphisms and functional magnetic resonance imaging is conducted for comprehensive study of schizophrenia. We developed a deep canonically correlated sparse autoencoder to classify schizophrenia patients from healthy controls, which can address the limitation of many existing methods such as canonical correlation analysis, deep canonical correlation analysis and sparse autoencoder. RESULTS The proposed deep canonically correlated sparse autoencoder can not only use complex nonlinear transformation and dimension reduction, but also achieve more accurate classifications. Our experiments showed the proposed method achieved an accuracy of 95.65% for SNP data sets and an accuracy of 80.53% for fMRI data sets. CONCLUSIONS Experiments demonstrated higher accuracy of using the proposed method over other conventional models when classifying schizophrenia patients and healthy controls.
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Affiliation(s)
- Gang Li
- School of Electronic and Control Engineering, Chang'an University, Xi'an 710064, Shaanxi, China; Key Laboratory of Road Construction Technology and Equipment of MOE, Chang'an University, China.
| | - Depeng Han
- School of Electronic and Control Engineering, Chang'an University, Xi'an 710064, Shaanxi, China
| | - Chao Wang
- School of Electronic and Control Engineering, Chang'an University, Xi'an 710064, Shaanxi, China
| | - Wenxing Hu
- Biomedical Engineering Department, Tulane University, New Orleans, LA 70118, USA.
| | - Vince D Calhoun
- Mind Research Network and Department of ECE, University of New Mexico, Albuquerque, NM 87106, USA.
| | - Yu-Ping Wang
- Biomedical Engineering Department, Tulane University, New Orleans, LA 70118, USA.
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22
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Debs N, Rasti P, Victor L, Cho TH, Frindel C, Rousseau D. Simulated perfusion MRI data to boost training of convolutional neural networks for lesion fate prediction in acute stroke. Comput Biol Med 2019; 116:103579. [PMID: 31999557 DOI: 10.1016/j.compbiomed.2019.103579] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Revised: 12/04/2019] [Accepted: 12/08/2019] [Indexed: 11/16/2022]
Abstract
The problem of final tissue outcome prediction of acute ischemic stroke is assessed from physically realistic simulated perfusion magnetic resonance images. Different types of simulations with a focus on the arterial input function are discussed. These simulated perfusion magnetic resonance images are fed to convolutional neural network to predict real patients. Performances close to the state-of-the-art performances are obtained with a patient specific approach. This approach consists in training a model only from simulated images tuned to the arterial input function of a tested real patient. This demonstrates the added value of physically realistic simulated images to predict the final infarct from perfusion.
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Affiliation(s)
- Noëlie Debs
- CREATIS, CNRS UMR-5220, INSERM U1206, Université Lyon 1, INSA Lyon Bât, Blaise Pascal, 7 Avenue Jean Capelle, 69621, Villeurbanne, France
| | - Pejman Rasti
- Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS), UMR INRA IRHS, Université d'Angers, 62 Avenue Notre Dame du Lac, 49000 Angers, France
| | - Léon Victor
- CREATIS, CNRS UMR-5220, INSERM U1206, Université Lyon 1, INSA Lyon Bât, Blaise Pascal, 7 Avenue Jean Capelle, 69621, Villeurbanne, France
| | - Tae-Hee Cho
- CREATIS, CNRS UMR-5220, INSERM U1206, Université Lyon 1, INSA Lyon Bât, Blaise Pascal, 7 Avenue Jean Capelle, 69621, Villeurbanne, France
| | - Carole Frindel
- CREATIS, CNRS UMR-5220, INSERM U1206, Université Lyon 1, INSA Lyon Bât, Blaise Pascal, 7 Avenue Jean Capelle, 69621, Villeurbanne, France
| | - David Rousseau
- Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS), UMR INRA IRHS, Université d'Angers, 62 Avenue Notre Dame du Lac, 49000 Angers, France.
