Automatic Segmentation of Bladder and Prostate Using Coupled 3D Deformable Models

Costa, Maria Jimena; Delingette, Hervé; Novellas, Sébastien; Ayache, Nicholas

doi:10.1007/978-3-540-75757-3_31

Cited by 70 publications

(77 citation statements)

References 24 publications

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“…Deformable surfaces is another type of segmentation method which has been often used for the delineation of tri-dimensional structures [24,5]. A strong reason for using deformable surfaces is the introduction of prior knowledge about the shape of the target object [3,29,15].…”

Section: Contextmentioning

confidence: 99%

“…While, in the past, several groups have combined the power of anatomical atlases and deformable models for the segmentation of anatomical structures, they limited the use of anatomical atlases to the initialization of deformable models [5,19]. However, we think that probabilistic segmentations bring valuable information that can prevent the segmentation from deteriorating when the prostate capsule does not appears in the image or when "false boundaries" lie in the vicinity of the surface.…”

Section: Contextmentioning

confidence: 99%

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Automated segmentation of the prostate in 3D MR images using a probabilistic atlas and a spatially constrained deformable model

Martin

Troccaz

Daanen³

2010

Med. Phys.

130

101

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Purpose: We present a fully automatic algorithm for the segmentation of the prostate in three-dimensional magnetic resonance (MR) images.Method: Our approach requires the use of an anatomical atlas which is built by computing transformation fields mapping a set of manually segmented images to a common reference. These transformation fields are then applied to the manually segmented structures of the training set in order to get a probabilistic map on the atlas.The segmentation is then realized through a two stage procedure. In the first stage, the processed image is registered to the probabilistic atlas. Subsequently, a probabilistic segmentation is obtained by mapping the probabilistic map of the atlas to the patient's anatomy. In the second stage, a deformable surface evolves towards the prostate boundaries by merging information coming from the probabilistic segmentation, an image feature model and a statistical shape model. During the evolution of the surface, the probabilistic segmentation allows the introduction of a spatial constraint that prevents the deformable surface from leaking in an unlikely configuration. Results:The proposed method is evaluated on 36 exams, that were manually segmented by a single expert. A median Dice similarity coefficient of 0.86 and an average surface error of 2.41 mm are achieved.Conclusion: By merging prior knowledge, the presented method achieves a robust and completely automatic segmentation of the prostate in MR images. Results show that the use of a spatial constraint is useful to increase the robustness of the deformable model comparatively to a deformable surface that is only driven by an image appearance model.

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Section: Contextmentioning

confidence: 99%

Section: Contextmentioning

confidence: 99%

Automated segmentation of the prostate in 3D MR images using a probabilistic atlas and a spatially constrained deformable model

Martin

Troccaz

Daanen³

2010

Med. Phys.

130

101

View full text Add to dashboard Cite

show abstract

“…These challenges restrict the use of classical intensity-based segmentation methods. In addition the high intra-and inter-individual variability may cause model-based methods to fail [25]. Atlas based approaches are common methods for organ segmentation, not only for obtaining a final contour but also to provide initial organ positions for further segmentation algorithms.…”

Section: Introductionmentioning

confidence: 99%

Multi-Atlas-Based Segmentation of Pelvic Structures from CT Scans for Planning in Prostate Cancer Radiotherapy

Acosta

Dowling

Dréan

et al. 2013

Abdomen and Thoracic Imaging

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In prostate cancer radiotherapy, the accurate identification of the prostate and organs at risk in planning computer tomography (CT) images is an important part of the therapy planning and optimization. Manually contouring these organs can be a time consuming process and subject to intra-and inter-expert variability. Automatic identification of organ boundaries from these images is challenging due to the poor soft tissue contrast. Atlas-based approaches may provide a priori structural information by propagating manual expert delineations to a new individual space; however the inter-individual variability and registration errors may lead to biased results. Multiatlas approaches can partly overcome some of these difficulties by selecting the most similar atlases among a large data base but the definition of similarity measure between the available atlases and the query individual has still to be addressed. The purpose of this chapter is to explain atlas-based segmentation approaches and the evaluation of different atlas-based strategies to simultaneously segment prostate, bladder and rectum from CT images. A comparison between single and multiple atlases is performed. Experiments on atlas ranking, selection strategies and fusion decision rules are carried out to illustrate the presented methodology. Propagation of labels using two registration strategies are applied and the results of the comparison with manual delineations are reported.

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“…Rousson et al developed a method to incorporate both shape and appearance model using bayesian formulation [4]. The deformable shape model with shape prior information was employed in [5] and [6] for prostate and bladder segmentation. Recently, graph-based methods with a global optimality guarantee have attracted a lot of attention.…”

Section: Introductionmentioning

confidence: 99%

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

Song

Liu

et al. 2010

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010

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Abstract. The segmentation of soft tissues in medical images is a challenging problem due to the weak boundary, large deformation and serious mutual influence. We present a novel method incorporating both the shape and appearance information in a 3-D graph-theoretic framework to overcome those difficulties for simultaneous segmentation of prostate and bladder. An arc-weighted graph is constructed corresponding to the initial mesh. Both the boundary and region information is incorporated into the graph with learned intensity distribution, which drives the mesh to the best fit of the image. A shape prior penalty is introduced by adding weighted-arcs in the graph, which maintains the original topology of the model and constraints the flexibility of the mesh. The surface-distance constraints are enforced to avoid the leakage between prostate and bladder. The target surfaces are found by solving a maximum flow problem in low-order polynomial time. Both qualitative and quantitative results on prostate and bladder segmentation were promising, proving the power of our algorithm.

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Automatic Segmentation of Bladder and Prostate Using Coupled 3D Deformable Models

Cited by 70 publications

References 24 publications

Automated segmentation of the prostate in 3D MR images using a probabilistic atlas and a spatially constrained deformable model

Automated segmentation of the prostate in 3D MR images using a probabilistic atlas and a spatially constrained deformable model

Multi-Atlas-Based Segmentation of Pelvic Structures from CT Scans for Planning in Prostate Cancer Radiotherapy

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

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