Automatic segmentation of the prostate in 3D MR images by atlas matching using localized mutual information.

By Stefan Klein, U. A. van der Heide, I. Lips, M. van Vulpen, Marius Staring, Josien P. W. Pluim. Medical Physics (Lancaster), 2008

460 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/9f129b866e4ac0ea2cdcd254c3f38f695a876db1

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In Biomedical Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Volumetric ConvNets with Mixed Residual Connections for Automated Prostate Segmentation from 3D MR Images (2017, methodology): “Prostate segmentation Previous automated prostate segmentation methods mainly include multi-atlas based methods (Klein et al. 2008) and deformable methods (Toth and Madabhushi 2012).”
Intensity-Based Image Registration by Minimizing Residual Complexity (2010, methodology): “We used mesh size of 48, 24, and 12 pixels, respectively, for both our and Elastix implementations.”
Computer-Aided Detection of Prostate Cancer in MRI (2014, methodology): “proach similar to the one presented in [17], using the atlas se-”
Combination Strategies in Multi-Atlas Image Segmentation: Application to Brain MR Data (2009, methodology): “Moreover, in [ 20 ] majority voting and STAPLE showed mostly not statistically significant differences when combining segmentations of MR image of the prostate, and in some atlas sets majority voting performed better.”
Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy (2009, unknown): “Klein et al. (2008) use a similar approach where multi-atlas segmentation is carried out using only atlases that reach a user-defined threshold of similarity with the target.”
MS-Net: Multi-Site Network for Improving Prostate Segmentation With Heterogeneous MRI Data (2020, methodology): “Previous automated prostate segmentation methods can be broadly categorized into three branches: multi-atlas-based methods [24], deformable methods [25] and learning-based methods [1].”
Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge (2013, methodology): “The DSC was used as the evaluation measure in the SIMPLE algorithm.”
Deep Learning-based Image Segmentation on Multimodal Medical Imaging (2019, methodology): “segmentation, such as [35] (fusing the results from different atlases by majority voting) and [36] (fusing the support vector machine results from different modalities by majority”
elastix: A Toolbox for Intensity-Based Medical Image Registration (2010, methodology): “For each of the metrics above, a localized version can be constructed, as explained in [8], by selecting the appropriate sampler.”
Computer Aided-Diagnosis of Prostate Cancer on Multiparametric MRI: A Technical Review of Current Research (2014, methodology): “proposed a segmentation method based on nonrigid registration of a set of 3D labeled atlas images using localized mutual information [42].”