A survey of MRI-based medical image analysis for brain tumor studies

被引:618
|
作者
Bauer, Stefan [1 ]
Wiest, Roland [2 ]
Nolte, Lutz-P [1 ]
Reyes, Mauricio [1 ]
机构
[1] Univ Bern, Inst Surg Technol & Biomech, CH-3012 Bern, Switzerland
[2] Univ Hosp Bern, Inselspital, Univ Inst Diagnost & Intervent Neuroradiol, SCAN, Bern, Switzerland
来源
PHYSICS IN MEDICINE AND BIOLOGY | 2013年 / 58卷 / 13期
基金
瑞士国家科学基金会;
关键词
COMPUTER-AIDED DETECTION; ATLAS-BASED SEGMENTATION; MAGNETIC-RESONANCE-SPECTROSCOPY; AUTOMATIC SEGMENTATION; DEFORMABLE REGISTRATION; TISSUE CHARACTERIZATION; NONRIGID REGISTRATION; SUBJECT REGISTRATION; VOLUME DETERMINATION; GLIOMA GROWTH;
D O I
10.1088/0031-9155/58/13/R97
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
MRI-based medical image analysis for brain tumor studies is gaining attention in recent times due to an increased need for efficient and objective evaluation of large amounts of data. While the pioneering approaches applying automated methods for the analysis of brain tumor images date back almost two decades, the current methods are becoming more mature and coming closer to routine clinical application. This review aims to provide a comprehensive overview by giving a brief introduction to brain tumors and imaging of brain tumors first. Then, we review the state of the art in segmentation, registration and modeling related to tumor-bearing brain images with a focus on gliomas. The objective in the segmentation is outlining the tumor including its sub-compartments and surrounding tissues, while the main challenge in registration and modeling is the handling of morphological changes caused by the tumor. The qualities of different approaches are discussed with a focus on methods that can be applied on standard clinical imaging protocols. Finally, a critical assessment of the current state is performed and future developments and trends are addressed, giving special attention to recent developments in radiological tumor assessment guidelines.
引用
收藏
页码:R97 / R129
页数:33
相关论文
共 50 条
  • [21] Performance comparison of deep learning models for MRI-based brain tumor detection
    Alsufyani, Abdulmajeed
    AIMS BIOENGINEERING, 2025, 12 (01): : 1 - 21
  • [22] A survey on machine learning based brain retrieval algorithms in medical image analysis
    Arpit Kumar Sharma
    Amita Nandal
    Arvind Dhaka
    Rahul Dixit
    Health and Technology, 2020, 10 : 1359 - 1373
  • [23] A survey on machine learning based brain retrieval algorithms in medical image analysis
    Sharma, Arpit Kumar
    Nandal, Amita
    Dhaka, Arvind
    Dixit, Rahul
    HEALTH AND TECHNOLOGY, 2020, 10 (06) : 1359 - 1373
  • [24] Brain Tumor Image Segmentation in MRI Image
    Tjahyaningtijas, Hapsari Peni Agustin
    2ND INTERNATIONAL CONFERENCE ON VOCATIONAL EDUCATION AND ELECTRICAL ENGINEERING (ICVEE), 2018, 336
  • [25] Cross-Modal Distillation to Improve MRI-Based Brain Tumor Segmentation With Missing MRI Sequences
    Rahimpour, Masoomeh
    Bertels, Jeroen
    Radwan, Ahmed
    Vandermeulen, Henri
    Sunaert, Stefan
    Vandermeulen, Dirk
    Maes, Frederik
    Goffin, Karolien
    Koole, Michel
    IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2022, 69 (07) : 2153 - 2164
  • [26] MRI-based knee image for personal identification
    Shamir, Lior
    INTERNATIONAL JOURNAL OF BIOMETRICS, 2013, 5 (02) : 113 - 125
  • [27] YOUNG-ADULT HUMAN BRAIN - AN MRI-BASED MORPHOMETRIC ANALYSIS
    FILIPEK, PA
    RICHELME, C
    KENNEDY, DN
    CAVINESS, VS
    CEREBRAL CORTEX, 1994, 4 (04) : 344 - 360
  • [28] A Simultaneous Method for MRI-based Partial Volume Correction and Image Registration in Brain PET
    Ibaraki, M.
    Matsubara, K.
    Kinoshita, T.
    EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING, 2017, 44 : S424 - S425
  • [29] A Survey of Automatic MRI based Brain Tumor Segmentation Techniques
    Subashree, M.
    Sangeetha, J.
    PROCEEDINGS OF THE 2018 3RD INTERNATIONAL CONFERENCE ON INVENTIVE COMPUTATION TECHNOLOGIES (ICICT 2018), 2018, : 157 - 161
  • [30] A robust MRI-based brain tumor classification via a hybrid deep learning technique
    Shaimaa E. Nassar
    Ibrahim Yasser
    Hanan M. Amer
    Mohamed A. Mohamed
    The Journal of Supercomputing, 2024, 80 : 2403 - 2427