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Won SY, Kim JH, Woo C, Kim DH, Park KY, Kim EY, Baek SY, Han HJ, Sohn B. Real-world application of a 3D deep learning model for detecting and localizing cerebral microbleeds. Acta Neurochir (Wien) 2024; 166:381. [PMID: 39325068 DOI: 10.1007/s00701-024-06267-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2024] [Accepted: 09/08/2024] [Indexed: 09/27/2024]
Abstract
BACKGROUND Detection and localization of cerebral microbleeds (CMBs) is crucial for disease diagnosis and treatment planning. However, CMB detection is labor-intensive, time-consuming, and challenging owing to its visual similarity to mimics. This study aimed to validate the performance of a three-dimensional (3D) deep learning model that not only detects CMBs but also identifies their anatomic location in real-world settings. METHODS A total of 21 patients with 116 CMBs and 12 without CMBs were visited in the neurosurgery outpatient department between January 2023 and October 2023. Three readers, including a board-certified neuroradiologist (reader 1), a resident in radiology (reader 2), and a neurosurgeon (reader 3) independently reviewed SWIs of 33 patients to detect CMBs and categorized their locations into lobar, deep, and infratentorial regions without any AI assistance. After a one-month washout period, the same datasets were redistributed randomly, and readers reviewed them again with the assistance of the 3D deep learning model. A comparison of the diagnostic performance between readers with and without AI assistance was performed. RESULTS All readers with an AI assistant (reader 1:0.991 [0.930-0.999], reader 2:0.922 [0.881-0.905], and reader 3:0.966 [0.928-0.984]) tended to have higher sensitivity per lesion than readers only (reader 1:0.905 [0.849-0.942], reader 2:0.621 [0.541-0.694], and reader 3:0.871 [0.759-0.935], p = 0.132, 0.017, and 0.227, respectively). In particular, radiology residents (reader 2) showed a statistically significant increase in sensitivity per lesion when using AI. There was no statistically significant difference in the number of FPs per patient for all readers with AI assistant (reader 1: 0.394 [0.152-1.021], reader 2: 0.727 [0.334-1.582], reader 3: 0.182 [0.077-0.429]) and reader only (reader 1: 0.364 [0.159-0.831], reader 2: 0.576 [0.240-1.382], reader 3: 0.121 [0.038-0.383], p = 0.853, 0.251, and 0.157, respectively). Our model accurately categorized the anatomical location of all CMBs. CONCLUSIONS Our model demonstrated promising potential for the detection and anatomical localization of CMBs, although further research with a larger and more diverse population is necessary to establish clinical utility in real-world settings.
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Affiliation(s)
- So Yeon Won
- Department of Radiology and Center for Imaging Sciences, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Department of Radiology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Jun-Ho Kim
- Department of Electrical and Electronic Engineering, College of Engineering, Yonsei University, Seoul, Republic of Korea
| | - Changsoo Woo
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Dong-Hyun Kim
- Department of Electrical and Electronic Engineering, College of Engineering, Yonsei University, Seoul, Republic of Korea
| | - Keun Young Park
- Department of Neurosurgery, Severance Stroke Center, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Eung Yeop Kim
- Department of Radiology and Center for Imaging Sciences, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Sun-Young Baek
- Department of Radiology and Center for Imaging Sciences, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Research Institute for Future Medicine, Samsung Medical Center, Seoul, Republic of Korea
| | - Hyun Jin Han
- Department of Neurosurgery, Severance Stroke Center, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea.
| | - Beomseok Sohn
- Department of Radiology and Center for Imaging Sciences, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
- Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science, Yonsei University College of Medicine, Seoul, Republic of Korea.
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Xia P, Hui ES, Chua BJ, Huang F, Wang Z, Zhang H, Yu H, Lau KK, Mak HKF, Cao P. Deep-Learning-Based MRI Microbleeds Detection for Cerebral Small Vessel Disease on Quantitative Susceptibility Mapping. J Magn Reson Imaging 2024; 60:1165-1175. [PMID: 38149750 DOI: 10.1002/jmri.29198] [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: 09/17/2023] [Revised: 12/04/2023] [Accepted: 12/05/2023] [Indexed: 12/28/2023] Open
Abstract
BACKGROUND Cerebral microbleeds (CMB) are indicators of severe cerebral small vessel disease (CSVD) that can be identified through hemosiderin-sensitive sequences in MRI. Specifically, quantitative susceptibility mapping (QSM) and deep learning were applied to detect CMBs in MRI. PURPOSE To automatically detect CMB on QSM, we proposed a two-stage deep learning pipeline. STUDY TYPE Retrospective. SUBJECTS A total number of 1843 CMBs from 393 patients (69 ± 12) with cerebral small vessel disease were included in this study. Seventy-eight subjects (70 ± 13) were used as external testing. FIELD STRENGTH/SEQUENCE 3 T/QSM. ASSESSMENT The proposed pipeline consisted of two stages. In stage I, 2.5D fast radial symmetry transform (FRST) algorithm along with a one-layer convolutional network was used to identify CMB candidate regions in QSM images. In stage II, the V-Net was utilized to reduce false positives. The V-Net was trained using CMB and non CMB labels, which allowed for high-level feature extraction and differentiation between CMBs and CMB mimics like vessels. The location of CMB was assessed according to the microbleeds anatomical rating scale (MARS) system. STATISTICAL TESTS The sensitivity and positive predicative value (PPV) were reported to evaluate the performance of the model. The number of false positive per subject was presented. RESULTS Our pipeline demonstrated high sensitivities of up to 94.9% at stage I and 93.5% at stage II. The overall sensitivity was 88.9%, and the false positive rate per subject was 2.87. With respect to MARS, sensitivities of above 85% were observed for nine different brain regions. DATA CONCLUSION We have presented a deep learning pipeline for detecting CMB in the CSVD cohort, along with a semi-automated MARS scoring system using the proposed method. Our results demonstrated the successful application of deep learning for CMB detection on QSM and outperformed previous handcrafted methods. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Peng Xia
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, China
| | - Edward S Hui
- Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, China
| | - Bryan J Chua
- Division of Neurology, Department of Medicine, The University of Hong Kong, Hong Kong, China
| | - Fan Huang
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, China
| | - Zuojun Wang
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, China
| | - Huiqin Zhang
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, China
| | - Han Yu
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, China
| | - Kui Kai Lau
- Division of Neurology, Department of Medicine, The University of Hong Kong, Hong Kong, China
- The State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, China
| | - Henry K F Mak
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, China
| | - Peng Cao
- Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, China
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Noroozi M, Gholami M, Sadeghsalehi H, Behzadi S, Habibzadeh A, Erabi G, Sadatmadani SF, Diyanati M, Rezaee A, Dianati M, Rasoulian P, Khani Siyah Rood Y, Ilati F, Hadavi SM, Arbab Mojeni F, Roostaie M, Deravi N. Machine and deep learning algorithms for classifying different types of dementia: A literature review. APPLIED NEUROPSYCHOLOGY. ADULT 2024:1-15. [PMID: 39087520 DOI: 10.1080/23279095.2024.2382823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/02/2024]
Abstract
The cognitive impairment known as dementia affects millions of individuals throughout the globe. The use of machine learning (ML) and deep learning (DL) algorithms has shown great promise as a means of early identification and treatment of dementia. Dementias such as Alzheimer's Dementia, frontotemporal dementia, Lewy body dementia, and vascular dementia are all discussed in this article, along with a literature review on using ML algorithms in their diagnosis. Different ML algorithms, such as support vector machines, artificial neural networks, decision trees, and random forests, are compared and contrasted, along with their benefits and drawbacks. As discussed in this article, accurate ML models may be achieved by carefully considering feature selection and data preparation. We also discuss how ML algorithms can predict disease progression and patient responses to therapy. However, overreliance on ML and DL technologies should be avoided without further proof. It's important to note that these technologies are meant to assist in diagnosis but should not be used as the sole criteria for a final diagnosis. The research implies that ML algorithms may help increase the precision with which dementia is diagnosed, especially in its early stages. The efficacy of ML and DL algorithms in clinical contexts must be verified, and ethical issues around the use of personal data must be addressed, but this requires more study.
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Affiliation(s)
- Masoud Noroozi
- Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran
| | - Mohammadreza Gholami
- Department of Electrical and Computer Engineering, Tarbiat Modares Univeristy, Tehran, Iran
| | - Hamidreza Sadeghsalehi
- Department of Artificial Intelligence in Medical Sciences, Iran University of Medical Sciences, Tehran, Iran
| | - Saleh Behzadi
- Student Research Committee, Rafsanjan University of Medical Sciences, Rafsanjan, Iran
| | - Adrina Habibzadeh
- Student Research Committee, Fasa University of Medical Sciences, Fasa, Iran
- USERN Office, Fasa University of Medical Sciences, Fasa, Iran
| | - Gisou Erabi
- Student Research Committee, Urmia University of Medical Sciences, Urmia, Iran
| | | | - Mitra Diyanati
- Paul M. Rady Department of Mechanical Engineering, University of Colorado Boulder, Boulder, CO, USA
| | - Aryan Rezaee
- Student Research Committee, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Maryam Dianati
- Student Research Committee, Rafsanjan University of Medical Sciences, Rafsanjan, Iran
| | - Pegah Rasoulian
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Yashar Khani Siyah Rood
- Faculty of Engineering, Computer Engineering, Islamic Azad University of Bandar Abbas, Bandar Abbas, Iran
| | - Fatemeh Ilati
- Student Research Committee, Faculty of Medicine, Islamic Azad University of Mashhad, Mashhad, Iran
| | | | - Fariba Arbab Mojeni
- Student Research Committee, School of Medicine, Mazandaran University of Medical Sciences, Sari, Iran
| | - Minoo Roostaie
- School of Medicine, Islamic Azad University Tehran Medical Branch, Tehran, Iran
| | - Niloofar Deravi
- Student Research Committee, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Uparela-Reyes MJ, Villegas-Trujillo LM, Cespedes J, Velásquez-Vera M, Rubiano AM. Usefulness of Artificial Intelligence in Traumatic Brain Injury: A Bibliometric Analysis and Mini-review. World Neurosurg 2024; 188:83-92. [PMID: 38759786 DOI: 10.1016/j.wneu.2024.05.065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Accepted: 05/10/2024] [Indexed: 05/19/2024]
Abstract
BACKGROUND Traumatic brain injury (TBI) has become a major source of disability worldwide, increasing the interest in algorithms that use artificial intelligence (AI) to optimize the interpretation of imaging studies, prognosis estimation, and critical care issues. In this study we present a bibliometric analysis and mini-review on the main uses that have been developed for TBI in AI. METHODS The results informing this review come from a Scopus database search as of April 15, 2023. The bibliometric analysis was carried out via the mapping bibliographic metrics method. Knowledge mapping was made in the VOSviewer software (V1.6.18), analyzing the "link strength" of networks based on co-occurrence of key words, countries co-authorship, and co-cited authors. In the mini-review section, we highlight the main findings and contributions of the studies. RESULTS A total of 495 scientific publications were identified from 2000 to 2023, with 9262 citations published since 2013. Among the 160 journals identified, The Journal of Neurotrauma, Frontiers in Neurology, and PLOS ONE were those with the greatest number of publications. The most frequently co-occurring key words were: "machine learning", "deep learning", "magnetic resonance imaging", and "intracranial pressure". The United States accounted for more collaborations than any other country, followed by United Kingdom and China. Four co-citation author clusters were found, and the top 20 papers were divided into reviews and original articles. CONCLUSIONS AI has become a relevant research field in TBI during the last 20 years, demonstrating great potential in imaging, but a more modest performance for prognostic estimation and neuromonitoring.
