Graph Search with Appearance and Shape Information for 3-D Prostate and Bladder Segmentation

Song, Qi; Liu, Yinxiao; Liu, Yunlong; Saha, Punam K.; Sonka, Milan; Wu, Xiaodong

doi:10.1007/978-3-642-15711-0_22

Cited by 20 publications

(41 citation statements)

References 21 publications

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“…Finally, note that shape and appearance features can be combined, as in the studies by Zouqi and Samarabandu [74], Song et al [75], Yuan et al [76] and Qiu et al [77].…”

Section: Appearancementioning

confidence: 96%

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A survey of prostate modeling for image analysis

Chilali¹,

Ouzzane²,

Diaf³

et al. 2014

Computers in Biology and Medicine

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Computer technology is widely used for multimodal image analysis of the prostate gland. Several techniques have been developed, most of which incorporate a priori knowledge extracted from organ features. Knowledge extraction and modeling are multi-step tasks. Here, we review these steps and classify the modeling according to the data analysis methods employed and the features used. We conclude with a survey of some clinical applications where these techniques are employed.

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“…Finally, note that shape and appearance features can be combined, as in the studies by Zouqi and Samarabandu [74], Song et al [75], Yuan et al [76] and Qiu et al [77].…”

Section: Appearancementioning

confidence: 96%

“…Principal components analysis ASM AAM Segmentation [22,36,55,56,66,100] Automatic initialization of ASM Segmentation [49,119,150,151,152] Segmentation and registration [23,24] [104] Shape motion model [28,79] Segmentation [75] Segmentation [38] Principal geodesic analysis…”

Section: Analysis Techniquesmentioning

confidence: 99%

A survey of prostate modeling for image analysis

Chilali¹,

Ouzzane²,

Diaf³

et al. 2014

Computers in Biology and Medicine

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show abstract

“…A specific links topology allows to constrain that the distance between the 2 surfaces in the competition areas lies in a specific interval (see [7] for more detail). This constraint allows to prevent the leakage between surfaces in the interacting area.…”

Section: E Multiple Organs Osd: Initial Organs Meshes and Graph Consmentioning

confidence: 99%

Adaptation and evaluation of the multiple organs OSD for T2 MRI prostate segmentation

Garnier

Alirezaie

et al. 2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Abstract-This paper deals with the adaptation, the tuning and the evaluation of the multiple organs Optimal Surface Detection (OSD) algorithm for the T2 MRI prostate segmentation. This algorithm is initialized by first surface approximations of the prostate (obtained after a model adjustment), the bladder (obtained automatically) and the rectum (interactive geometrical model). These three organs are then segmented together in a multiple organs OSD scheme which proposes a competition between the gray level characteristics and some topological and anatomical information of these three organs. This method has been evaluated on the MICCAI Grand Challenge: Prostate MR Image Segmentation (PROMISE) 2012 training dataset.

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“…[2][3][4][5][6][7][8][9][10][11] It is, however, very difficult to incorporate prior shape knowledge into graph-cut based approaches. The approaches of Slabaugh and Unal 6 and Zhu-Jacquot, 8 tried to spatially constrain the graph-cut segmentation by incorporating parametric shape information.…”

Section: Introductionmentioning

confidence: 99%

A shape prior-based MRF model for 3D masseter muscle segmentation

et al. 2012

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Medical image segmentation is generally an ill-posed problem that can only be solved by incorporating prior knowledge. The ambiguities arise due to the presence of noise, weak edges, imaging artifacts, inhomogeneous interior and adjacent anatomical structures having similar intensity profile as the target structure. In this paper we propose a novel approach to segment the masseter muscle using the graph-cut incorporating additional 3D shape priors in CT datasets, which is robust to noise; artifacts; and shape deformations. The main contribution of this paper is in translating the 3D shape knowledge into both unary and pairwise potentials of the Markov Random Field (MRF). The segmentation task is casted as a Maximum-A-Posteriori (MAP) estimation of the MRF. Graph-cut is then used to obtain the global minimum which results in the segmentation of the masseter muscle. The method is tested on 21 CT datasets of the masseter muscle, which are noisy with almost all possessing mild to severe imaging artifacts such as high-density artifacts caused by e.g. the very common dental fillings and dental implants. We show that the proposed technique produces clinically acceptable results to the challenging problem of muscle segmentation, and further provide a quantitative and qualitative comparison with other methods. We statistically show that adding additional shape prior into both unary and pairwise potentials can increase the robustness of the proposed method in noisy datasets.

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Graph Search with Appearance and Shape Information for 3-D Prostate and Bladder Segmentation

Cited by 20 publications

References 21 publications

A survey of prostate modeling for image analysis

A survey of prostate modeling for image analysis

Adaptation and evaluation of the multiple organs OSD for T2 MRI prostate segmentation

A shape prior-based MRF model for 3D masseter muscle segmentation

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