Statistical shape models for 3D medical image segmentation: A review

Heimann, Tobias; Meinzer, Hans Peter

doi:10.1016/j.media.2009.05.004

Cited by 1,219 publications

(784 citation statements)

References 254 publications

(225 reference statements)

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“…The template surface mesh can be used to generate a personalised patient-specific mesh for biomechanical simulation (Lamata et al, 2011). The statistical shape model can be used as prior knowledge to guide image segmentation (Heimann and Meinzer, 2009). The fibre orientation atlas has the potential to be be adapted to a patient image to enable cardiac electromechanical modelling (Marchesseau et al, 2013).…”

Section: Related Workmentioning

confidence: 99%

A bi-ventricular cardiac atlas built from 1000+ high resolution MR images of healthy subjects and an analysis of shape and motion

Bai

Shi

Marvao

et al. 2015

Medical Image Analysis

123

116

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Section: Related Workmentioning

confidence: 99%

A bi-ventricular cardiac atlas built from 1000+ high resolution MR images of healthy subjects and an analysis of shape and motion

Bai

Shi

Marvao

et al. 2015

Medical Image Analysis

123

116

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“…Some of the most common algorithms are simple thresholding (global or spatially varying), clustering, level sets (Osher and Sethian, 1988), active contours (snakes) (Kass et al, 1988), region growing algorithms (Adams and Bischof, 1994), the watershed transform (Digabel and Lantuejoul, 1977;Roerdink and Meijster, 2000), classification-based algorithms, graph cuts (Shi and Malik, 2000), segmentation by registration to templates or atlases and segmentation based on (statistical) shape models (Cootes et al, 1995;Heimann and Meinzer, 2009). Segmentation is still an active area of research, and there is no single segmentation algorithm yet found that can solve all problems.…”

Section: Image Segmentationmentioning

confidence: 99%

Medical image processing on the GPU – Past, present and future

Eklund

Dufort

Forsberg

et al. 2013

Medical Image Analysis

347

198

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Graphics processing units (GPUs) are used today in a wide range of applications, mainly because they can dramatically accelerate parallel computing, are affordable and energy efficient. In the field of medical imaging, GPUs are in some cases crucial for enabling practical use of computationally demanding algorithms. This review presents the past and present work on GPU accelerated medical image processing, and is meant to serve as an overview and introduction to existing GPU implementations. The review covers GPU acceleration of basic image processing operations (filtering, interpolation, histogram estimation and distance transforms), the most commonly used algorithms in medical imaging (image registration, image segmentation and image denoising) and algorithms that are specific to individual modalities (CT, PET, SPECT, MRI, fMRI, DTI, ultrasound, optical imaging and microscopy). The review ends by highlighting some future possibilities and challenges.

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“…In fact, the prior information on a shape class lies in the residual transformation r after factoring out g corresponding to extrinsic factors from the transformation τ. Based on this, most existing shape prior models [22], e.g., the well-known active shape/appearance models (ASMs/AAMs) [16,15], are built by first aligning all the training samples into a reference space (to factor out the similarity group) and then learning the shape distribution on these registered samples.…”

Section: Main Obstacle -Extrinsic Factorsmentioning

confidence: 99%

“…It is a fundamental problem in computer vision, computer graphics, medical image analysis and has been widely investigated in numerous important applications such as 3D surface matching and reconstruction [5,32,12,30,7,21], statistical shape modeling and knowledge-based segmentation [16,15,22,34], feature correspondence and image registration [28,38,1,20], shape similarity and object recognition [2,3,29]. Let S ⊂ R 3 denote a shape 1 .…”

Section: Introductionmentioning

confidence: 99%

Modeling Shapes with Higher-Order Graphs: Methodology and Applications

Wang

Zeng

Samaras

et al. 2013

Shape Perception in Human and Computer Vision

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Extrinsic factors such as object pose and camera parameters are a main source of shape variability and pose an obstacle to efficiently solving shape matching and inference. Most existing methods address the influence of extrinsic factors by decomposing the transformation of the source shape (model) into two parts: one corresponding to the extrinsic factors and the other accounting for intra-class variability and noise, which are solved in a successive or alternating manner. In this chapter, we consider a methodology to circumvent the influence of extrinsic factors by exploiting shape properties that are invariant to them. Based on higher-order graph-based models, we implement such a methodology to address various important vision problems, such as non-rigid 3D surface matching and knowledge-based 3D segmentation, in a one-shot optimization scheme. Experimental results demonstrate the superior performance and potential of this type of approach.

show abstract

Statistical shape models for 3D medical image segmentation: A review

Cited by 1,219 publications

References 254 publications

A bi-ventricular cardiac atlas built from 1000+ high resolution MR images of healthy subjects and an analysis of shape and motion

A bi-ventricular cardiac atlas built from 1000+ high resolution MR images of healthy subjects and an analysis of shape and motion

Medical image processing on the GPU – Past, present and future

Modeling Shapes with Higher-Order Graphs: Methodology and Applications

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