Chapter

Supervised Nonparametric Image Parcellation

DOI: 10.1007/978-3-642-04271-3_130

ABSTRACT 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.

0 Followers
 · 
67 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: Kidney segmentation from abdominal MRI data is used as an effective and accurate indicator for renal function in many clinical situations. The goal of this research is to accurately segment kidney from very low contrast MRI data. The present problem becomes challenging mainly due to poor contrast, high noise and partial volume effects introduced during the scanning process. In this paper, we propose a novel kidney segmentation algorithm using graph cuts and pixel connectivity. A connectivity term is introduced in the energy function of the standard graph cut via pixel labeling. Each pixel is assigned a different label based on its probabilities to belong to two different segmentation classes and probabilities of its neighbors to belong to these segmentation classes. The labeling process is formulated according to Dijkstra's shortest path algorithm. Experimental results yield a (mean +/- s.d.) Dice coefficient value of (98.60 +/- 0.52)% on 25 datasets. (c) 2013 Elsevier B.V. All rights reserved.
    Pattern Recognition Letters 10/2013; 34(13). DOI:10.1016/j.patrec.2013.05.013 · 1.06 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Automatic segmentation of the heart’s left atrium offers great benefits for planning and outcome evaluation of atrial ablation procedures. However, the high anatomical variability of the left atrium presents significant challenges for atlas-guided segmentation. In this paper, we demonstrate an automatic method for left atrium segmentation using weighted voting label fusion and a variant of the demons registration algorithm adapted to handle images with different intensity distributions. We achieve accurate automatic segmentation that is robust to the high anatomical variations in the shape of the left atrium in a clinical dataset of MRA images.
    Statistical Atlases and Computational Models of the Heart, First International Workshop, STACOM 2010, and Cardiac Electrophysiological Simulation Challenge, CESC 2010, Held in Conjunction with MICCAI 2010, Beijing, China, September 20, 2010. Proceedings; 01/2010
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: We propose a nonparametric, probabilistic model for the automatic segmentation of medical images, given a training set of images and corresponding label maps. The resulting inference algorithms rely on pairwise registrations between the test image and individual training images. The training labels are then transferred to the test image and fused to compute the final segmentation of the test subject. Such label fusion methods have been shown to yield accurate segmentation, since the use of multiple registrations captures greater inter-subject anatomical variability and improves robustness against occasional registration failures. To the best of our knowledge, this manuscript presents the first comprehensive probabilistic framework that rigorously motivates label fusion as a segmentation approach. The proposed framework allows us to compare different label fusion algorithms theoretically and practically. In particular, recent label fusion or multiatlas segmentation algorithms are interpreted as special cases of our framework. We conduct two sets of experiments to validate the proposed methods. In the first set of experiments, we use 39 brain MRI scans-with manually segmented white matter, cerebral cortex, ventricles and subcortical structures-to compare different label fusion algorithms and the widely-used FreeSurfer whole-brain segmentation tool. Our results indicate that the proposed framework yields more accurate segmentation than FreeSurfer and previous label fusion algorithms. In a second experiment, we use brain MRI scans of 282 subjects to demonstrate that the proposed segmentation tool is sufficiently sensitive to robustly detect hippocampal volume changes in a study of aging and Alzheimer's Disease.
    10/2010; 29(10):1714-29. DOI:10.1109/TMI.2010.2050897