Chris A. Cocosco

Multi-spectral brain tissue segmentation using automatically trained k-Nearest-Neighbor classification

Conventional k-Nearest-Neighbor (kNN) classification, which has been successfully applied to classify brain tissue in MR data, requires training on manually labeled subjects. This manual labeling is a laborious and time-consuming procedure. In this work, a new fully automated brain tissue classification procedure is presented, in which kNN training is automated. This is achieved by non-rigidly registering the MR data with a tissue probability atlas to automatically select training samples, followed by a post-processing step to keep the most reliable samples. The accuracy of the new method was compared to rigid registration-based training and to conventional kNN-based segmentation using training on manually labeled subjects for segmenting gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) in 12 data sets. Furthermore, for all classification methods, the performance was assessed when varying the free parameters. Finally, the robustness of the fully automated procedure was evaluated on 59 subjects. The automated training method using non-rigid registration with a tissue probability atlas was significantly more accurate than rigid registration. For both automated training using non-rigid registration and for the manually trained kNN classifier, the difference with the manual labeling by observers was not significantly larger than inter-observer variability for all tissue types. From the robustness study, it was clear that, given an appropriate brain atlas and optimal parameters, our new fully automated, non-rigid registration-based method gives accurate and robust segmentation results. A similarity index was used for comparison with manually trained kNN. The similarity indices were 0.93, 0.92 and 0.92, for CSF, GM and WM, respectively. It can be concluded that our fully automated method using non-rigid registration may replace manual segmentation, and thus that automated brain tissue segmentation without laborious manual training is feasible.

Automatic Image-Driven Segmentation of the Ventricles in Cardiac Cine MRI

To propose and to evaluate a novel method for the automatic segmentation of the hearts two ventricles from dynamic (“cine”) short‐axis “steady state free precession” (SSFP) MR images. This segmentation task is of significant clinical importance. Previously published automated methods have various disadvantages for routine clinical use.

kNN-based multi-spectral MRI brain tissue classification: manual training versus automated atlas-based training

Conventional k-Nearest-Neighbor (kNN) classification, which has been successfully applied to classify brain tissue, requires laborious training on manually labeled subjects. In this work, the performance of kNN-based segmentation of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) using manual training is compared with a new method, in which training is automated using an atlas. From 12 subjects, standard T2 and PD scans and a high-resolution, high-contrast scan (Siemens T1-weighted HASTE sequence with reverse contrast) were used as feature sets. For the conventional kNN method, manual segmentations were used for training, and classifications were evaluated in a leave-one-out study. The performance as a function of the number of samples per tissue, and k was studied. For fully automated training, scans were registered to a probabilistic brain atlas. Initial training samples were randomly selected per tissue based on a threshold on the tissue probability. These initials were processed to keep the most reliable samples. Performance of the method for varying the threshold on the tissue probability method was studied. By measuring the percentage overlap (SI), classification results of both methods were validated. For conventional kNN classification, varying the number of training samples did not result in significant differences, while increasing k gave significantly better results. In the method using automated training, there is an overestimation of GM at the expense of CSF at higher thresholds on the tissue probability maps. The difference between the conventional method (k=45) and the observers was not significantly larger than inter-observer variability for all tissue types. The automated method performed slightly worse and performed equal to the observers for WM, and less for CSF and GM. From these results it can be concluded that conventional kNN classification may replace manual segmentation, and that atlas-based kNN segmentation has strong potential for fully automated segmentation, without the need of laborious manual training.

Segmentation of Medical Images with a Shape and Motion Model: A Bayesian Perspective

This paper describes a Bayesian framework for the segmentation of a temporal sequence of medical images, where both shape and motion prior information are integrated into a stochastic model. With this approach, we aim to take into account all the information available to compute an optimum solution, thus increasing the robustness and accuracy of the shape and motion reconstruction. The segmentation algorithm we develop is based on sequential Monte Carlo sampling methods previously applied in tracking applications. Moreover, we show how stochastic shape models can be constructed using a global shape description based on orthonormal functions. This makes our approach independent of the dimension of the object (2D or 3D) and on the particular shape parameterization used. Results of the segmentation method applied to cardiac cine MR images are presented.

Model-based segmentation of cardiac MRI cine sequences: a Bayesian formulation

The quantitative analysis of cardiac cine MRI sequences requires automated, robust, and fast image processing algorithms for the 4D (3D + time) segmentation of the heart chambers. The use of shape models has proven efficient in extracting the cardiac volumes for single phases, but less attention has been focused on incorporating prior knowledge about the cardiac motion. To explicitly address the temporal aspect of the segmentation problem, this paper proposes a full Bayesian model, where the prior information is represented by a cardiac shape and motion model. In this framework, the solution of the segmentation is defined by means of a probability distribution over the parameters of the space-time problem. The computed solution, obtained by means of sequential Monte Carlo techniques, has the advantage of being both spatially and temporally coherent. Furthermore, the method does not require any particular representation of the shape or of the motion model; it is therefore generic and highly flexible.

Slice-to-volume registration using mutual information between probabilistic image classifications

Intensity based registration algorithms have proved to be accurate and robust for 3D-3D registration tasks. However, these methods utilise the information content within an image, and therefore their performance is hindered for image data that is sparse. This is the case for the registration of a single image slice to a 3D image volume. There are some important applications that could benefit from improved slice-to-volume registration, for example, the planning of magnetic resonance (MR) scans or cardiac MR imaging, where images are acquired as stacks of single slices. We have developed and validated an information based slice-to-volume registration algorithm that uses vector valued probabilistic images of tissue classification that have been derived from the original intensity images. We believe that using such methods inherently incorporates into the registration framework more information about the images, especially in images containing severe partial volume artifacts. Initial experimental results indicate that the suggested method can achieve a more robust registration compared to standard intensity based methods for the rigid registration of a single thick brain MR slice, containing severe partial volume artifacts in the through-plane direction, to a complete 3D MR brain volume.

Erratum to: “A fully automatic and robust brain MRI tissue classification method” [Medical Image Analysis 7 (2003) 513–527]

PII of original article: S1361-8415(03)00037-9. DOI of original article: 10.1016/S1361-8415(03)00037-9. * Corresponding author. Present address: Philips Research Laboratories, Division Technical Systems, Roentgenstrasse 24-26, D-22335 Hamburg, Germany. Tel.: +49-40-5078-2811; fax: +49-40-5078-2510. E-mail address: crisco@bic.mni.mcgill.ca (C.A. Cocosco). URL: http://www.bic.mni.mcgill.ca/users/crisco/.

Java Internet Viewer: A WWW Tool for Remote 3D Medical Image Data Visualization and Comparison

There is a growing need in the research and clinical medical imaging community for Internet-capable tools that facilitate remote data dissemination and interaction. 3-dimensional (3D)medical imaging datasets typically require special-purpose, non-portable, software to be installed and maintained on each workstation. Internet technologies have potential for improving this.