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Affiliation(s)
- Maria José Uparela-Reyes
- Neurosurgery Section, School of Medicine, Universidad del Valle, Cali, Colombia; Neurosurgery Section, Hospital Universitario del Valle, Cali, Colombia.
| | - Lina María Villegas-Trujillo
- Neurosurgery Section, School of Medicine, Universidad del Valle, Cali, Colombia; School of Biomedical Sciences, Universidad del Valle, Cali, Colombia
| | - Jorge Cespedes
- Comprehensive Epilepsy Center, Yale University, New Haven, Connecticut, USA
| | - Miguel Velásquez-Vera
- Neurosurgery Section, School of Medicine, Universidad del Valle, Cali, Colombia; Neurosurgery Section, Hospital Universitario del Valle, Cali, Colombia
| | - Andrés M Rubiano
- Neurosurgery Section, School of Medicine, Universidad del Valle, Cali, Colombia; Neurosurgery Section, Hospital Universitario del Valle, Cali, Colombia; INUB-Meditech Research Group, Neurosciences Institute, Universidad El Bosque, Bogotá, Colombia
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Nishioka N, Shimizu Y, Shirai T, Ochi H, Bito Y, Watanabe K, Kameda H, Harada T, Kudo K. Automated Detection of Cerebral Microbleeds on Two-dimensional Gradient-recalled Echo T2* Weighted Images Using a Morphology Filter Bank and Convolutional Neural Network. Magn Reson Med Sci 2024:mp.2023-0146. [PMID: 38494702 DOI: 10.2463/mrms.mp.2023-0146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2024] Open
Abstract
PURPOSE We present a novel algorithm for the automated detection of cerebral microbleeds (CMBs) on 2D gradient-recalled echo T2* weighted images (T2*WIs). This approach combines a morphology filter bank with a convolutional neural network (CNN) to improve the efficiency of CMB detection. A technical evaluation was performed to ascertain the algorithm's accuracy. METHODS In this retrospective study, 60 patients with CMBs on T2*WIs were included. The gold standard was set by three neuroradiologists based on the Microbleed Anatomic Rating Scale guidelines. Images with CMBs were extracted from the training dataset comprising 30 cases using a morphology filter bank, and false positives (FPs) were removed based on the threshold of size and signal intensity. The extracted images were used to train the CNN (Vgg16). To determine the effectiveness of the morphology filter bank, the outcomes of the following two methods for detecting CMBs from the 30-case test dataset were compared: (a) employing the morphology filter bank and additional FP removal and (b) comprehensive detection without filters. The trained CNN processed both sets of initial CMB candidates, and the final CMB candidates were compared with the gold standard. The sensitivity and FPs per patient of both methods were compared. RESULTS After CNN processing, the morphology-filter-bank-based method had a 95.0% sensitivity with 4.37 FPs per patient. In contrast, the comprehensive method had a 97.5% sensitivity with 25.87 FPs per patient. CONCLUSION Through effective CMB candidate refinement with a morphology filter bank and FP removal with a CNN, we achieved a high CMB detection rate and low FP count. Combining a CNN and morphology filter bank may facilitate the accurate automated detection of CMBs on T2*WIs.
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Affiliation(s)
- Noriko Nishioka
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Hokkaido, Japan
- Department of Diagnostic Imaging, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan
| | - Yukie Shimizu
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Hokkaido, Japan
- Department of Diagnostic Imaging, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan
| | - Toru Shirai
- Medical Systems Research & Development Center, FUJIFILM Corporation, Tokyo, Japan
| | - Hisaaki Ochi
- Medical Systems Research & Development Center, FUJIFILM Corporation, Tokyo, Japan
| | | | - Kiichi Watanabe
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Hokkaido, Japan
| | - Hiroyuki Kameda
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Hokkaido, Japan
- Faculty of Dental Medicine, Department of Radiology, Hokkaido University, Sapporo, Hokkaido, Japan
| | - Taisuke Harada
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Hokkaido, Japan
- Department of Diagnostic Imaging, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan
| | - Kohsuke Kudo
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Hokkaido, Japan
- Department of Diagnostic Imaging, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan
- Division of Medical AI Education and Research, Hokkaido University Graduate School of Medicine, Sapporo, Hokkaido, Japan
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Jaafari O, Salih S, Alkatheeri A, Alshehri M, Al-Shammari M, Maeni M, Alqahtani A, Alomaim W, Hasaneen M. Appropriate incorporation of susceptibility-weighted magnetic resonance imaging into routine imaging protocols for accurate diagnosis of traumatic brain injuries: a systematic review. J Med Life 2024; 17:273-280. [PMID: 39044937 PMCID: PMC11262612 DOI: 10.25122/jml-2023-0487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 02/12/2024] [Indexed: 07/25/2024] Open
Abstract
Traumatic brain injury (TBI) results from physical or traumatic injuries to the brain's surrounding bony structures and associated tissues, which can lead to various sequelae, including simple concussion, acute epidural hematoma, parenchymal contusions, subarachnoid hemorrhage, diffuse axonal injury, and chronic traumatic encephalopathy. Susceptibility-weighted imaging (SWI) has enhanced the accuracy of neuroimaging for these injuries. SWI is based on 3D gradient echo magnetic resonance imaging (MRI) with long echo times and flow compensation. Owing to its sensitivity to deoxyhemoglobin, hemosiderin, iron, and calcium, SWI is extremely informative and superior to conventional MRI for the diagnosis and follow-up of patients with acute, subacute, and prolonged hemorrhage. This systematic review aimed to evaluate and summarize the published articles that report SWI results for the evaluation of TBI and to determine correlations between clinical status and SWI results. Consequently, our analysis also aimed to identify the appropriate MRI sequences to use in the assessment of patients with TBI. We searched the Medline and Embase online electronic databases for relevant papers published from 2012 onwards. We found that SWI had higher sensitivity than gradient echo MRI in detecting and characterizing microbleeds in TBIs and was able to differentiate diamagnetic calcifications from paramagnetic microhemorrhages. However, it is important that future research not only continues to evaluate the utility of SWI in TBIs but also attempts to overcome the limitations of the studies described in this review, which should help validate the conclusions and recommendations from our analysis.
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Affiliation(s)
- Osama Jaafari
- Radiology Department, Royal Commission Medical Center, King Fahad, Al-Nakheel, Yanbu, Saudi Arabia
| | - Suliman Salih
- Department of Radiography and Medical Imaging, Fatima College of Health Sciences, Al Ain, United Arab Emirates
| | - Ajnas Alkatheeri
- Department of Radiography and Medical Imaging, Fatima College of Health Sciences, Al Ain, United Arab Emirates
| | - Muhamed Alshehri
- Department of Radiology and Medical Imaging, Prince Sultan Military Medical City, Riyadh, Saudi Arabia
| | - Majedh Al-Shammari
- Department of Radiological Sciences, College of Applied Medical Sciences, Najran University, Najran, Saudi Arabia
| | - Mousa Maeni
- Radiology Department, Royal Commission Medical Center, King Fahad, Al-Nakheel, Yanbu, Saudi Arabia
| | - Abdullah Alqahtani
- Radiology Department, Royal Commission Medical Center, King Fahad, Al-Nakheel, Yanbu, Saudi Arabia
| | - Wijdan Alomaim
- Department of Radiography and Medical Imaging, Fatima College of Health Sciences, Al Ain, United Arab Emirates
| | - Mohamed Hasaneen
- Department of Radiography and Medical Imaging, Fatima College of Health Sciences, Al Ain, United Arab Emirates
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Dong C, Hayashi S. Deep learning applications in vascular dementia using neuroimaging. Curr Opin Psychiatry 2024; 37:101-106. [PMID: 38226547 DOI: 10.1097/yco.0000000000000920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/17/2024]
Abstract
PURPOSE OF REVIEW Vascular dementia (VaD) is the second common cause of dementia after Alzheimer's disease, and deep learning has emerged as a critical tool in dementia research. The aim of this article is to highlight the current deep learning applications in VaD-related imaging biomarkers and diagnosis. RECENT FINDINGS The main deep learning technology applied in VaD using neuroimaging data is convolutional neural networks (CNN). CNN models have been widely used for lesion detection and segmentation, such as white matter hyperintensities (WMH), cerebral microbleeds (CMBs), perivascular spaces (PVS), lacunes, cortical superficial siderosis, and brain atrophy. Applications in VaD subtypes classification also showed excellent results. CNN-based deep learning models have potential for further diagnosis and prognosis of VaD. SUMMARY Deep learning neural networks with neuroimaging data in VaD research represent significant promise for advancing early diagnosis and treatment strategies. Ongoing research and collaboration between clinicians, data scientists, and neuroimaging experts are essential to address challenges and unlock the full potential of deep learning in VaD diagnosis and management.
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Affiliation(s)
- Chao Dong
- Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry & Mental Health, School of Clinical Medicine, UNSW Sydney, NSW, Australia
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Wheeler KV, Irimia A, Braskie MN. Using Neuroimaging to Study Cerebral Amyloid Angiopathy and Its Relationship to Alzheimer's Disease. J Alzheimers Dis 2024; 97:1479-1502. [PMID: 38306032 DOI: 10.3233/jad-230553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Abstract
Cerebral amyloid angiopathy (CAA) is characterized by amyloid-β aggregation in the media and adventitia of the leptomeningeal and cortical blood vessels. CAA is one of the strongest vascular contributors to Alzheimer's disease (AD). It frequently co-occurs in AD patients, but the relationship between CAA and AD is incompletely understood. CAA may drive AD risk through damage to the neurovascular unit and accelerate parenchymal amyloid and tau deposition. Conversely, early AD may also drive CAA through cerebrovascular remodeling that impairs blood vessels from clearing amyloid-β. Sole reliance on autopsy examination to study CAA limits researchers' ability to investigate CAA's natural disease course and the effect of CAA on cognitive decline. Neuroimaging allows for in vivo assessment of brain function and structure and can be leveraged to investigate CAA staging and explore its associations with AD. In this review, we will discuss neuroimaging modalities that can be used to investigate markers associated with CAA that may impact AD vulnerability including hemorrhages and microbleeds, blood-brain barrier permeability disruption, reduced cerebral blood flow, amyloid and tau accumulation, white matter tract disruption, reduced cerebrovascular reactivity, and lowered brain glucose metabolism. We present possible areas for research inquiry to advance biomarker discovery and improve diagnostics.
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Affiliation(s)
- Koral V Wheeler
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina Del Rey, CA, USA
| | - Andrei Irimia
- Ethel Percy Andrus Gerontology Center, USC Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
- Department of Biomedical Engineering, Corwin D. Denney Research Center, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA
| | - Meredith N Braskie
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina Del Rey, CA, USA
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Ali Z, Naz S, Yasmin S, Bukhari M, Kim M. Deep learning-assisted IoMT framework for cerebral microbleed detection. Heliyon 2023; 9:e22879. [PMID: 38125517 PMCID: PMC10731074 DOI: 10.1016/j.heliyon.2023.e22879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 11/17/2023] [Accepted: 11/22/2023] [Indexed: 12/23/2023] Open
Abstract
The Internet of Things (IoT), big data, and artificial intelligence (AI) are all key technologies that influence the formation and implementation of digital medical services. Building Internet of Medical Things (IoMT) systems that combine advanced sensors with AI-powered insights is critical for intelligent medical systems. This paper presents an IoMT framework for brain magnetic resonance imaging (MRI) analysis to lessen the unavoidable diagnosis and therapy faults that occur in human clinical settings for the accurate detection of cerebral microbleeds (CMBs). The problems in accurate CMB detection include that CMBs are tiny dots 5-10 mm in diameter; they are similar to healthy tissues and are exceedingly difficult to identify, necessitating specialist guidance in remote and underdeveloped medical centers. Secondly, in the existing studies, computer-aided diagnostic (CAD) systems are designed for accurate CMB detection, however, their proposed approaches consist of two stages. Potential candidate CMBs from the complete MRI image are selected in the first stage and then passed to the phase of false-positive reduction. These pre-and post-processing steps make it difficult to build a completely automated CAD system for CMB that can produce results without human intervention. Hence, as a key goal of this work, an end-to-end enhanced UNet-based model for effective CMB detection and segmentation for IoMT devices is proposed. The proposed system requires no pre-processing or post-processing steps for CMB segmentation, and no existing research localizes each CMB pixel from the complete MRI image input. The findings indicate that the suggested method outperforms in detecting CMBs in the presence of contrast variations and similarities with other normal tissues and yields a good dice score of 0.70, an accuracy of 99 %, as well as a false-positive rate of 0.002 %. © 2017 Elsevier Inc. All rights reserved.
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Affiliation(s)
- Zeeshan Ali
- Research and Development Setups, National University of Computer and Emerging Sciences, Islamabad, 44000, Pakistan
| | - Sheneela Naz
- Department of Computer Science, COMSATS University Islamabad, Islamabad, 45550, Pakistan
| | - Sadaf Yasmin
- Department of Computer Science, COMSATS University Islamabad, Attock Campus, Attock, 43600, Pakistan
| | - Maryam Bukhari
- Department of Computer Science, COMSATS University Islamabad, Attock Campus, Attock, 43600, Pakistan
| | - Mucheol Kim
- School of Computer Science and Engineering, Chung-Ang University, Seoul, 06974, South Korea
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Wu R, Liu H, Li H, Chen L, Wei L, Huang X, Liu X, Men X, Li X, Han L, Lu Z, Qin B. Deep learning based on susceptibility-weighted MR sequence for detecting cerebral microbleeds and classifying cerebral small vessel disease. Biomed Eng Online 2023; 22:99. [PMID: 37848906 PMCID: PMC10580591 DOI: 10.1186/s12938-023-01164-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 10/10/2023] [Indexed: 10/19/2023] Open
Abstract
BACKGROUND Cerebral microbleeds (CMBs) serve as neuroimaging biomarkers to assess risk of intracerebral hemorrhage and diagnose cerebral small vessel disease (CSVD). Therefore, detecting CMBs can evaluate the risk of intracerebral hemorrhage and use its presence to support CSVD classification, both are conducive to optimizing CSVD management. This study aimed to develop and test a deep learning (DL) model based on susceptibility-weighted MR sequence (SWS) to detect CMBs and classify CSVD to assist neurologists in optimizing CSVD management. Patients with arteriolosclerosis (aSVD), cerebral amyloid angiopathy (CAA), and cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) treated at three centers were enrolled between January 2017 and May 2022 in this retrospective study. The SWSs of patients from two centers were used as the development set, and the SWSs of patients from the remaining center were used as the external test set. The DL model contains a Mask R-CNN for detecting CMBs and a multi-instance learning (MIL) network for classifying CSVD. The metrics for model performance included intersection over union (IoU), Dice score, recall, confusion matrices, receiver operating characteristic curve (ROC) analysis, accuracy, precision, and F1-score. RESULTS A total of 364 SWS were recruited, including 336 in the development set and 28 in the external test set. IoU for the model was 0.523 ± 0.319, Dice score 0.627 ± 0.296, and recall 0.706 ± 0.365 for CMBs detection in the external test set. For CSVD classification, the model achieved a weighted-average AUC of 0.908 (95% CI 0.895-0.921), accuracy of 0.819 (95% CI 0.768-0.870), weighted-average precision of 0.864 (95% CI 0.831-0.897), and weighted-average F1-score of 0.829 (95% CI 0.782-0.876) in the external set, outperforming the performance of the neurologist group. CONCLUSION The DL model based on SWS can detect CMBs and classify CSVD, thereby assisting neurologists in optimizing CSVD management.
