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

被引:620
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
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 167 条
[1]   Efficacy of Texture, Shape, and Intensity Feature Fusion for Posterior-Fossa Tumor Segmentation in MRI [J].
Ahmed, Shaheen ;
Iftekharuddin, Khan M. ;
Vossough, Arastoo .
IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, 2011, 15 (02) :206-213
[2]   Computer-Aided Detection of Metastatic Brain Tumors Using Automated Three-Dimensional Template Matching [J].
Ambrosini, Robert D. ;
Wang, Peng ;
O'Dell, Walter G. .
JOURNAL OF MAGNETIC RESONANCE IMAGING, 2010, 31 (01) :85-93
[3]   Cost function masking during normalization of brains with focal lesions: Still a necessity? [J].
Andersen, Sarah M. ;
Rapcsak, Steven Z. ;
Beeson, Pelagie M. .
NEUROIMAGE, 2010, 53 (01) :78-84
[4]   Glioma dynamics and computational models:: a review of segmentation, registration, and in silico growth algorithms and their clinical applications [J].
Angelini, Elsa D. ;
Clatz, Olivier ;
Mandonnet, Emmanuel ;
Konukoglu, Ender ;
Capelle, Laurent ;
Duffau, Hugues .
CURRENT MEDICAL IMAGING REVIEWS, 2007, 3 (04) :262-276
[5]   Differential MRI analysis for quantification of low grade glioma growth [J].
Angelini, Elsa D. ;
Delon, Julie ;
Bah, Alpha Boubacar ;
Capelle, Laurent ;
Mandonnet, Emmanuel .
MEDICAL IMAGE ANALYSIS, 2012, 16 (01) :114-126
[6]  
[Anonymous], HDB PATTERN RECOGNIT
[7]  
[Anonymous], COMPUT METHODS PROGR
[8]  
[Anonymous], 2005, P 4 INT C MACH LEARN
[9]  
[Anonymous], MICCAI WORKSH COMP I
[10]  
[Anonymous], RECENT RESULTS CANC