Supervised nonparametric image parcellation

Mert R. Sabuncu, B. T. Thomas Yeo, Koen Van Leemput, Bruce Fischl, Polina Golland

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


Segmentation of medical images is commonly formulated as a supervised learning problem, where manually labeled training data are summarized using a parametric atlas. Summarizing the data alleviates the computational burden at the expense of possibly losing valuable information on inter-subject variability. This paper presents a novel framework for Supervised Nonparametric Image Parcellation (SNIP). SNIP models the intensity and label images as samples of a joint distribution estimated from the training data in a non-parametric fashion. By capitalizing on recently developed fast and robust pairwise image alignment tools, SNIP employs the entire training data to segment a new image via Expectation Maximization. The use of multiple registrations increases robustness to occasional registration failures. We report experiments on 39 volumetric brain MRI scans with manual labels for the white matter, cortex and subcortical structures. SNIP yields better segmentation than state-of-the-art algorithms in multiple regions of interest. © 2009 Springer-Verlag.
Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention
Number of pages9
PublisherSpringer-verlag Berlin
Publication date2009
ISBN (Print)978-3-642-04270-6
Publication statusPublished - 2009
Externally publishedYes
Event12th International Conference on Medical Image Computing and Computer-Assisted Intervention - Imperial College, London, United Kingdom
Duration: 20 Sep 200924 Sep 2009
Conference number: 12


Conference12th International Conference on Medical Image Computing and Computer-Assisted Intervention
LocationImperial College
CountryUnited Kingdom
SeriesLecture Notes in Computer Science


  • Channel estimation
  • Labels
  • Medical computing
  • Image segmentation

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