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Affiliation(s)
- Ruizhen Wu
- Department of Neurology, The Third Affiliated Hospital of Sun Yat-Sen University, Sun Yat-Sen University, No. 600 Tianhe Road, Guangzhou, 510630, People's Republic of China
| | - Huaqing Liu
- Center for Artificial Intelligence in Medicine, Research Institute of Tsinghua, Pearl River Delta, Tsinghua University, No. 98 Xiangxue 8Th Road, Guangzhou, 510700, People's Republic of China
| | - Hao Li
- Department of Neurology, Maoming People's Hospital, No.101 Weimin Road, Maoming, 525000, People's Republic of China
| | - Lifen Chen
- Department of Neurology, the First Affiliated Hospital of SHANTOU University Medical College, Shantou University, No. 57 of Changping Road, Shantou, 515041, People's Republic of China
| | - Lei Wei
- Department of Neurology, The Third Affiliated Hospital of Sun Yat-Sen University, Sun Yat-Sen University, No. 600 Tianhe Road, Guangzhou, 510630, People's Republic of China
| | - Xuehong Huang
- Department of Neurology, The Third Affiliated Hospital of Sun Yat-Sen University, Sun Yat-Sen University, No. 600 Tianhe Road, Guangzhou, 510630, People's Republic of China
| | - Xu Liu
- Department of Neurology, The Third Affiliated Hospital of Sun Yat-Sen University, Sun Yat-Sen University, No. 600 Tianhe Road, Guangzhou, 510630, People's Republic of China
| | - Xuejiao Men
- Department of Neurology, The Third Affiliated Hospital of Sun Yat-Sen University, Sun Yat-Sen University, No. 600 Tianhe Road, Guangzhou, 510630, People's Republic of China
| | - Xidan Li
- Center for Artificial Intelligence in Medicine, Research Institute of Tsinghua, Pearl River Delta, Tsinghua University, No. 98 Xiangxue 8Th Road, Guangzhou, 510700, People's Republic of China
| | - Lanqing Han
- Center for Artificial Intelligence in Medicine, Research Institute of Tsinghua, Pearl River Delta, Tsinghua University, No. 98 Xiangxue 8Th Road, Guangzhou, 510700, People's Republic of China
| | - Zhengqi Lu
- Department of Neurology, The Third Affiliated Hospital of Sun Yat-Sen University, Sun Yat-Sen University, No. 600 Tianhe Road, Guangzhou, 510630, People's Republic of China
| | - Bing Qin
- Department of Neurology, The Third Affiliated Hospital of Sun Yat-Sen University, Sun Yat-Sen University, No. 600 Tianhe Road, Guangzhou, 510630, People's Republic of China.
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11
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Sundaresan V, Arthofer C, Zamboni G, Murchison AG, Dineen RA, Rothwell PM, Auer DP, Wang C, Miller KL, Tendler BC, Alfaro-Almagro F, Sotiropoulos SN, Sprigg N, Griffanti L, Jenkinson M. Automated detection of cerebral microbleeds on MR images using knowledge distillation framework. Front Neuroinform 2023; 17:1204186. [PMID: 37492242 PMCID: PMC10363739 DOI: 10.3389/fninf.2023.1204186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 06/19/2023] [Indexed: 07/27/2023] Open
Abstract
Introduction Cerebral microbleeds (CMBs) are associated with white matter damage, and various neurodegenerative and cerebrovascular diseases. CMBs occur as small, circular hypointense lesions on T2*-weighted gradient recalled echo (GRE) and susceptibility-weighted imaging (SWI) images, and hyperintense on quantitative susceptibility mapping (QSM) images due to their paramagnetic nature. Accurate automated detection of CMBs would help to determine quantitative imaging biomarkers (e.g., CMB count) on large datasets. In this work, we propose a fully automated, deep learning-based, 3-step algorithm, using structural and anatomical properties of CMBs from any single input image modality (e.g., GRE/SWI/QSM) for their accurate detections. Methods In our method, the first step consists of an initial candidate detection step that detects CMBs with high sensitivity. In the second step, candidate discrimination step is performed using a knowledge distillation framework, with a multi-tasking teacher network that guides the student network to classify CMB and non-CMB instances in an offline manner. Finally, a morphological clean-up step further reduces false positives using anatomical constraints. We used four datasets consisting of different modalities specified above, acquired using various protocols and with a variety of pathological and demographic characteristics. Results On cross-validation within datasets, our method achieved a cluster-wise true positive rate (TPR) of over 90% with an average of <2 false positives per subject. The knowledge distillation framework improves the cluster-wise TPR of the student model by 15%. Our method is flexible in terms of the input modality and provides comparable cluster-wise TPR and better cluster-wise precision compared to existing state-of-the-art methods. When evaluating across different datasets, our method showed good generalizability with a cluster-wise TPR >80 % with different modalities. The python implementation of the proposed method is openly available.
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Affiliation(s)
- Vaanathi Sundaresan
- Department of Computational and Data Sciences, Indian Institute of Science, Bengaluru, Karnataka, India
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Christoph Arthofer
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- National Institute for Health and Care Research (NIHR) Nottingham Biomedical Research Centre, Queen's Medical Centre, University of Nottingham, Nottingham, United Kingdom
- Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom
| | - Giovanna Zamboni
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- Centre for Prevention of Stroke and Dementia, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- Dipartimento di Scienze Biomediche, Metaboliche e Neuroscienze, Universitá di Modena e Reggio Emilia, Modena, Italy
| | - Andrew G. Murchison
- Department of Neuroradiology, Oxford University Hospitals National Health Service (NHS) Foundation Trust, Oxford, United Kingdom
| | - Robert A. Dineen
- National Institute for Health and Care Research (NIHR) Nottingham Biomedical Research Centre, Queen's Medical Centre, University of Nottingham, Nottingham, United Kingdom
- Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom
- Radiological Sciences, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Peter M. Rothwell
- Centre for Prevention of Stroke and Dementia, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Dorothee P. Auer
- National Institute for Health and Care Research (NIHR) Nottingham Biomedical Research Centre, Queen's Medical Centre, University of Nottingham, Nottingham, United Kingdom
- Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom
- Radiological Sciences, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Chaoyue Wang
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Karla L. Miller
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Benjamin C. Tendler
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Fidel Alfaro-Almagro
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Stamatios N. Sotiropoulos
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- National Institute for Health and Care Research (NIHR) Nottingham Biomedical Research Centre, Queen's Medical Centre, University of Nottingham, Nottingham, United Kingdom
- Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom
| | - Nikola Sprigg
- Stroke Trials Unit, Mental Health and Clinical Neuroscience, University of Nottingham, Nottingham, United Kingdom
| | - Ludovica Griffanti
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, Department of Psychiatry, University of Oxford, Oxford, United Kingdom
| | - Mark Jenkinson
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- South Australian Health and Medical Research Institute, Adelaide, SA, Australia
- Australian Institute for Machine Learning, School of Computer Science, The University of Adelaide, Adelaide, SA, Australia
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12
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Oh JH, Kim HG, Lee KM. Developing and Evaluating Deep Learning Algorithms for Object Detection: Key Points for Achieving Superior Model Performance. Korean J Radiol 2023; 24:698-714. [PMID: 37404112 DOI: 10.3348/kjr.2022.0765] [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: 10/07/2022] [Revised: 04/29/2023] [Accepted: 05/16/2023] [Indexed: 07/06/2023] Open
Abstract
In recent years, artificial intelligence, especially object detection-based deep learning in computer vision, has made significant advancements, driven by the development of computing power and the widespread use of graphic processor units. Object detection-based deep learning techniques have been applied in various fields, including the medical imaging domain, where remarkable achievements have been reported in disease detection. However, the application of deep learning does not always guarantee satisfactory performance, and researchers have been employing trial-and-error to identify the factors contributing to performance degradation and enhance their models. Moreover, due to the black-box problem, the intermediate processes of a deep learning network cannot be comprehended by humans; as a result, identifying problems in a deep learning model that exhibits poor performance can be challenging. This article highlights potential issues that may cause performance degradation at each deep learning step in the medical imaging domain and discusses factors that must be considered to improve the performance of deep learning models. Researchers who wish to begin deep learning research can reduce the required amount of trial-and-error by understanding the issues discussed in this study.
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Affiliation(s)
- Jang-Hoon Oh
- Department of Radiology, Kyung Hee University Hospital, Kyung Hee University College of Medicine, Seoul, Korea
| | - Hyug-Gi Kim
- Department of Radiology, Kyung Hee University Hospital, Kyung Hee University College of Medicine, Seoul, Korea
| | - Kyung Mi Lee
- Department of Radiology, Kyung Hee University Hospital, Kyung Hee University College of Medicine, Seoul, Korea.
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13
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Rui W, Zhang S, Shi H, Sheng Y, Zhu F, Yao Y, Chen X, Cheng H, Zhang Y, Aili A, Yao Z, Zhang XY, Ren Y. Deep Learning-Assisted Quantitative Susceptibility Mapping as a Tool for Grading and Molecular Subtyping of Gliomas. PHENOMICS (CHAM, SWITZERLAND) 2023; 3:243-254. [PMID: 37325712 PMCID: PMC10260708 DOI: 10.1007/s43657-022-00087-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 11/16/2022] [Accepted: 11/20/2022] [Indexed: 06/17/2023]
Abstract
This study aimed to explore the value of deep learning (DL)-assisted quantitative susceptibility mapping (QSM) in glioma grading and molecular subtyping. Forty-two patients with gliomas, who underwent preoperative T2 fluid-attenuated inversion recovery (T2 FLAIR), contrast-enhanced T1-weighted imaging (T1WI + C), and QSM scanning at 3.0T magnetic resonance imaging (MRI) were included in this study. Histopathology and immunohistochemistry staining were used to determine glioma grades, and isocitrate dehydrogenase (IDH) 1 and alpha thalassemia/mental retardation syndrome X-linked gene (ATRX) subtypes. Tumor segmentation was performed manually using Insight Toolkit-SNAP program (www.itksnap.org). An inception convolutional neural network (CNN) with a subsequent linear layer was employed as the training encoder to capture multi-scale features from MRI slices. Fivefold cross-validation was utilized as the training strategy (seven samples for each fold), and the ratio of sample size of the training, validation, and test dataset was 4:1:1. The performance was evaluated by the accuracy and area under the curve (AUC). With the inception CNN, single modal of QSM showed better performance in differentiating glioblastomas (GBM) and other grade gliomas (OGG, grade II-III), and predicting IDH1 mutation and ATRX loss (accuracy: 0.80, 0.77, 0.60) than either T2 FLAIR (0.69, 0.57, 0.54) or T1WI + C (0.74, 0.57, 0.46). When combining three modalities, compared with any single modality, the best AUC/accuracy/F1-scores were reached in grading gliomas (OGG and GBM: 0.91/0.89/0.87, low-grade and high-grade gliomas: 0.83/0.86/0.81), predicting IDH1 mutation (0.88/0.89/0.85), and predicting ATRX loss (0.78/0.71/0.67). As a supplement to conventional MRI, DL-assisted QSM is a promising molecular imaging method to evaluate glioma grades, IDH1 mutation, and ATRX loss. Supplementary Information The online version contains supplementary material available at 10.1007/s43657-022-00087-6.
