ArticleProceedings of SPIE--the International Society for Optical Engineering
Patch-Based Label Fusion for Automatic Multi-Atlas-Based Prostate Segmentation in MR Images.
Article in Proceedings of SPIE--the International Society for Optical Engineering. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- MRI-based prostate and dominant lesion segmentation using cascaded scoring convolutional neural network.Medical physics · 2022Article
- Deeply supervised 3D fully convolutional networks with group dilated convolution for automatic MRI prostate segmentation.Medical physics · 2019Article
- A Patch-based CBCT Scatter Artifact Correction Using Prior CT.Proceedings of SPIE--the International Society for Optical Engineering · 2017Article
- Pseudo CT Estimation from MRI Using Patch-based Random Forest.Proceedings of SPIE--the International Society for Optical Engineering · 2017Article
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Authors and funding
6 authors.
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Abstract
In this paper, we propose a 3D multi-atlas-based prostate segmentation method for MR images, which utilizes patch-based label fusion strategy. The atlases with the most similar appearance are selected to serve as the best subjects in the label fusion. A local patch-based atlas fusion is performed using voxel weighting based on anatomical signature. This segmentation technique was validated with a clinical study of 13 patients and its accuracy was assessed using the physicians' manual segmentations (gold standard). Dice volumetric overlapping was used to quantify the difference between the automatic and manual segmentation. In summary, we have developed a new prostate MR segmentation approach based on nonlocal patch-based label fusion, demonstrated its clinical feasibility, and validated its accuracy with manual segmentations.
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