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Vupputuri A, Ashwal S, Tsao B, Ghosh N. Ischemic stroke segmentation in multi-sequence MRI by symmetry determined superpixel based hierarchical clustering. Comput Biol Med 2019; 116:103536. [PMID: 31783255 DOI: 10.1016/j.compbiomed.2019.103536] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Revised: 11/07/2019] [Accepted: 11/07/2019] [Indexed: 02/02/2023]
Abstract
Automated estimation of ischemic stroke evolution across different brain anatomical regions has immense potential to revolutionize stroke treatment. Multi-sequence Magnetic Resonance Imaging (MRI) techniques provide information to characterize abnormal tissues based on their anatomy and physical properties. Asymmetry of the right and left hemispheres of the brain is an important cue for abnormality estimation but using it alone is susceptible to occasional error due to self-asymmetry of the brain. A precise estimate of the symmetry axis is therefore essential for accurate asymmetry identification, which holds the key to the proposed method. The proposed symmetry determined superpixel based hierarchical clustering (SSHC) method initially estimates the lesion from inter-hemispheric asymmetry. This asymmetry further determines the thresholding parameter for hierarchically clustering the superpixels leading to an automated and accurate lesion delineation. A multi-sequence MRI based pipeline also combines the estimations from individual sequences. SSHC is evaluated on different sequences of the Loma Linda University (LLU) dataset with 26 patients and the Ischemic Stroke Lesion Segmentation (ISLES'15) dataset with 28 patients. SSHC eliminates the need for manual determination of threshold for combining the superpixel clusters and is more reliable as it derives the information from the quick estimation of asymmetry. SSHC outperforms the state-of-the-art resulting in a high Dice similarity score of 0.704±0.27 and a recall of 0.85±0.01 which are 6% and 35% respectively higher than the challenge winning method. SSHC thus demonstrates a promising potential in the automated detection of (sub-)acute adult ischemic stroke.
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Affiliation(s)
- Anusha Vupputuri
- Department of Electrical Engineering, Indian Institute of Technology, Kharagpur, 721302, India.
| | - Stephen Ashwal
- Department of Pediatrics, Loma Linda University, Loma Linda, CA, 92354, USA.
| | - Bryan Tsao
- Department of Neurology, Loma Linda University, Loma Linda, CA, 92354, USA.
| | - Nirmalya Ghosh
- Department of Electrical Engineering, Indian Institute of Technology, Kharagpur, 721302, India.
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24
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Sathish R, Rajan R, Vupputuri A, Ghosh N, Sheet D. Adversarially Trained Convolutional Neural Networks for Semantic Segmentation of Ischaemic Stroke Lesion using Multisequence Magnetic Resonance Imaging. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2019:1010-1013. [PMID: 31946064 DOI: 10.1109/embc.2019.8857527] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Ischaemic stroke is a medical condition caused by occlusion of blood supply to the brain tissue thus forming a lesion. A lesion is zoned into a core associated with irreversible necrosis typically located at the center of the lesion, while reversible hypoxic changes in the outer regions of the lesion are termed as the penumbra. Early estimation of core and penumbra in ischaemic stroke is crucial for timely intervention with thrombolytic therapy to reverse the damage and restore normalcy. Multisequence magnetic resonance imaging (MRI) is commonly employed for clinical diagnosis. However, a sequence singly has not been found to be sufficiently able to differentiate between core and penumbra, while a combination of sequences is required to determine the extent of the damage. The challenge, however, is that with an increase in the number of sequences, it cognitively taxes the clinician to discover symptomatic biomarkers in these images. In this paper, we present a data-driven fully automated method for estimation of core and penumbra in ischaemic lesions using diffusion-weighted imaging (DWI) and perfusion-weighted imaging (PWI) sequence maps of MRI. The method employs recent developments in convolutional neural networks (CNN) for semantic segmentation in medical images. In the absence of availability of a large amount of labeled data, the CNN is trained using an adversarial approach employing cross-entropy as a segmentation loss along with losses aggregated from three discriminators of which two employ relativistic visual Turing test. This method is experimentally validated on the ISLES-2015 dataset through three-fold cross-validation to obtain with an average Dice score of 0.82 and 0.73 for segmentation of penumbra and core respectively.