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Affiliation(s)
- Wenting Rui
- Department of Radiology, Huashan Hospital, Fudan University, Mid 12 Wulumuqi Road, Shanghai, 200040 China
| | - Shengjie Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433 China
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433 China
| | - Huidong Shi
- Department of Radiology, Huashan Hospital, Fudan University, Mid 12 Wulumuqi Road, Shanghai, 200040 China
| | - Yaru Sheng
- Department of Radiology, Huashan Hospital, Fudan University, Mid 12 Wulumuqi Road, Shanghai, 200040 China
| | - Fengping Zhu
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, 200040 China
| | - YiDi Yao
- Department of Radiology, Huashan Hospital, Fudan University, Mid 12 Wulumuqi Road, Shanghai, 200040 China
| | - Xiang Chen
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433 China
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433 China
| | - Haixia Cheng
- Department of Neuropathology, Huashan Hospital, Fudan University, Shanghai, 200040 China
| | - Yong Zhang
- GE Healthcare, MR Research, Huatuo Road, Shanghai, 201203 China
| | - Ababikere Aili
- Department of Radiology, Kuqa County People’s Hospital, Xinjiang, 842000 China
| | - Zhenwei Yao
- Department of Radiology, Huashan Hospital, Fudan University, Mid 12 Wulumuqi Road, Shanghai, 200040 China
| | - Xiao-Yong Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433 China
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433 China
| | - Yan Ren
- Department of Radiology, Huashan Hospital, Fudan University, Mid 12 Wulumuqi Road, Shanghai, 200040 China
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14
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Hsu CCT, Sethi SK, Haacke EM. The Current State of Susceptibility-Weighted Imaging and Quantitative Susceptibility Mapping in Head Trauma. Neuroimaging Clin N Am 2023; 33:343-356. [PMID: 36965951 DOI: 10.1016/j.nic.2023.01.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/27/2023]
Abstract
Susceptibility-weighted imaging (SWI) is a MR imaging technique suited to detect structural and microstructural abnormalities in traumatic brain injury (TBI). This review article provide an insight in to the physics principles of SWI and its clinical application in unraveling the complex interaction of the biophysical mechanisms of head injury. Literature evidences support SWI as the most ideal sequence in detection of microbleeds, which is the "tip of the iceberg" biomarker of microvascular injuries. The review also detailed the emerging advance techniques of Quantitative susceptibility mapping (QSM) and artificial intelligence offer the ability to detect and follow the evolution of microbleeds in patient with chronic TBI. These new techniques offers a unique insight into the acute and chronic state of TBI.
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Affiliation(s)
- Charlie Chia-Tsong Hsu
- Division of Neuroradiology, Department of Medical Imaging, Gold Coast University Hospital, Australia; Division of Neuroradiology, Lumus Imaging, Varsity Lakes Day Hospital, Gold Coast, Australia.
| | - Sean K Sethi
- Department of Radiology, Wayne State University School of Medicine
| | - E Mark Haacke
- Department of Radiology, Wayne State University School of Medicine; Department of Neurology, Wayne State University School of Medicine
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15
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Huang P, Zhang M. Magnetic Resonance Imaging Studies of Neurodegenerative Disease: From Methods to Translational Research. Neurosci Bull 2023; 39:99-112. [PMID: 35771383 PMCID: PMC9849544 DOI: 10.1007/s12264-022-00905-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 05/07/2022] [Indexed: 01/22/2023] Open
Abstract
Neurodegenerative diseases (NDs) have become a significant threat to an aging human society. Numerous studies have been conducted in the past decades to clarify their pathologic mechanisms and search for reliable biomarkers. Magnetic resonance imaging (MRI) is a powerful tool for investigating structural and functional brain alterations in NDs. With the advantages of being non-invasive and non-radioactive, it has been frequently used in both animal research and large-scale clinical investigations. MRI may serve as a bridge connecting micro- and macro-level analysis and promoting bench-to-bed translational research. Nevertheless, due to the abundance and complexity of MRI techniques, exploiting their potential is not always straightforward. This review aims to briefly introduce research progress in clinical imaging studies and discuss possible strategies for applying MRI in translational ND research.
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Affiliation(s)
- Peiyu Huang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009 China
| | - Minming Zhang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009 China
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16
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Wang H, Chen X, Yu R, Wei Z, Yao T, Gao C, Li Y, Wang Z, Yi D, Wu Y. E-DU: Deep neural network for multimodal medical image segmentation based on semantic gap compensation. Comput Biol Med 2022; 151:106206. [PMID: 36395592 DOI: 10.1016/j.compbiomed.2022.106206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 09/08/2022] [Accepted: 10/09/2022] [Indexed: 12/27/2022]
Abstract
BACKGROUND U-Net includes encoder, decoder and skip connection structures. It has become the benchmark network in medical image segmentation. However, the direct fusion of low-level and high-level convolution features with semantic gaps by traditional skip connections may lead to problems such as fuzzy generated feature maps and target region segmentation errors. OBJECTIVE We use spatial enhancement filtering technology to compensate for the semantic gap and propose an enhanced dense U-Net (E-DU), aiming to apply it to multimodal medical image segmentation to improve the segmentation performance and efficiency. METHODS Before combining encoder and decoder features, we replace the traditional skip connection with a multiscale denoise enhancement (MDE) module. The encoder features need to be deeply convolved by the spatial enhancement filter and then combined with the decoder features. We propose a simple and efficient deep full convolution network structure E-DU, which can not only fuse semantically various features but also denoise and enhance the feature map. RESULTS We performed experiments on medical image segmentation datasets with seven image modalities and combined MDE with various baseline networks to perform ablation studies. E-DU achieved the best segmentation results on evaluation indicators such as DSC on the U-Net family, with DSC values of 97.78, 97.64, 95.31, 94.42, 94.93, 98.85, and 98.38 (%), respectively. The addition of the MDE module to the attention mechanism network improves segmentation performance and efficiency, reflecting its generalization performance. In comparison to advanced methods, our method is also competitive. CONCLUSION Our proposed MDE module has a good segmentation effect and operating efficiency, and it can be easily extended to multiple modal medical segmentation datasets. Our idea and method can achieve clinical multimodal medical image segmentation and make full use of image information to provide clinical decision support. It has great application value and promotion prospects.
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Affiliation(s)
- Haojia Wang
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China
| | - Xicheng Chen
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China
| | - Rui Yu
- Tactical Health Service Department, NCO School of Army Medical University, Zhongshanxi Road 450, Qiaoxi District, Shijiazhuang, 050081, China
| | - Zeliang Wei
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China
| | - Tianhua Yao
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China
| | - Chengcheng Gao
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China
| | - Yang Li
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China
| | - Zhenyan Wang
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China
| | - Dong Yi
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China.
| | - Yazhou Wu
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, NO.30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China.
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Suwalska A, Wang Y, Yuan Z, Jiang Y, Zhu D, Chen J, Cui M, Chen X, Suo C, Polanska J. CMB-HUNT: Automatic detection of cerebral microbleeds using a deep neural network. Comput Biol Med 2022; 151:106233. [PMID: 36370581 DOI: 10.1016/j.compbiomed.2022.106233] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 10/03/2022] [Accepted: 10/22/2022] [Indexed: 12/27/2022]
Abstract
Cerebral microbleeds (CMBs) are gaining increasing interest due to their importance in diagnosing cerebral small vessel diseases. However, manual inspection of CMBs is time-consuming and prone to human error. Existing automated or semi-automated solutions still have insufficient detection sensitivity and specificity. Furthermore, they frequently use more than one magnetic resonance imaging modality, but these are not always available. The majority of AI-based solutions use either numeric or image data, which may not provide sufficient information about the true nature of CMBs. This paper proposes a deep neural network with multi-type input data for automated CMB detection (CMB-HUNT) using only susceptibility-weighted imaging data (SWI). Combination of SWIs and radiomic-type numerical features allowed us to identify CMBs with high accuracy without the need for additional imaging modalities or complex predictive models. Two independent datasets were used: one with 304 patients (39 with CMBs) for training and internal system validation and one with 61 patients (21 with CMBs) for external validation. For the hold-out testing dataset, CMB-HUNT reached a sensitivity of 90.0%. As results of testing showed, CMB-HUNT outperforms existing methods in terms of the number of FPs per case, which is the lowest reported thus far (0.54 FPs/patient). The proposed system was successfully applied to the independent validation set, reaching a sensitivity of 91.5% with 1.9 false positives per patient and proving its generalization potential. The results were comparable to previous studies. Our research confirms the usefulness of deep learning solutions for CMB detection based only on one MRI modality.
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Affiliation(s)
- Aleksandra Suwalska
- Department of Data Science and Engineering, Silesian University of Technology, Akademicka 16, 44-100, Gliwice, Poland.
| | - Yingzhe Wang
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences, Fudan University, Songhu Road, 2005, Shanghai, China; Taizhou Institute of Health Sciences, Fudan University, Yaocheng Road 799, Taizhou, Jiangsu, China
| | - Ziyu Yuan
- Taizhou Institute of Health Sciences, Fudan University, Yaocheng Road 799, Taizhou, Jiangsu, China
| | - Yanfeng Jiang
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences, Fudan University, Songhu Road, 2005, Shanghai, China; Taizhou Institute of Health Sciences, Fudan University, Yaocheng Road 799, Taizhou, Jiangsu, China
| | - Dongliang Zhu
- Department of Epidemiology & Ministry of Education Key Laboratory of Public Health Safety, School of Public Health, Fudan University, Dongan Road 130, Shanghai, China
| | - Jinhua Chen
- Taizhou People's Hospital, Taihu Road 366, Taizhou, Jiangsu, China
| | - Mei Cui
- Department of Neurology, Huashan Hospital, Fudan University, Middle Wulumuqi Road 12, Shanghai, China
| | - Xingdong Chen
- State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences, Fudan University, Songhu Road, 2005, Shanghai, China; Taizhou Institute of Health Sciences, Fudan University, Yaocheng Road 799, Taizhou, Jiangsu, China.
| | - Chen Suo
- Taizhou Institute of Health Sciences, Fudan University, Yaocheng Road 799, Taizhou, Jiangsu, China; Department of Epidemiology & Ministry of Education Key Laboratory of Public Health Safety, School of Public Health, Fudan University, Dongan Road 130, Shanghai, China.
| | - Joanna Polanska
- Department of Data Science and Engineering, Silesian University of Technology, Akademicka 16, 44-100, Gliwice, Poland
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18
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Lee H, Kim J, Lee S, Jung K, Kim W, Noh Y, Kim EY, Kang KM, Sohn C, Lee DY, Al‐masni MA, Kim D. Detection of Cerebral Microbleeds in
MR
Images Using a
Single‐Stage
Triplanar Ensemble Detection Network (TPE‐Det). J Magn Reson Imaging 2022. [DOI: 10.1002/jmri.28487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 10/05/2022] [Accepted: 10/06/2022] [Indexed: 11/06/2022] Open
Affiliation(s)
- Haejoon Lee
- Department of Electrical and Electronic Engineering, College of Engineering Yonsei University Seoul Republic of Korea
- Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh Pennsylvania USA
| | - Jun‐Ho Kim
- Department of Electrical and Electronic Engineering, College of Engineering Yonsei University Seoul Republic of Korea
| | - Seul Lee
- Department of Electrical and Electronic Engineering, College of Engineering Yonsei University Seoul Republic of Korea
| | - Kyu‐Jin Jung
- Department of Electrical and Electronic Engineering, College of Engineering Yonsei University Seoul Republic of Korea
| | - Woo‐Ram Kim
- Neuroscience Research Institute Gachon University Incheon Republic of Korea
| | - Young Noh
- Neuroscience Research Institute Gachon University Incheon Republic of Korea
- Department of Neurology, Gachon University College of Medicine Gil Medical Center Incheon Republic of Korea
| | - Eung Yeop Kim
- Department of Radiology, Gachon University College of Medicine Gil Medical Center Incheon Republic of Korea
| | - Koung Mi Kang
- Department of Radiology Seoul National University Hospital Seoul Republic of Korea
- Department of Radiology Seoul National University College of Medicine Seoul Republic of Korea
| | - Chul‐Ho Sohn
- Department of Radiology Seoul National University Hospital Seoul Republic of Korea
- Department of Radiology Seoul National University College of Medicine Seoul Republic of Korea
| | - Dong Young Lee
- Department of Neuropsychiatry Seoul National University Hospital Seoul Republic of Korea
- Department of Psychiatry Seoul National University College of Medicine Seoul Republic of Korea
- Institute of Human Behavioral Medicine Medical Research Center Seoul National University Seoul Republic of Korea
| | - Mohammed A. Al‐masni
- Department of Artificial Intelligence, College of Software & Convergence Technology, Daeyang AI Center Sejong University Seoul Republic of Korea
| | - Dong‐Hyun Kim
- Department of Electrical and Electronic Engineering, College of Engineering Yonsei University Seoul Republic of Korea
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19
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Avberšek LK, Repovš G. Deep learning in neuroimaging data analysis: Applications, challenges, and solutions. FRONTIERS IN NEUROIMAGING 2022; 1:981642. [PMID: 37555142 PMCID: PMC10406264 DOI: 10.3389/fnimg.2022.981642] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 10/10/2022] [Indexed: 08/10/2023]
Abstract
Methods for the analysis of neuroimaging data have advanced significantly since the beginning of neuroscience as a scientific discipline. Today, sophisticated statistical procedures allow us to examine complex multivariate patterns, however most of them are still constrained by assuming inherent linearity of neural processes. Here, we discuss a group of machine learning methods, called deep learning, which have drawn much attention in and outside the field of neuroscience in recent years and hold the potential to surpass the mentioned limitations. Firstly, we describe and explain the essential concepts in deep learning: the structure and the computational operations that allow deep models to learn. After that, we move to the most common applications of deep learning in neuroimaging data analysis: prediction of outcome, interpretation of internal representations, generation of synthetic data and segmentation. In the next section we present issues that deep learning poses, which concerns multidimensionality and multimodality of data, overfitting and computational cost, and propose possible solutions. Lastly, we discuss the current reach of DL usage in all the common applications in neuroimaging data analysis, where we consider the promise of multimodality, capability of processing raw data, and advanced visualization strategies. We identify research gaps, such as focusing on a limited number of criterion variables and the lack of a well-defined strategy for choosing architecture and hyperparameters. Furthermore, we talk about the possibility of conducting research with constructs that have been ignored so far or/and moving toward frameworks, such as RDoC, the potential of transfer learning and generation of synthetic data.
