A Shape-Guided Deformable Model with Evolutionary Algorithm Initialization for 3D Soft Tissue Segmentation

Heimann, Tobias; Münzing, Sascha; Meinzer, Hans-Peter; Wolf, Ivo

doi:10.1007/978-3-540-73273-0_1

Cited by 89 publications

(64 citation statements)

References 18 publications

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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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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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“…However, the cardinality of the training set is proportional to the degree of natural variability of the organ shape. For example, Heimann et al selected 32 subjects out of 86 to train their 3D reference model of 2562 equally distributed landmarks, used for segmentation of the liver in CT volumes [23]. Unfortunately, such a large dataset is currently not available for tongue MRI volumes.…”

Section: Related Workmentioning

confidence: 99%

Minimally Interactive MRI Segmentation for Subject-Specific Modelling of the Tongue

Harandi

Abugharbieh

Fels

2013

Lecture Notes in Computational Vision and Biomechanics

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Subject-Specific biomechanical modelling of the human tongue is beneficial for investigating the inter-subject variability in the physiology of the speech, chewing and swallowing. Delineation of the tongue tissue from MRI is essential for modelling, but still remains a challenge due to the lack of definitive boundary features. In this paper, we propose a minimally interactive inter-subject mesh-to-image registration scheme to tackle 3D segmentation of the tongue from MR volumes. An exemplar expert-delineated template is deformed to match the target volume, constrained based on a shape matching regularization technique. We enable effective minimal user interaction by incorporating additional boundary labels in areas where automatic segmentation is deemed inadequate. We validate our method on 12 normal-subjects. Results indicate an average dice overlap of 0.904 with the ground truth, achieved within three minutes of the expert interaction.

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“…This problem becomes more critical in medical image analysis due to inadequate training samples and varied pathologies. There have been strategies to relieve the over constraint, including loosening up the shape constraints [2,3], introducing additional flexibility [4], synthesising additional training samples [5], and more recently, modifying shape prior using manifold learning [6] or sparse composition [7]. In these methods there is no obvious a priori optimal parameters and it is time consuming to tune the parameters to specific applications.…”

Section: Introductionmentioning

confidence: 99%

Active shape model unleashed with multi-scale local appearance

Zhang

Bhalerao

Helm

et al. 2015

2015 IEEE International Conference on Image Processing (ICIP)

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A Shape-Guided Deformable Model with Evolutionary Algorithm Initialization for 3D Soft Tissue Segmentation

Cited by 89 publications

References 18 publications

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

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

Minimally Interactive MRI Segmentation for Subject-Specific Modelling of the Tongue

Active shape model unleashed with multi-scale local appearance

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