Automated characterisation of cerebral microbleeds using their size and spatial distribution on brain MRI

被引:0
|
作者
Sundaresan, Vaanathi [1 ]
Zamboni, Giovanna [2 ,3 ]
Dineen, Robert A. [4 ,5 ]
Auer, Dorothee P. [4 ,5 ]
Sotiropoulos, Stamatios N. [2 ,4 ,6 ]
Sprigg, Nikola [5 ]
Jenkinson, Mark [2 ,6 ,7 ]
Griffanti, Ludovica [2 ,6 ,8 ]
机构
[1] Indian Inst Sci, Dept Computat & Data Sci, Bengaluru 560012, Karnataka, India
[2] Univ Oxford, Nuffield Dept Clin Neurosci, Oxford OX3 9DU, England
[3] Univ Modena & Reggio Emilia, Dipartimento Sci Biomed Metab & Neurosci, I-41121 Modena, Italy
[4] Univ Nottingham, Natl Inst Hlth & Care Res NIHR, Nottingham Biomed Res Ctr, Queens Med Ctr,Sir Peter Mansfield Imaging Ctr, Nottingham NG7 2RD, England
[5] Univ Nottingham, Sch Med, Radiol Sci Mental Hlth & Clin Neurosci, Nottingham NG7 2RD, England
[6] Univ Oxford, Wellcome Ctr Integrat Neuroimaging, Oxford OX3 9DU, England
[7] Univ Adelaide, South Australian Hlth & Med Res Inst SAHMRI, Australian Inst Machine Learning, Sch Comp & Math Sci, Adelaide, SA 5005, Australia
[8] Univ Oxford, Dept Psychiat, Oxford OX3 7JX, England
关键词
Brain; Cerebral haemorrhage; Cerebrovascular disorders; Hemosiderin; Magnetic resonance imaging; ROBUST; MODEL;
D O I
10.1186/s41747-024-00544-z
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
Cerebral microbleeds (CMBs) are small, hypointense hemosiderin deposits in the brain measuring 2-10 mm in diameter. As one of the important biomarkers of small vessel disease, they have been associated with various neurodegenerative and cerebrovascular diseases. Hence, automated detection, and subsequent extraction of clinically useful metrics (e.g., size and spatial distribution) from CMBs are essential for investigating their clinical impact, especially in large-scale studies. While some work has been done for CMB segmentation, extraction of clinically relevant information is not yet explored. Herein, we propose the first automated method to characterise CMBs using their size and spatial distribution, i.e., CMB count in three regions (and their substructures) used in Microbleed Anatomical Rating Scale (MARS): infratentorial, deep, and lobar. Our method uses structural atlases of the brain for determining individual regions. On an intracerebral haemorrhage study dataset, we achieved a mean absolute error of 2.5 mm for size estimation and an overall accuracy > 90% for automated rating. The code and the atlas of MARS regions in Montreal Neurological Institute-MNI space are publicly available.
引用
收藏
页数:8
相关论文
共 50 条
  • [31] Characterisation of Endothelin-1-Induced Intrastriatal Lesions Within the Juvenile and Adult Rat Brain Using MRI and 31P MRS
    Saggu, Raman
    TRANSLATIONAL STROKE RESEARCH, 2013, 4 (03) : 351 - 367
  • [32] Corpus Callosum Shape and Size Changes in Early Alzheimer's Disease: A Longitudinal MRI Study Using the OASIS Brain Database
    Bachman, Alvin H.
    Lee, Sang Han
    Sidtis, John J.
    Ardekani, Babak A.
    JOURNAL OF ALZHEIMERS DISEASE, 2014, 39 (01) : 71 - 78
  • [33] Fully automated brain tumour segmentation system in 3D-MRI using symmetry analysis of brain and level sets
    Kermi, Adel
    Andjouh, Khaled
    Zidane, Ferhat
    IET IMAGE PROCESSING, 2018, 12 (11) : 1964 - 1971
  • [34] Automated gradient-based electrical properties tomography in the human brain using 7 Tesla MRI
    Wang, Yicun
    Van de Moortele, Pierre-Francois
    He, Bin
    MAGNETIC RESONANCE IMAGING, 2019, 63 : 258 - 266
  • [35] Frequency and Distribution of Perinatal Arterial Ischemic Stroke in a Cohort of Patients With Cerebral Palsy Using Delayed MRI
    Jalloul, Mohammad
    Venkatakrishna, Shyam Sunder B.
    Alves, Cesar Augusto P.
    Curic, Jelena
    Andronikou, Savvas
    JOURNAL OF COMPUTER ASSISTED TOMOGRAPHY, 2025, 49 (02) : 327 - 331
  • [36] Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI
    Soltaninejad, Mohammadreza
    Yang, Guang
    Lambrou, Tryphon
    Allinson, Nigel
    Jones, Timothy L.
    Barrick, Thomas R.
    Howe, Franklyn A.
    Ye, Xujiong
    INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY, 2017, 12 (02) : 183 - 203
  • [37] Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI
    Mohammadreza Soltaninejad
    Guang Yang
    Tryphon Lambrou
    Nigel Allinson
    Timothy L. Jones
    Thomas R. Barrick
    Franklyn A. Howe
    Xujiong Ye
    International Journal of Computer Assisted Radiology and Surgery, 2017, 12 : 183 - 203
  • [38] Automated Multimodal Brain Tumor Segmentation and Localization in MRI Images Using Hybrid Res2-UNeXt
    Nehru, V.
    Prabhu, V.
    JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY, 2024, 19 (05) : 3485 - 3497
  • [39] A Review on Breast Cancer Brain Metastasis: Automated MRI Image Analysis for the Prediction of Primary Cancer Using Radiomics
    Tzardis, Vangelis
    Kyriacou, Efthyvoulos
    Loizou, Christos P.
    Constantinidou, Anastasia
    COMPUTER ANALYSIS OF IMAGES AND PATTERNS, CAIP 2021, PT 1, 2021, 13052 : 245 - 255
  • [40] Assistance to neurosurgical planning: Using a fuzzy spatial graph model of the brain for locating anatomical targets in MRI
    Villeger, Alice
    Ouchchane, Lemlih
    Lemaire, Jean-Jacques
    Boire, Jean-Yves
    MEDICAL IMAGING 2007: IMAGE PROCESSING, PTS 1-3, 2007, 6512