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Affiliation(s)
- Lev Kiar Avberšek
- Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia
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20
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Matsoukas S, Scaggiante J, Schuldt BR, Smith CJ, Chennareddy S, Kalagara R, Majidi S, Bederson JB, Fifi JT, Mocco J, Kellner CP. Accuracy of artificial intelligence for the detection of intracranial hemorrhage and chronic cerebral microbleeds: a systematic review and pooled analysis. LA RADIOLOGIA MEDICA 2022; 127:1106-1123. [PMID: 35962888 DOI: 10.1007/s11547-022-01530-4] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Accepted: 07/12/2022] [Indexed: 11/25/2022]
Abstract
BACKGROUND Artificial intelligence (AI)-driven software has been developed and become commercially available within the past few years for the detection of intracranial hemorrhage (ICH) and chronic cerebral microbleeds (CMBs). However, there is currently no systematic review that summarizes all of these tools or provides pooled estimates of their performance. METHODS In this PROSPERO-registered, PRISMA compliant systematic review, we sought to compile and review all MEDLINE and EMBASE published studies that have developed and/or tested AI algorithms for ICH detection on non-contrast CT scans (NCCTs) or MRI scans and CMBs detection on MRI scans. RESULTS In total, 40 studies described AI algorithms for ICH detection in NCCTs/MRIs and 19 for CMBs detection in MRIs. The overall sensitivity, specificity, and accuracy were 92.06%, 93.54%, and 93.46%, respectively, for ICH detection and 91.6%, 93.9%, and 92.7% for CMBs detection. Some of the challenges encountered in the development of these algorithms include the laborious work of creating large, labeled and balanced datasets, the volumetric nature of the imaging examinations, the fine tuning of the algorithms, and the reduction in false positives. CONCLUSIONS Numerous AI-driven software tools have been developed over the last decade. On average, they are characterized by high performance and expert-level accuracy for the diagnosis of ICH and CMBs. As a result, implementing these tools in clinical practice may improve workflow and act as a failsafe for the detection of such lesions. REGISTRATION-URL: https://www.crd.york.ac.uk/prospero/ Unique Identifier: CRD42021246848.
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Affiliation(s)
- Stavros Matsoukas
- Department of Neurosurgery, Mount Sinai Health System, Annenberg Building, Room 20-86, 1468 Madison Ave, New York, NY, 10029, USA.
| | - Jacopo Scaggiante
- Department of Neurosurgery, Mount Sinai Health System, Annenberg Building, Room 20-86, 1468 Madison Ave, New York, NY, 10029, USA
| | - Braxton R Schuldt
- Department of Neurosurgery, Mount Sinai Health System, Annenberg Building, Room 20-86, 1468 Madison Ave, New York, NY, 10029, USA
| | - Colton J Smith
- Department of Neurosurgery, Mount Sinai Health System, Annenberg Building, Room 20-86, 1468 Madison Ave, New York, NY, 10029, USA
| | - Susmita Chennareddy
- Department of Neurosurgery, Mount Sinai Health System, Annenberg Building, Room 20-86, 1468 Madison Ave, New York, NY, 10029, USA
| | - Roshini Kalagara
- Department of Neurosurgery, Mount Sinai Health System, Annenberg Building, Room 20-86, 1468 Madison Ave, New York, NY, 10029, USA
| | - Shahram Majidi
- Department of Neurosurgery, Mount Sinai Health System, Annenberg Building, Room 20-86, 1468 Madison Ave, New York, NY, 10029, USA
| | - Joshua B Bederson
- Department of Neurosurgery, Mount Sinai Health System, Annenberg Building, Room 20-86, 1468 Madison Ave, New York, NY, 10029, USA
| | - Johanna T Fifi
- Department of Neurosurgery, Mount Sinai Health System, Annenberg Building, Room 20-86, 1468 Madison Ave, New York, NY, 10029, USA
| | - J Mocco
- Department of Neurosurgery, Mount Sinai Health System, Annenberg Building, Room 20-86, 1468 Madison Ave, New York, NY, 10029, USA
| | - Christopher P Kellner
- Department of Neurosurgery, Mount Sinai Health System, Annenberg Building, Room 20-86, 1468 Madison Ave, New York, NY, 10029, USA
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21
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Jiang J, Wang D, Song Y, Sachdev PS, Wen W. Computer-Aided Extraction of Select MRI Markers of Cerebral Small Vessel Disease: A Systematic Review. Neuroimage 2022; 261:119528. [PMID: 35914668 DOI: 10.1016/j.neuroimage.2022.119528] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 07/18/2022] [Accepted: 07/28/2022] [Indexed: 11/30/2022] Open
Abstract
Cerebral small vessel disease (CSVD) is a major vascular contributor to cognitive impairment in ageing, including dementias. Imaging remains the most promising method for in vivo studies of CSVD. To replace the subjective and laborious visual rating approaches, emerging studies have applied state-of-the-art artificial intelligence to extract imaging biomarkers of CSVD from MRI scans. We aimed to summarise published computer-aided methods for the examination of three imaging biomarkers of CSVD, namely cerebral microbleeds (CMB), dilated perivascular spaces (PVS), and lacunes of presumed vascular origin. Seventy classical image processing, classical machine learning, and deep learning studies were identified. Transfer learning and weak supervision techniques have been applied to accommodate the limitations in the training data. While good performance metrics were achieved in local datasets, there have not been generalisable pipelines validated in different research and/or clinical cohorts. Future studies could consider pooling data from multiple sources to increase data size and diversity, and evaluating performance using both image processing metrics and associations with clinical measures.
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Affiliation(s)
- Jiyang Jiang
- Centre for Healthy Brain Ageing, Discipline of Psychiatry and Mental Health, School of Clinical Medicine, Faculty of Medicine, University of New South Wales, NSW 2052, Australia.
| | - Dadong Wang
- Quantitative Imaging Research Team, Data61, CSIRO, Marsfield, NSW 2122, Australia
| | - Yang Song
- School of Computer Science and Engineering, University of New South Wales, NSW 2052, Australia
| | - Perminder S Sachdev
- Centre for Healthy Brain Ageing, Discipline of Psychiatry and Mental Health, School of Clinical Medicine, Faculty of Medicine, University of New South Wales, NSW 2052, Australia; Neuropsychiatric Institute, Prince of Wales Hospital, Randwick, NSW 2031, Australia
| | - Wei Wen
- Centre for Healthy Brain Ageing, Discipline of Psychiatry and Mental Health, School of Clinical Medicine, Faculty of Medicine, University of New South Wales, NSW 2052, Australia; Neuropsychiatric Institute, Prince of Wales Hospital, Randwick, NSW 2031, Australia
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22
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Wei Z, Chen X, Huang J, Wang Z, Yao T, Gao C, Wang H, Li P, Ye W, Li Y, Yao N, Zhang R, Tang N, Wang F, Hu J, Yi D, Wu Y. Construction of a Medical Micro-Object Cascade Network for Automated Segmentation of Cerebral Microbleeds in Susceptibility Weighted Imaging. Front Bioeng Biotechnol 2022; 10:937314. [PMID: 35935490 PMCID: PMC9350526 DOI: 10.3389/fbioe.2022.937314] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 06/20/2022] [Indexed: 11/20/2022] Open
Abstract
Aim: The detection and segmentation of cerebral microbleeds (CMBs) images are the focus of clinical diagnosis and treatment. However, segmentation is difficult in clinical practice, and missed diagnosis may occur. Few related studies on the automated segmentation of CMB images have been performed, and we provide the most effective CMB segmentation to date using an automated segmentation system. Materials and Methods: From a research perspective, we focused on the automated segmentation of CMB targets in susceptibility weighted imaging (SWI) for the first time and then constructed a deep learning network focused on the segmentation of micro-objects. We collected and marked clinical datasets and proposed a new medical micro-object cascade network (MMOC-Net). In the first stage, U-Net was utilized to select the region of interest (ROI). In the second stage, we utilized a full-resolution network (FRN) to complete fine segmentation. We also incorporated residual atrous spatial pyramid pooling (R-ASPP) and a new joint loss function. Results: The most suitable segmentation result was achieved with a ROI size of 32 × 32. To verify the validity of each part of the method, ablation studies were performed, which showed that the best segmentation results were obtained when FRN, R-ASPP and the combined loss function were used simultaneously. Under these conditions, the obtained Dice similarity coefficient (DSC) value was 87.93% and the F2-score (F2) value was 90.69%. We also innovatively developed a visual clinical diagnosis system that can provide effective support for clinical diagnosis and treatment decisions. Conclusions: We created the MMOC-Net method to perform the automated segmentation task of CMBs in an SWI and obtained better segmentation performance; hence, this pioneering method has research significance.
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Affiliation(s)
- Zeliang Wei
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
| | - Xicheng Chen
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
| | - Jialu Huang
- Department of Neurology, Southwest Hospital, Army Medical University, Chongqing, China
| | - Zhenyan Wang
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
| | - Tianhua Yao
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
| | - Chengcheng Gao
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
| | - Haojia Wang
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
| | - Pengpeng Li
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
| | - Wei Ye
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
| | - Yang Li
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
| | - Ning Yao
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
| | - Rui Zhang
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
| | - Ning Tang
- Department of Medical Engineering, The 953 Hospital of the Chinese People’s Liberation Army, Shigatse, China
| | - Fei Wang
- Medical Big Data and Artificial Intelligence Center, Southwest Hospital, Army Medical University, Chongqing, China
| | - Jun Hu
- Department of Neurology, Southwest Hospital, Army Medical University, Chongqing, China
- *Correspondence: Jun Hu, ; Dong Yi, ; Yazhou Wu,
| | - Dong Yi
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
- *Correspondence: Jun Hu, ; Dong Yi, ; Yazhou Wu,
| | - Yazhou Wu
- Department of Health Statistics, College of Preventive Medicine, Army Medical University, Chongqing, China
- *Correspondence: Jun Hu, ; Dong Yi, ; Yazhou Wu,
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23
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Stanley BF, Wilfred Franklin S. Automated cerebral microbleed detection using selective 3D gradient co-occurance matrix and convolutional neural network. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103560] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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24
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Automated detection of cerebral microbleeds via segmentation in susceptibility-weighted images of patients with traumatic brain injury. Neuroimage Clin 2022; 35:103027. [PMID: 35597029 PMCID: PMC9127224 DOI: 10.1016/j.nicl.2022.103027] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 04/21/2022] [Accepted: 04/24/2022] [Indexed: 12/24/2022]
Abstract
Cerebral microbleeds (CMBs) are a recognised biomarker of traumatic axonal injury (TAI). Their number and location provide valuable information in the long-term prognosis of patients who sustained a traumatic brain injury (TBI). Accurate detection of CMBs is necessary for both research and clinical applications. CMBs appear as small hypointense lesions on susceptibility-weighted magnetic resonance imaging (SWI). Their size and shape vary markedly in cases of TBI. Manual annotation of CMBs is a difficult, error-prone, and time-consuming task. Several studies addressed the detection of CMBs in other neuropathologies with convolutional neural networks (CNNs). In this study, we developed and contrasted a classification (Patch-CNN) and two segmentation (Segmentation-CNN, U-Net) approaches for the detection of CMBs in TBI cases. The models were trained using 45 datasets, and the best models were chosen according to 16 validation sets. Finally, the models were evaluated on 10 TBI and healthy control cases, respectively. Our three models outperform the current status quo in the detection of traumatic CMBs, achieving higher sensitivity at low false positive (FP) counts. Furthermore, using a segmentation approach allows for better precision. The best model, the U-Net, achieves a detection rate of 90% at FP counts of 17.1 in TBI patients and 3.4 in healthy controls.
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25
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Zhu G, Chen H, Jiang B, Chen F, Xie Y, Wintermark M. Application of Deep Learning to Ischemic and Hemorrhagic Stroke Computed Tomography and Magnetic Resonance Imaging. Semin Ultrasound CT MR 2022; 43:147-152. [PMID: 35339255 DOI: 10.1053/j.sult.2022.02.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Deep Learning (DL) algorithm holds great potential in the field of stroke imaging. It has been applied not only to the "downstream" side such as lesion detection, treatment decision making, and outcome prediction, but also to the "upstream" side for generation and enhancement of stroke imaging. This paper aims to comprehensively overview the common applications of DL to stroke imaging. In the future, more standardized imaging datasets and more extensive studies are needed to establish and validate the role of DL in stroke imaging.
