A survey of prostate segmentation methodologies in ultrasound, magnetic resonance and computed tomography images

被引:157
作者
Ghose, Soumya [1 ,2 ]
Oliver, Arnau [2 ]
Marti, Robert [2 ]
Llado, Xavier [2 ]
Vilanova, Joan C. [3 ]
Freixenet, Jordi [2 ]
Mitra, Jhimli [1 ,2 ]
Sidibe, Desire [1 ]
Meriaudeau, Fabrice [1 ]
机构
[1] Univ Bourgogne, CNRS, Lab Le2I, UMR 6306, F-71200 Le Creusot, France
[2] Univ Girona, Comp Vis & Robot Grp, Girona 17071, Spain
[3] Girona Magnet Resonance Ctr, Girona 17002, Spain
关键词
Prostate gland segmentation methods; TRUS images; MR images; CT images; ACTIVE SHAPE MODELS; 3D MR-IMAGES; AUTOMATIC SEGMENTATION; EDGE-DETECTION; SEMIAUTOMATIC SEGMENTATION; BOUNDARY DELINEATION; STATISTICAL SHAPE; MEDICAL IMAGERY; GRAPH SEARCH; 3-D PROSTATE;
D O I
10.1016/j.cmpb.2012.04.006
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Prostate segmentation is a challenging task, and the challenges significantly differ from one imaging modality to another. Low contrast, speckle, micro-calcifications and imaging artifacts like shadow poses serious challenges to accurate prostate segmentation in transrectal ultrasound (TRUS) images. However in magnetic resonance (MR) images, superior soft tissue contrast highlights large variability in shape, size and texture information inside the prostate. In contrast poor soft tissue contrast between prostate and surrounding tissues in computed tomography (CT) images pose a challenge in accurate prostate segmentation. This article reviews the methods developed for prostate gland segmentation TRUS, MR and CT images, the three primary imaging modalities that aids prostate cancer diagnosis and treatment. The objective of this work is to study the key similarities and differences among the different methods, highlighting their strengths and weaknesses in order to assist in the choice of an appropriate segmentation methodology. We define a new taxonomy for prostate segmentation strategies that allows first to group the algorithms and then to point out the main advantages and drawbacks of each strategy. We provide a comprehensive description of the existing methods in all TRUS, MR and CT modalities, highlighting their key-points and features. Finally, a discussion on choosing the most appropriate segmentation strategy for a given imaging modality is provided. A quantitative comparison of the results as reported in literature is also presented. (c) 2012 Elsevier Ireland Ltd. All rights reserved.
引用
收藏
页码:262 / 287
页数:26
相关论文
共 138 条
[31]  
Ding M., 2006, P 27 ANN INT C ENG M, P662
[32]   Prostate segmentation in 3D US images using the Cardinal-spline based discrete dynamic contour [J].
Ding, MY ;
Chen, CJ ;
Wang, YQ ;
Gyacskov, I ;
Fenster, A .
MEDICAL IMAGING 2003: VISUALIZATION, IMAGE-GUIDED PROCEDURES, AND DISPLAY, 2003, 5029 :69-76
[33]   Particle filtering [J].
Djuric, PM ;
Kotecha, JH ;
Zhang, JQ ;
Huang, YF ;
Ghirmai, T ;
Bugallo, MF ;
Míguez, J .
IEEE SIGNAL PROCESSING MAGAZINE, 2003, 20 (05) :19-38
[34]  
Dowling J., 2009, AUTOMATIC ATLAS BASE
[35]  
Dowling JA, 2011, LECT NOTES COMPUT SC, V6963, P10, DOI 10.1007/978-3-642-23944-1_2
[36]  
Feng Q, 2010, MED PHYS, V37
[37]   SEGMENTING CT PROSTATE IMAGES USING POPULATION AND PATIENT-SPECIFIC STATISTICS FOR RADIOTHERAPY [J].
Feng, Qianjin ;
Foskey, Mark ;
Tang, Songyuan ;
Chen, Wufan ;
Shen, Dinggang .
2009 IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: FROM NANO TO MACRO, VOLS 1 AND 2, 2009, :282-+
[38]  
Firjani A, 2011, I S BIOMED IMAGING, P1476, DOI 10.1109/ISBI.2011.5872679
[39]  
Firjany A, 2010, LECT NOTES COMPUT SC, V6367, P121, DOI 10.1007/978-3-642-15989-3_14
[40]   Robust automatic segmentation of multiple implanted cylindrical gold fiducial markers in cone-beam CT projections [J].
Fledelius, Walther ;
Worm, Esben ;
Elstrom, Ulrik V. ;
Petersen, Jorgen B. ;
Grau, Cai ;
Hoyer, Morten ;
Poulsen, Per R. .
MEDICAL PHYSICS, 2011, 38 (12) :6351-6361