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25
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Melingi SB, Vijayalakshmi V. Automatic segmentation of sub-acute ischemic stroke lesion by using DTCWT and DBN with parameter fine tuning. EVOLUTIONARY INTELLIGENCE 2019. [DOI: 10.1007/s12065-019-00240-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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26
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Johnson PM, Drangova M. Conditional generative adversarial network for 3D rigid-body motion correction in MRI. Magn Reson Med 2019; 82:901-910. [PMID: 31006909 DOI: 10.1002/mrm.27772] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Revised: 03/09/2019] [Accepted: 03/22/2019] [Indexed: 11/10/2022]
Abstract
PURPOSE Subject motion in MRI remains an unsolved problem; motion during image acquisition may cause blurring and artifacts that severely degrade image quality. In this work, we approach motion correction as an image-to-image translation problem, which refers to the approach of training a deep neural network to predict an image in 1 domain from an image in another domain. Specifically, the purpose of this work was to develop and train a conditional generative adversarial network to predict artifact-free brain images from motion-corrupted data. METHODS An open source MRI data set comprising T2 *-weighted, FLASH magnitude, and phase brain images for 53 patients was used to generate complex image data for motion simulation. To simulate rigid motion, rotations and translations were applied to the image data based on randomly generated motion profiles. A conditional generative adversarial network, comprising a generator and discriminator networks, was trained using the motion-corrupted and corresponding ground truth (original) images as training pairs. RESULTS The images predicted by the conditional generative adversarial network have improved image quality compared to the motion-corrupted images. The mean absolute error between the motion-corrupted and ground-truth images of the test set was 16.4% of the image mean value, whereas the mean absolute error between the conditional generative adversarial network-predicted and ground-truth images was 10.8% The network output also demonstrated improved peak SNR and structural similarity index for all test-set images. CONCLUSION The images predicted by the conditional generative adversarial network have quantitatively and qualitatively improved image quality compared to the motion-corrupted images.
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Affiliation(s)
- Patricia M Johnson
- Imaging Research Laboratories, Robarts Research Institute, The University of Western Ontario, London, Ontario, Canada.,Department of Medical Biophysics, Schulich School of Medicine & Dentistry, The University of Western Ontario, London, Ontario, Canada
| | - Maria Drangova
- Imaging Research Laboratories, Robarts Research Institute, The University of Western Ontario, London, Ontario, Canada.,Department of Medical Biophysics, Schulich School of Medicine & Dentistry, The University of Western Ontario, London, Ontario, Canada
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Volumetric Adversarial Training for Ischemic Stroke Lesion Segmentation. BRAINLESION: GLIOMA, MULTIPLE SCLEROSIS, STROKE AND TRAUMATIC BRAIN INJURIES 2019. [DOI: 10.1007/978-3-030-11723-8_35] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
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28
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Dolz J, Ben Ayed I, Desrosiers C. Dense Multi-path U-Net for Ischemic Stroke Lesion Segmentation in Multiple Image Modalities. BRAINLESION: GLIOMA, MULTIPLE SCLEROSIS, STROKE AND TRAUMATIC BRAIN INJURIES 2019. [DOI: 10.1007/978-3-030-11723-8_27] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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29
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Praveen G, Agrawal A, Pareek S, Prince A. Brain abnormality detection using template matching. BIO-ALGORITHMS AND MED-SYSTEMS 2018. [DOI: 10.1515/bams-2018-0029] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract
Magnetic resonance imaging (MRI) is a widely used imaging modality to evaluate brain disorders. MRI generates huge volumes of data, which consist of a sequence of scans taken at different instances of time. As the presence of brain disorders has to be evaluated on all magnetic resonance (MR) sequences, manual brain disorder detection becomes a tedious process and is prone to inter- and intra-rater errors. A technique for detecting abnormalities in brain MRI using template matching is proposed. Bias filed correction is performed on volumetric scans using N4ITK filter, followed by volumetric registration. Normalized cross-correlation template matching is used for image registration taking into account, the rotation and scaling operations. A template of abnormality is selected which is then matched in the volumetric scans, if found, the corresponding image is retrieved. Post-processing of the retrieved images is performed by the thresholding operation; the coordinates and area of the abnormality are reported. The experiments are carried out on the glioma dataset obtained from Brain Tumor Segmentation Challenge 2013 database (BRATS 2013). Glioma dataset consisted of MR scans of 30 real glioma patients and 50 simulated glioma patients. NVIDIA Compute Unified Device Architecture framework is employed in this paper, and it is found that the detection speed using graphics processing unit is almost four times faster than using only central processing unit. The average Dice and Jaccard coefficients for a wide range of trials are found to be 0.91 and 0.83, respectively.
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Affiliation(s)
- G.B. Praveen
- Department of Electrical and Electronics Engineering , BITS Pilani , K.K. Birla Goa Campus , Goa , India
| | - Anita Agrawal
- Department of Electrical and Electronics Engineering , BITS Pilani , K.K. Birla Goa Campus , Goa , India
| | - Shrey Pareek
- Department of Electrical and Electronics Engineering , BITS Pilani , K.K. Birla Goa Campus , Goa , India
| | - Amalin Prince
- Department of Electrical and Electronics Engineering , BITS Pilani , K.K. Birla Goa Campus , Goa , India
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