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Affiliation(s)
- Guangming Zhu
- Department of Radiology, Neuroradiology Section, Stanford University School of Medicine, Stanford, CA
| | - Hui Chen
- Department of Radiology, Neuroradiology Section, Stanford University School of Medicine, Stanford, CA
| | - Bin Jiang
- Department of Radiology, Neuroradiology Section, Stanford University School of Medicine, Stanford, CA
| | - Fei Chen
- Department of Neurology, Xuan Wu hospital, Capital Meidcal University, Beijing, China
| | - Yuan Xie
- Subtle Medical Inc, Menlo Park, CA
| | - Max Wintermark
- Department of Radiology, Neuroradiology Section, Stanford University School of Medicine, Stanford, CA.
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26
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Sundaresan V, Arthofer C, Zamboni G, Dineen RA, Rothwell PM, Sotiropoulos SN, Auer DP, Tozer DJ, Markus HS, Miller KL, Dragonu I, Sprigg N, Alfaro-Almagro F, Jenkinson M, Griffanti L. Automated Detection of Candidate Subjects With Cerebral Microbleeds Using Machine Learning. Front Neuroinform 2022; 15:777828. [PMID: 35126079 PMCID: PMC8811357 DOI: 10.3389/fninf.2021.777828] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 12/23/2021] [Indexed: 11/21/2022] Open
Abstract
Cerebral microbleeds (CMBs) appear as small, circular, well defined hypointense lesions of a few mm in size on T2*-weighted gradient recalled echo (T2*-GRE) images and appear enhanced on susceptibility weighted images (SWI). Due to their small size, contrast variations and other mimics (e.g., blood vessels), CMBs are highly challenging to detect automatically. In large datasets (e.g., the UK Biobank dataset), exhaustively labelling CMBs manually is difficult and time consuming. Hence it would be useful to preselect candidate CMB subjects in order to focus on those for manual labelling, which is essential for training and testing automated CMB detection tools on these datasets. In this work, we aim to detect CMB candidate subjects from a larger dataset, UK Biobank, using a machine learning-based, computationally light pipeline. For our evaluation, we used 3 different datasets, with different intensity characteristics, acquired with different scanners. They include the UK Biobank dataset and two clinical datasets with different pathological conditions. We developed and evaluated our pipelines on different types of images, consisting of SWI or GRE images. We also used the UK Biobank dataset to compare our approach with alternative CMB preselection methods using non-imaging factors and/or imaging data. Finally, we evaluated the pipeline's generalisability across datasets. Our method provided subject-level detection accuracy > 80% on all the datasets (within-dataset results), and showed good generalisability across datasets, providing a consistent accuracy of over 80%, even when evaluated across different modalities.
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Affiliation(s)
- Vaanathi Sundaresan
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, University of Oxford, Oxford, United Kingdom
- Oxford-Nottingham Centre for Doctoral Training in Biomedical Imaging, University of Oxford, Oxford, United Kingdom
| | - Christoph Arthofer
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, University of Oxford, Oxford, United Kingdom
- NIHR Nottingham Biomedical Research Centre, Queen's Medical Centre, University of Nottingham, Nottingham, United Kingdom
- Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom
| | - Giovanna Zamboni
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, University of Oxford, Oxford, United Kingdom
- Wolfson Centre for Prevention of Stroke and Dementia, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- Dipartimento di Scienze Biomediche, Metaboliche e Neuroscienze, Università di Modena e Reggio Emilia, Modena, Italy
| | - Robert A. Dineen
- NIHR Nottingham Biomedical Research Centre, Queen's Medical Centre, University of Nottingham, Nottingham, United Kingdom
- Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom
- Radiological Sciences, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Peter M. Rothwell
- Wolfson Centre for Prevention of Stroke and Dementia, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Stamatios N. Sotiropoulos
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, University of Oxford, Oxford, United Kingdom
- NIHR Nottingham Biomedical Research Centre, Queen's Medical Centre, University of Nottingham, Nottingham, United Kingdom
- Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom
| | - Dorothee P. Auer
- NIHR Nottingham Biomedical Research Centre, Queen's Medical Centre, University of Nottingham, Nottingham, United Kingdom
- Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom
- Radiological Sciences, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Daniel J. Tozer
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Hugh S. Markus
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom
| | - Karla L. Miller
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, University of Oxford, Oxford, United Kingdom
| | - Iulius Dragonu
- Siemens Healthcare Ltd., Research and Collaborations GB & I, Frimley, United Kingdom
| | - Nikola Sprigg
- Stroke Trials Unit, Mental Health and Clinical Neuroscience, University of Nottingham, Nottingham, United Kingdom
| | - Fidel Alfaro-Almagro
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, University of Oxford, Oxford, United Kingdom
| | - Mark Jenkinson
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, University of Oxford, Oxford, United Kingdom
- South Australian Health and Medical Research Institute (SAHMRI), Adelaide, SA, Australia
- Australian Institute for Machine Learning (AIML), School of Computer Science, The University of Adelaide, Adelaide, SA, Australia
| | - Ludovica Griffanti
- Nuffield Department of Clinical Neurosciences, Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional MRI of the Brain, University of Oxford, Oxford, United Kingdom
- Department of Psychiatry, Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity, University of Oxford, Oxford, United Kingdom
- *Correspondence: Ludovica Griffanti
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27
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Momeni S, Fazlollahi A, Lebrat L, Yates P, Rowe C, Gao Y, Liew AWC, Salvado O. Generative Model of Brain Microbleeds for MRI Detection of Vascular Marker of Neurodegenerative Diseases. Front Neurosci 2022; 15:778767. [PMID: 34975381 PMCID: PMC8716785 DOI: 10.3389/fnins.2021.778767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 11/05/2021] [Indexed: 11/13/2022] Open
Abstract
Cerebral microbleeds (CMB) are increasingly present with aging and can reveal vascular pathologies associated with neurodegeneration. Deep learning-based classifiers can detect and quantify CMB from MRI, such as susceptibility imaging, but are challenging to train because of the limited availability of ground truth and many confounding imaging features, such as vessels or infarcts. In this study, we present a novel generative adversarial network (GAN) that has been trained to generate three-dimensional lesions, conditioned by volume and location. This allows one to investigate CMB characteristics and create large training datasets for deep learning-based detectors. We demonstrate the benefit of this approach by achieving state-of-the-art CMB detection of real CMB using a convolutional neural network classifier trained on synthetic CMB. Moreover, we showed that our proposed 3D lesion GAN model can be applied on unseen dataset, with different MRI parameters and diseases, to generate synthetic lesions with high diversity and without needing laboriously marked ground truth.
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Affiliation(s)
- Saba Momeni
- Commonwealth Scientific and Industrial Research Organisation (CSIRO) Data61, Brisbane, QLD, Australia.,School of Engineering and Built Environment, Griffith University, Nathan, QLD, Australia
| | - Amir Fazlollahi
- Commonwealth Scientific and Industrial Research Organisation (CSIRO) Health and Biosecurity, Australian E-Health Research Centre, Brisbane, QLD, Australia.,Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Leo Lebrat
- Commonwealth Scientific and Industrial Research Organisation (CSIRO) Health and Biosecurity, Australian E-Health Research Centre, Brisbane, QLD, Australia
| | - Paul Yates
- Department of Geriatric Medicine, Austin Health, Heidelberg, VIC, Australia
| | - Christopher Rowe
- Department of Molecular Imaging and Therapy, Austin Health, Heidelberg, VIC, Australia.,The Florey Department of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - Yongsheng Gao
- School of Engineering and Built Environment, Griffith University, Nathan, QLD, Australia
| | - Alan Wee-Chung Liew
- School of Information and Communication Technology, Griffith University, Nathan, QLD, Australia
| | - Olivier Salvado
- Commonwealth Scientific and Industrial Research Organisation (CSIRO) Data61, Brisbane, QLD, Australia.,Department of Nuclear Medicine, Centre for PET, Austin Health, Heidelberg, VIC, Australia
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28
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Xie H, Zhuang H, Guo Y, Sharma RD, Zhang Q, Li J, Lu S, Xu L, Chan Q, Yoneda T, Spincemaille P, Zhang H, Guo H, Prince MR, Yu C, Wang Y. The appearance of magnetic susceptibility objects in SWI phase depends on object size: Comparison with QSM and CT. Clin Imaging 2021; 82:67-72. [PMID: 34798560 DOI: 10.1016/j.clinimag.2021.11.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 10/14/2021] [Accepted: 11/07/2021] [Indexed: 11/03/2022]
Abstract
PURPOSE Tissue magnetic susceptibility sign can potentially be detected on susceptibility weighted imaging (SWI) phase (SW-P). This study aims to investigate its performance for depicting brain susceptibility structures. METHODS A simulation was performed to depict magnetic susceptibility structures of various geometries on SW-P and quantitative susceptibility mapping (QSM). Brain MRI was performed on 25 subjects using SWI on a 3 T MRI system. QSM was generated from the same data. SW-P and QSM were analyzed according to radiological assessment for depicting globus pallidus nuclei, optic radiation white matter tracts, and lateral ventricular choroid plexus calcifications. In 11 of these subjects, CT was available and correlated with SW-P and QSM to assess their performance in quantifying calcifications in the choroid plexus. RESULTS In simulation, the appearance of a sphere on SW-P ranged from centric nodule to mixed positive and negative values as the diameter increased. Large cylinders also appeared as mixed positive and negative values. In comparison, QSM correctly depicted the susceptibility distribution of all magnetic structures. On human brain images, SW-P depicted the globus pallidus and optic radiation with mixed positive and negative values, consistent with simulation, and small choroid plexus calcifications as either mixed positive and negative values or as centric nodules; QSM depicted all structures as solid structures with the expected signs. For measuring calcification in the choroid plexus, QSM vs CT linear regression had a higher coefficient of determination compared to SW-P vs CT and SW-P vs QSM. CONCLUSION Appearance of susceptibility sources on SW-P changes with object size. This problem can be overcome using QSM.
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Affiliation(s)
- Hong Xie
- Department of Radiology, The First College of Clinical Medical Science, Three Gorges University, Yichang, Hubei Province, China
| | - Hangwei Zhuang
- Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA
| | - Yihao Guo
- Department of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong Province, China
| | - Ria D Sharma
- Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA
| | - Qihao Zhang
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA
| | - Jiahao Li
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA
| | - Shimin Lu
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Liang Xu
- Department of Radiology, The First College of Clinical Medical Science, Three Gorges University, Yichang, Hubei Province, China
| | | | - Tetsuya Yoneda
- Department of Medical Imaging Sciences, Kumamoto University, Kumamoto, Japan
| | - Pascal Spincemaille
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA
| | - Honglei Zhang
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Hua Guo
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Martin R Prince
- Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA
| | - Chengxin Yu
- Department of Radiology, The First College of Clinical Medical Science, Three Gorges University, Yichang, Hubei Province, China
| | - Yi Wang
- Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA; Department of Radiology, Weill Medical College of Cornell University, New York, NY, USA.
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A Comprehensive Analysis of Deep Neural-Based Cerebral Microbleeds Detection System. ELECTRONICS 2021. [DOI: 10.3390/electronics10182208] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Machine learning-based systems are gaining interest in the field of medicine, mostly in medical imaging and diagnosis. In this paper, we address the problem of automatic cerebral microbleeds (CMB) detection in magnetic resonance images. It is challenging due to difficulty in distinguishing a true CMB from its mimics, however, if successfully solved, it would streamline the radiologists work. To deal with this complex three-dimensional problem, we propose a machine learning approach based on a 2D Faster RCNN network. We aimed to achieve a reliable system, i.e., with balanced sensitivity and precision. Therefore, we have researched and analysed, among others, impact of the way the training data are provided to the system, their pre-processing, the choice of model and its structure, and also the ways of regularisation. Furthermore, we also carefully analysed the network predictions and proposed an algorithm for its post-processing. The proposed approach enabled for obtaining high precision (89.74%), sensitivity (92.62%), and F1 score (90.84%). The paper presents the main challenges connected with automatic cerebral microbleeds detection, its deep analysis and developed system. The conducted research may significantly contribute to automatic medical diagnosis.
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Lu SY, Nayak DR, Wang SH, Zhang YD. A cerebral microbleed diagnosis method via FeatureNet and ensembled randomized neural networks. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107567] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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Momeni S, Fazlollahi A, Yates P, Rowe C, Gao Y, Liew AWC, Salvado O. Synthetic microbleeds generation for classifier training without ground truth. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 207:106127. [PMID: 34051412 DOI: 10.1016/j.cmpb.2021.106127] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Accepted: 04/21/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVE Cerebral microbleeds (CMB) are important biomarkers of cerebrovascular diseases and cognitive dysfunctions. Susceptibility weighted imaging (SWI) is a common MRI sequence where CMB appear as small hypointense blobs. The prevalence of CMB in the population and in each scan is low, resulting in tedious and time-consuming visual assessment. Automated detection methods would be of value but are challenged by the CMB low prevalence, the presence of mimics such as blood vessels, and the difficulty to obtain sufficient ground truth for training and testing. In this paper, synthetic CMB (sCMB) generation using an analytical model is proposed for training and testing machine learning methods. The main aim is creating perfect synthetic ground truth as similar as reals, in high number, with a high diversity of shape, volume, intensity, and location to improve training of supervised methods. METHOD sCMB were modelled with a random Gaussian shape and added to healthy brain locations. We compared training on our synthetic data to standard augmentation techniques. We performed a validation experiment using sCMB and report result for whole brain detection using a 10-fold cross validation design with an ensemble of 10 neural networks. RESULTS Performance was close to state of the art (~9 false positives per scan), when random forest was trained on synthetic only and tested on real lesion. Other experiments showed that top detection performance could be achieved when training on synthetic CMB only. Our dataset is made available, including a version with 37,000 synthetic lesions, that could be used for benchmarking and training. CONCLUSION Our proposed synthetic microbleeds model is a powerful data augmentation approach for CMB classification with and should be considered for training automated lesion detection system from MRI SWI.
