ArticleScientific reports2012
Template-cut: a pattern-based segmentation paradigm.
Article in Scientific reports, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Machine learning applications in imaging analysis for patients with pituitary tumors: a review of the current literature and future directions.Pituitary · 2020Review
- Algorithm guided outlining of 105 pancreatic cancer liver metastases in Ultrasound.Scientific reports · 2017Article
- Interactive Outlining of Pancreatic Cancer Liver Metastases in Ultrasound Images.Scientific reports · 2017Article
- Shape complexes: the intersection of label orderings and star convexity constraints in continuous max-flow medical image segmentation.Journal of medical imaging (Bellingham, Wash.) · 2016Article
- Iterative-cuts: longitudinal and scale-invariant segmentation via user-defined templates for rectosigmoid colon in gynecological brachytherapy.Journal of medical imaging (Bellingham, Wash.) · 2016Article
- Refinement-cut: user-guided segmentation algorithm for translational science.Scientific reports · 2014Article
- Development of an open source software module for enhanced visualization during MR-guided interstitial gynecologic brachytherapy.SpringerPlus · 2014Article
- Article
- Pituitary adenoma volumetry with 3D Slicer.PloS one · 2012Article
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Authors and funding
4 authors.
Funding
Abstract
We present a scale-invariant, template-based segmentation paradigm that sets up a graph and performs a graph cut to separate an object from the background. Typically graph-based schemes distribute the nodes of the graph uniformly and equidistantly on the image, and use a regularizer to bias the cut towards a particular shape. The strategy of uniform and equidistant nodes does not allow the cut to prefer more complex structures, especially when areas of the object are indistinguishable from the background. We propose a solution by introducing the concept of a "template shape" of the target object in which the nodes are sampled non-uniformly and non-equidistantly on the image. We evaluate it on 2D-images where the object's textures and backgrounds are similar, and large areas of the object have the same gray level appearance as the background. We also evaluate it in 3D on 60 brain tumor datasets for neurosurgical planning purposes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.