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Affiliation(s)
- Saba Momeni
- CSIRO Health and Biosecurity, Australian E-Health Research Centre, Brisbane, Australia; School of Engineering and Built Environment, Griffith University, Brisbane, Australia.
| | - Amir Fazlollahi
- CSIRO Health and Biosecurity, Australian E-Health Research Centre, Brisbane, Australia
| | - Paul Yates
- Department of Aged Care, Austin Health, Heidelberg, Victoria, Australia
| | - Christopher Rowe
- Department of Nuclear Medicine and Centre for PET, Austin Health, Heidelberg, Australia
| | - Yongsheng Gao
- School of Engineering and Built Environment, Griffith University, Brisbane, Australia
| | - Alan Wee-Chung Liew
- School of Information & Communication Technology, Griffith University, Gold Coast, Australia
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Rashid T, Abdulkadir A, Nasrallah IM, Ware JB, Liu H, Spincemaille P, Romero JR, Bryan RN, Heckbert SR, Habes M. DEEPMIR: a deep neural network for differential detection of cerebral microbleeds and iron deposits in MRI. Sci Rep 2021; 11:14124. [PMID: 34238951 PMCID: PMC8266884 DOI: 10.1038/s41598-021-93427-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 06/24/2021] [Indexed: 12/24/2022] Open
Abstract
Lobar cerebral microbleeds (CMBs) and localized non-hemorrhage iron deposits in the basal ganglia have been associated with brain aging, vascular disease and neurodegenerative disorders. Particularly, CMBs are small lesions and require multiple neuroimaging modalities for accurate detection. Quantitative susceptibility mapping (QSM) derived from in vivo magnetic resonance imaging (MRI) is necessary to differentiate between iron content and mineralization. We set out to develop a deep learning-based segmentation method suitable for segmenting both CMBs and iron deposits. We included a convenience sample of 24 participants from the MESA cohort and used T2-weighted images, susceptibility weighted imaging (SWI), and QSM to segment the two types of lesions. We developed a protocol for simultaneous manual annotation of CMBs and non-hemorrhage iron deposits in the basal ganglia. This manual annotation was then used to train a deep convolution neural network (CNN). Specifically, we adapted the U-Net model with a higher number of resolution layers to be able to detect small lesions such as CMBs from standard resolution MRI. We tested different combinations of the three modalities to determine the most informative data sources for the detection tasks. In the detection of CMBs using single class and multiclass models, we achieved an average sensitivity and precision of between 0.84-0.88 and 0.40-0.59, respectively. The same framework detected non-hemorrhage iron deposits with an average sensitivity and precision of about 0.75-0.81 and 0.62-0.75, respectively. Our results showed that deep learning could automate the detection of small vessel disease lesions and including multimodal MR data (particularly QSM) can improve the detection of CMB and non-hemorrhage iron deposits with sensitivity and precision that is compatible with use in large-scale research studies.
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Affiliation(s)
- Tanweer Rashid
- Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC), Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), San Antonio, TX, USA.
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.
| | - Ahmed Abdulkadir
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Ilya M Nasrallah
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Hospital of University of Pennsylvania, Perelman School of Medicine of the University of Pennsylvania, Philadelphia, PA, USA
| | - Jeffrey B Ware
- Department of Radiology, Hospital of University of Pennsylvania, Perelman School of Medicine of the University of Pennsylvania, Philadelphia, PA, USA
| | - Hangfan Liu
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
| | | | - J Rafael Romero
- Department of Neurology, School of Medicine, Boston University, Boston, MA, USA
| | - R Nick Bryan
- Department of Radiology, Hospital of University of Pennsylvania, Perelman School of Medicine of the University of Pennsylvania, Philadelphia, PA, USA
- Department of Diagnostic Medicine, Dell Medical School, University of Texas at Austin, Austin, TX, USA
| | - Susan R Heckbert
- Department of Epidemiology and Cardiovascular Health Research Unit, University of Washington, Seattle, WA, USA
| | - Mohamad Habes
- Neuroimage Analytics Laboratory (NAL) and the Biggs Institute Neuroimaging Core (BINC), Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas Health Science Center San Antonio (UTHSCSA), San Antonio, TX, USA.
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.
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Myung MJ, Lee KM, Kim HG, Oh J, Lee JY, Shin I, Kim EJ, Lee JS. Novel Approaches to Detection of Cerebral Microbleeds: Single Deep Learning Model to Achieve a Balanced Performance. J Stroke Cerebrovasc Dis 2021; 30:105886. [PMID: 34175642 DOI: 10.1016/j.jstrokecerebrovasdis.2021.105886] [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: 03/05/2021] [Revised: 05/05/2021] [Accepted: 05/08/2021] [Indexed: 10/21/2022] Open
Abstract
PURPOSE Cerebral microbleeds (CMBs) are considered essential indicators for the diagnosis of cerebrovascular disease and cognitive disorders. Traditionally, CMBs are manually interpreted based on criteria including the shape, diameter, and signal characteristics after an MR examination, such as susceptibility-weighted imaging or gradient echo imaging (GRE). In this paper, an efficient method for CMB detection in GRE scans is presented. MATERIALS AND METHODS The proposed framework consists of the following phases: (1) pre-processing (skull extraction), (2) the first training with the ground truth labeled using CMB, (3) the second training with the ground truth labeled with CMB mimicking the same subjects, and (4) post-processing (cerebrospinal fluid (CSF) filtering). The proposed technique was validated on a dataset of 1133 CBMs that consisted of 5284 images for training and 1737 images for testing. We applied a two-stage approach using a region-based CNN method based on You Only Look Once (YOLO) to investigate a novel CMB detection technique. RESULTS The sensitivity, precision, F1-score and false positive per person (FPavg) were evaluated as 80.96, 60.98, 69.57 and 6.57, 59.69, 62.70, 61.16 and 4.5, 66.90, 79.75, 72.76 and 2.15 for YOLO with a single label, YOLO with double labels, and YOLO + CSF filtering, respectively, and YOLO + CSF filtering showed the highest precision performance, F1-score and lowest FPavg. CONCLUSIONS Using proposed framework, we developed an optimized CMB learning model with low false positives and a balanced performance in clinical practice.
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Affiliation(s)
- Min Jae Myung
- Department of Radiology, Kyung Hee University College of Medicine, Kyung Hee University Hospital, #23 Kyungheedae-ro, Dongdaemun-gu, Seoul 02447, Republic of Korea.
| | - Kyung Mi Lee
- Department of Radiology, Kyung Hee University College of Medicine, Kyung Hee University Hospital, #23 Kyungheedae-ro, Dongdaemun-gu, Seoul 02447, Republic of Korea
| | - Hyug-Gi Kim
- Department of Radiology, Kyung Hee University College of Medicine, Kyung Hee University Hospital, #23 Kyungheedae-ro, Dongdaemun-gu, Seoul 02447, Republic of Korea
| | - Janghoon Oh
- Department of Biomedical Science and Technology, Graduate School, Kyung Hee University, #23 Kyungheedae-ro, Dongdaemun-gu, Seoul 02447, Republic of Korea
| | - Ji Young Lee
- Department of Medicine, Graduate School, Kyung Hee University, #23 Kyungheedae-ro, Dongdaemun-gu, Seoul 02447, Republic of Korea
| | - Ilah Shin
- Department of Radiology, Kyung Hee University College of Medicine, Kyung Hee University Hospital, #23 Kyungheedae-ro, Dongdaemun-gu, Seoul 02447, Republic of Korea
| | - Eui Jong Kim
- Department of Radiology, Kyung Hee University College of Medicine, Kyung Hee University Hospital, #23 Kyungheedae-ro, Dongdaemun-gu, Seoul 02447, Republic of Korea
| | - Jin San Lee
- Department of Neurology, Kyung Hee University College of Medicine, Kyung Hee University Hospital, #23 Kyungheedae-ro, Dongdaemun-gu, Seoul 02447, Republic of Korea
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Li T, Zou Y, Bai P, Li S, Wang H, Chen X, Meng Z, Kang Z, Zhou G. Detecting cerebral microbleeds via deep learning with features enhancement by reusing ground truth. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 204:106051. [PMID: 33831723 DOI: 10.1016/j.cmpb.2021.106051] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 03/07/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVES Cerebral microbleeds (CMBs) are cerebral small vascular diseases and are often used to diagnose symptoms such as stroke and dementia. Manual detection of cerebral microbleeds is a time-consuming and error-prone task, so the application of microbleed detection algorithms based on deep learning is of great significance. This study presents the feature enhancement technology applying to improve the performances of detecting CMBs. The primary purpose of the feature enhancement is emphasizing the meaningful features, leading deep learning network easier and correctly to optimize. METHOD In this study, we applied feature enhancement in detecting CMBs from brain MRI images. Feature enhancement enhanced specific intervals and suppressed the useless intervals of the feature map. This method was applied in SSD-512 and SSD-300 algorithm, using VGG architecture pre-trained in the ImageNet dataset. RESULTS The proposed method was applied in SSD-512. Moreover, the model was trained and tested on the sequence of SWAN images of brain MRI images. The results of the experiment demonstrate that our method effectively improves the detection performance of the SSD network in detecting CMBs. We train SSD-512 120000 iterations and test results on the test datasets, by applying the feature enhancement layer, improving the precision with 3.3% and the mAP of 2.3%. In the same way, we trained SSD-300, improving the mAP of 2.0%. 2.8% and 7.4% precision are improved by applying feature enhancement layer In ResNet-34 and MobileNet. CONCLUSIONS The proposed method achieved more effective performance, demonstrated that feature enhancement can be a helpful algorithm to enhance the deep learning model.
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Affiliation(s)
- Tianfu Li
- Guangdong Provincial Key Laboratory of Optical Information Materials and Technology & Institute of Electronic Paper Displays, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
| | - Yan Zou
- Department of Radiology, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Pengfei Bai
- Guangdong Provincial Key Laboratory of Optical Information Materials and Technology & Institute of Electronic Paper Displays, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China; National Center for International Research on Green Optoelectronics, South China Normal University, Guangzhou 510006, China.
| | - Shixiao Li
- Guangdong Provincial Key Laboratory of Optical Information Materials and Technology & Institute of Electronic Paper Displays, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
| | - Huawei Wang
- Guangdong Provincial Key Laboratory of Optical Information Materials and Technology & Institute of Electronic Paper Displays, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
| | - Xingliang Chen
- Guangdong Provincial Key Laboratory of Optical Information Materials and Technology & Institute of Electronic Paper Displays, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China
| | - Zhanao Meng
- Department of Radiology, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Zhuang Kang
- Department of Radiology, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, Guangdong, China
| | - Guofu Zhou
- Guangdong Provincial Key Laboratory of Optical Information Materials and Technology & Institute of Electronic Paper Displays, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China; National Center for International Research on Green Optoelectronics, South China Normal University, Guangzhou 510006, China; Academy of Shenzhen Guohua Optoelectronics, Shenzhen 518110, China
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Puy L, Pasi M, Rodrigues M, van Veluw SJ, Tsivgoulis G, Shoamanesh A, Cordonnier C. Cerebral microbleeds: from depiction to interpretation. J Neurol Neurosurg Psychiatry 2021; 92:jnnp-2020-323951. [PMID: 33563804 DOI: 10.1136/jnnp-2020-323951] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 12/22/2020] [Accepted: 01/04/2021] [Indexed: 11/04/2022]
Abstract
Cerebral microbleeds (CMBs) are defined as hypointense foci visible on T2*-weighted and susceptible-weighted MRI sequences. CMBs are increasingly recognised with the widespread use of MRI in healthy individuals as well as in the context of cerebrovascular disease or dementia. They can also be encountered in major critical medical conditions such as in patients requiring extracorporeal mechanical oxygenation. The advent of MRI-guided postmortem neuropathological examinations confirmed that, in the context of cerebrovascular disease, the vast majority of CMBs correspond to recent or old microhaemorrhages. Detection of CMBs is highly influenced by MRI parameters, in particular field strength, postprocessing methods used to enhance T2* contrast and three dimensional sequences. Despite recent progress, harmonising imaging parameters across research studies remains necessary to improve cross-study comparisons. CMBs are helpful markers to identify the nature and the severity of the underlying chronic small vessel disease. In daily clinical practice, presence and numbers of CMBs often trigger uncertainty for clinicians especially when antithrombotic treatments and acute reperfusion therapies are discussed. In the present review, we discuss those clinical dilemmas and address the value of CMBs as diagnostic and prognostic markers for future vascular events.
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Affiliation(s)
- Laurent Puy
- Department of Neurology, U1172 - LilNCog - Lille Neuroscience & Cognition, Univ. Lille, Inserm, CHU Lille, F-59000 Lille, France
| | - Marco Pasi
- Department of Neurology, U1172 - LilNCog - Lille Neuroscience & Cognition, Univ. Lille, Inserm, CHU Lille, F-59000 Lille, France
| | - Mark Rodrigues
- Centre for Clinical Brain Sciences, The University of Edinburgh College of Medicine and Veterinary Medicine, Edinburgh, Midlothian, UK
| | - Susanne J van Veluw
- Neurology Department, Hemorrhagic Stroke Research Program, Department of Neurology, Massachusetts General Hospital Stroke Research Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Georgios Tsivgoulis
- Second Department of Neurology, "Attikon" University Hospital, National and Kapodistrian University of Athens, School of Medicine, Athens, Greece
| | - Ashkan Shoamanesh
- Department of Medicine (Neurology), McMaster University and Population Health Research Institute, Hamilton, Ontario, Canada
| | - Charlotte Cordonnier
- Department of Neurology, U1172 - LilNCog - Lille Neuroscience & Cognition, Univ. Lille, Inserm, CHU Lille, F-59000 Lille, France
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"Omics" in traumatic brain injury: novel approaches to a complex disease. Acta Neurochir (Wien) 2021; 163:2581-2594. [PMID: 34273044 PMCID: PMC8357753 DOI: 10.1007/s00701-021-04928-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Accepted: 06/23/2021] [Indexed: 11/12/2022]
Abstract
BACKGROUND To date, there is neither any pharmacological treatment with efficacy in traumatic brain injury (TBI) nor any method to halt the disease progress. This is due to an incomplete understanding of the vast complexity of the biological cascades and failure to appreciate the diversity of secondary injury mechanisms in TBI. In recent years, techniques for high-throughput characterization and quantification of biological molecules that include genomics, proteomics, and metabolomics have evolved and referred to as omics. METHODS In this narrative review, we highlight how omics technology can be applied to potentiate diagnostics and prognostication as well as to advance our understanding of injury mechanisms in TBI. RESULTS The omics platforms provide possibilities to study function, dynamics, and alterations of molecular pathways of normal and TBI disease states. Through advanced bioinformatics, large datasets of molecular information from small biological samples can be analyzed in detail and provide valuable knowledge of pathophysiological mechanisms, to include in prognostic modeling when connected to clinically relevant data. In such a complex disease as TBI, omics enables broad categories of studies from gene compositions associated with susceptibility to secondary injury or poor outcome, to potential alterations in metabolites following TBI. CONCLUSION The field of omics in TBI research is rapidly evolving. The recent data and novel methods reviewed herein may form the basis for improved precision medicine approaches, development of pharmacological approaches, and individualization of therapeutic efforts by implementing mathematical "big data" predictive modeling in the near future.
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Magnetic resonance imaging manifestations of cerebral small vessel disease: automated quantification and clinical application. Chin Med J (Engl) 2020; 134:151-160. [PMID: 33443936 PMCID: PMC7817342 DOI: 10.1097/cm9.0000000000001299] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
The common cerebral small vessel disease (CSVD) neuroimaging features visible on conventional structural magnetic resonance imaging include recent small subcortical infarcts, lacunes, white matter hyperintensities, perivascular spaces, microbleeds, and brain atrophy. The CSVD neuroimaging features have shared and distinct clinical consequences, and the automatic quantification methods for these features are increasingly used in research and clinical settings. This review article explores the recent progress in CSVD neuroimaging feature quantification and provides an overview of the clinical consequences of these CSVD features as well as the possibilities of using these features as endpoints in clinical trials. The added value of CSVD neuroimaging quantification is also discussed for researches focused on the mechanism of CSVD and the prognosis in subjects with CSVD.
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Longitudinal Magnetic Resonance Imaging of Cerebral Microbleeds in Multiple Sclerosis Patients. Diagnostics (Basel) 2020; 10:diagnostics10110942. [PMID: 33198313 PMCID: PMC7697968 DOI: 10.3390/diagnostics10110942] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 10/30/2020] [Accepted: 11/09/2020] [Indexed: 01/21/2023] Open
Abstract
We hypothesized that cerebral microbleeds (CMBs) in multiple sclerosis (MS) patients will be detected with higher prevalence compared to healthy controls (HC) and that quantitative susceptibility mapping (QSM) will help remove false positives seen in susceptibility weighted imaging (SWI). A cohort of 100 relapsing remitting MS subjects scanned at 3T were used to validate a set of CMB detection guidelines specifically using QSM. A second longitudinal cohort of 112 MS and 25 HCs, also acquired at 3T, was reviewed across two time points. Both cohorts were imaged with SWI and fluid attenuated inversion recovery. Fourteen subjects in the first cohort (14%, 95% CI 8-21%) and twenty-one subjects in the second cohort (18.7%, 95% CI 11-27%) had at least one CMB. The combined information from SWI and QSM allowed us to discern stable CMBs and new CMBs from potential mimics and evaluate changes over time. The longitudinal results demonstrated that longer disease duration increased the chance to develop new CMBs. Higher age was also associated with increased CMB prevalence for MS and HC. We observed that MS subjects developed new CMBs between time points, indicating the need for longitudinal quantitative imaging of CMBs.
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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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Al-Masni MA, Kim WR, Kim EY, Noh Y, Kim DH. A Two Cascaded Network Integrating Regional-based YOLO and 3D-CNN for Cerebral Microbleeds Detection. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:1055-1058. [PMID: 33018167 DOI: 10.1109/embc44109.2020.9176073] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Cerebral Microbleeds (CMBs) are small chronic brain hemorrhages, which have been considered as diagnostic indicators for different cerebrovascular diseases including stroke, dysfunction, dementia, and cognitive impairment. In this paper, we propose a fully automated two-stage integrated deep learning approach for efficient CMBs detection, which combines a regional-based You Only Look Once (YOLO) stage for potential CMBs candidate detection and three-dimensional convolutional neural networks (3D-CNN) stage for false positives reduction. Both stages are conducted using the 3D contextual information of microbleeds from the MR susceptibility-weighted imaging (SWI) and phase images. However, we average the adjacent slices of SWI and complement the phase images independently and utilize them as a two- channel input for the regional-based YOLO method. The results in the first stage show that the proposed regional-based YOLO efficiently detected the CMBs with an overall sensitivity of 93.62% and an average number of false positives per subject (FPavg) of 52.18 throughout the five-folds cross-validation. The 3D-CNN based second stage further improved the detection performance by reducing the FPavg to 1.42. The outcomes of this work might provide useful guidelines towards applying deep learning algorithms for automatic CMBs detection.
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Al-Masni MA, Kim WR, Kim EY, Noh Y, Kim DH. Automated detection of cerebral microbleeds in MR images: A two-stage deep learning approach. Neuroimage Clin 2020; 28:102464. [PMID: 33395960 PMCID: PMC7575881 DOI: 10.1016/j.nicl.2020.102464] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 10/05/2020] [Accepted: 10/06/2020] [Indexed: 11/26/2022]
Abstract
Cerebral Microbleeds (CMBs) are small chronic brain hemorrhages, which have been considered as diagnostic indicators for different cerebrovascular diseases including stroke, dysfunction, dementia, and cognitive impairment. However, automated detection and identification of CMBs in Magnetic Resonance (MR) images is a very challenging task due to their wide distribution throughout the brain, small sizes, and the high degree of visual similarity between CMBs and CMB mimics such as calcifications, irons, and veins. In this paper, we propose a fully automated two-stage integrated deep learning approach for efficient CMBs detection, which combines a regional-based You Only Look Once (YOLO) stage for potential CMBs candidate detection and three-dimensional convolutional neural networks (3D-CNN) stage for false positives reduction. Both stages are conducted using the 3D contextual information of microbleeds from the MR susceptibility-weighted imaging (SWI) and phase images. However, we average the adjacent slices of SWI and complement the phase images independently and utilize them as a two-channel input for the regional-based YOLO method. This enables YOLO to learn more reliable and representative hierarchal features and hence achieve better detection performance. The proposed work was independently trained and evaluated using high and low in-plane resolution data, which contained 72 subjects with 188 CMBs and 107 subjects with 572 CMBs, respectively. The results in the first stage show that the proposed regional-based YOLO efficiently detected the CMBs with an overall sensitivity of 93.62% and 78.85% and an average number of false positives per subject (FPavg) of 52.18 and 155.50 throughout the five-folds cross-validation for both the high and low in-plane resolution data, respectively. These findings outperformed results by previously utilized techniques such as 3D fast radial symmetry transform, producing fewer FPavg and lower computational cost. The 3D-CNN based second stage further improved the detection performance by reducing the FPavg to 1.42 and 1.89 for the high and low in-plane resolution data, respectively. The outcomes of this work might provide useful guidelines towards applying deep learning algorithms for automatic CMBs detection.
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Affiliation(s)
- Mohammed A Al-Masni
- Department of Electrical and Electronic Engineering, College of Engineering, Yonsei University, Seoul, Republic of Korea.
| | - Woo-Ram Kim
- Neuroscience Research Institute, Gachon University, Incheon, Republic of Korea
| | - Eung Yeop Kim
- Department of Radiology, Gachon University College of Medicine, Gil Medical Center, Incheon, Republic of Korea
| | - Young Noh
- Department of Neurology, Gachon University College of Medicine, Gil Medical Center, Incheon, Republic of Korea.
| | - Dong-Hyun Kim
- Department of Electrical and Electronic Engineering, College of Engineering, Yonsei University, Seoul, Republic of Korea.
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Zhao X, Zhao XM. Deep learning of brain magnetic resonance images: A brief review. Methods 2020; 192:131-140. [PMID: 32931932 DOI: 10.1016/j.ymeth.2020.09.007] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Revised: 08/22/2020] [Accepted: 09/09/2020] [Indexed: 01/24/2023] Open
Abstract
Magnetic resonance imaging (MRI) is one of the most popular techniques in brain science and is important for understanding brain function and neuropsychiatric disorders. However, the processing and analysis of MRI is not a trivial task with lots of challenges. Recently, deep learning has shown superior performance over traditional machine learning approaches in image analysis. In this survey, we give a brief review of the recent popular deep learning approaches and their applications in brain MRI analysis. Furthermore, popular brain MRI databases and deep learning tools are also introduced. The strength and weaknesses of different approaches are addressed, and challenges as well as future directions are also discussed.
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Affiliation(s)
- Xingzhong Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, China
| | - Xing-Ming Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China; Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, China; Research Institute of Intelligent Complex Systems, Fudan University, Shanghai 200433, China.
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Smith LGF, Milliron E, Ho ML, Hu HH, Rusin J, Leonard J, Sribnick EA. Advanced neuroimaging in traumatic brain injury: an overview. Neurosurg Focus 2019; 47:E17. [PMID: 32364704 DOI: 10.3171/2019.9.focus19652] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Traumatic brain injury (TBI) is a common condition with many potential acute and chronic neurological consequences. Standard initial radiographic evaluation includes noncontrast head CT scanning to rapidly evaluate for pathology that might require intervention. The availability of fast, relatively inexpensive CT imaging has fundamentally changed the clinician's ability to noninvasively visualize neuroanatomy. However, in the context of TBI, limitations of head CT without contrast include poor prognostic ability, inability to analyze cerebral perfusion status, and poor visualization of underlying posttraumatic changes to brain parenchyma. Here, the authors review emerging advanced imaging for evaluation of both acute and chronic TBI and include QuickBrain MRI as an initial imaging modality. Dynamic susceptibility-weighted contrast-enhanced perfusion MRI, MR arterial spin labeling, and perfusion CT are reviewed as methods for examining cerebral blood flow following TBI. The authors evaluate MR-based diffusion tensor imaging and functional MRI for prognostication of recovery post-TBI. Finally, MR elastography, MR spectroscopy, and convolutional neural networks are examined as future tools in TBI management. Many imaging technologies are being developed and studied in TBI, and some of these may hold promise in improving the understanding and management of TBI. ABBREVIATIONS ASL = arterial spin labeling; CNN = convolutional neural network; CTP = perfusion CT; DAI = diffuse axonal injury; DMN = default mode network; DOC = disorders of consciousness; DTI = diffusion tensor imaging; FA = fractional anisotropy; fMRI = functional MRI; GCS = Glasgow Coma Scale; MD = mean diffusivity; MRE = MR elastography; MRS = MR spectroscopy; mTBI = mild TBI; NAA = N-acetylaspartate; SWI = susceptibility-weighted imaging; TBI = traumatic brain injury; UHF = ultra-high field.
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Affiliation(s)
| | - Eric Milliron
- 2The Ohio State University College of Medicine, The Ohio State University Wexner Medical Center, Columbus; and
| | | | | | | | - Jeffrey Leonard
- 1Department of Neurological Surgery and.,4Division of Neurological Surgery, Nationwide Children's Hospital, Columbus, Ohio
| | - Eric A Sribnick
- 1Department of Neurological Surgery and.,4Division of Neurological Surgery, Nationwide Children's Hospital, Columbus, Ohio
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