Brain Surface Segmentation of Magnetic Resonance Images of the Fetus

In this work we present a method for the image analysisof Magnetic Resonance Imaging (MRI) of fetuses. Our goalis to segment the brain surface from multiple volumes(axial, coronal and sagittal acquisitions) of a fetus. Tothis end we propose a two-step approach: first, a FiniteGaussian Mixture Model (FGMM) will segment the image into3 classes: brain, non-brain and mixture voxels. Second, aMarkov Random Field scheme will be applied tore-distribute mixture voxels into either brain ornon-brain tissue. Our main contributions are an adaptedenergy computation and an extended neighborhood frommultiple volumes in the MRF step. Preliminary results onfour fetuses of different gestational ages will be shown.

Customer Focus Newsletter, July-August, 2010, Vol. 7, no.4

A bi-monthly bulletin to keep the department/agency management teams of state government better informed. We hope to consolidate most of the service update messages we send throughout the month and keep you updated about the work of the Customer Councils. If yours is one of the many departments who participated in the second annual DAS customer satisfaction survey recently, we thank you for taking the time to give us this important feedback. We look forward to sharing survey results with you, and pledge to consider responses carefully as we work to determine benchmarks and set future priorities.

Customer Focus Newsletter, September-October, 2010, Vol. 7, no.5

A bi-monthly bulletin to keep the department/agency management teams of state government better informed. We hope to consolidate most of the service update messages we send throughout the month and keep you updated about the work of the Customer Councils. If yours is one of the many departments who participated in the second annual DAS customer satisfaction survey recently, we thank you for taking the time to give us this important feedback. We look forward to sharing survey results with you, and pledge to consider responses carefully as we work to determine benchmarks and set future priorities.

Customer Focus Newsletter, November-December, 2010, Vol. 7, no.6

A bi-monthly bulletin to keep the department/agency management teams of state government better informed. We hope to consolidate most of the service update messages we send throughout the month and keep you updated about the work of the Customer Councils. If yours is one of the many departments who participated in the second annual DAS customer satisfaction survey recently, we thank you for taking the time to give us this important feedback. We look forward to sharing survey results with you, and pledge to consider responses carefully as we work to determine benchmarks and set future priorities.

Customer Focus Newsletter, January-February, 2011, Vol. 8, no.1

A bi-monthly bulletin to keep the department/agency management teams of state government better informed. We hope to consolidate most of the service update messages we send throughout the month and keep you updated about the work of the Customer Councils. If yours is one of the many departments who participated in the second annual DAS customer satisfaction survey recently, we thank you for taking the time to give us this important feedback. We look forward to sharing survey results with you, and pledge to consider responses carefully as we work to determine benchmarks and set future priorities.

Customer Focus Newsletter, March - April, 2011, Vol. 8, no. 2

A bi-monthly bulletin to keep the department/agency management teams of state government better informed. We hope to consolidate most of the service update messages we send throughout the month and keep you updated about the work of the Customer Councils. If yours is one of the many departments who participated in the second annual DAS customer satisfaction survey recently, we thank you for taking the time to give us this important feedback. We look forward to sharing survey results with you, and pledge to consider responses carefully as we work to determine benchmarks and set future priorities.

Customer Focus Newsletter, May - June, 2011, Vol. 8, no. 3

A bi-monthly bulletin to keep the department/agency management teams of state government better informed. We hope to consolidate most of the service update messages we send throughout the month and keep you updated about the work of the Customer Councils. If yours is one of the many departments who participated in the second annual DAS customer satisfaction survey recently, we thank you for taking the time to give us this important feedback. We look forward to sharing survey results with you, and pledge to consider responses carefully as we work to determine benchmarks and set future priorities.

Customer Focus Newsletter, July - August, 2011, Vol. 8, no. 4

A bi-monthly bulletin to keep the department/agency management teams of state government better informed. We hope to consolidate most of the service update messages we send throughout the month and keep you updated about the work of the Customer Councils. If yours is one of the many departments who participated in the second annual DAS customer satisfaction survey recently, we thank you for taking the time to give us this important feedback. We look forward to sharing survey results with you, and pledge to consider responses carefully as we work to determine benchmarks and set future priorities.

Customer Focus Newsletter, September-October, 2011, Vol. 8, no. 5

A bi-monthly bulletin to keep the department/agency management teams of state government better informed. We hope to consolidate most of the service update messages we send throughout the month and keep you updated about the work of the Customer Councils. If yours is one of the many departments who participated in the second annual DAS customer satisfaction survey recently, we thank you for taking the time to give us this important feedback. We look forward to sharing survey results with you, and pledge to consider responses carefully as we work to determine benchmarks and set future priorities.

Multi-Atlas based Segmentation of Head and Neck CT Images using Active Contour

This paper presents the segmentation of bilateral parotid glands in the Head and Neck (H&N) CT images using an active contour based atlas registration. We compare segmentation results from three atlas selection strategies: (i) selection of "single-most-similar" atlas for each image to be segmented, (ii) fusion of segmentation results from multiple atlases using STAPLE, and (iii) fusion of segmentation results using majority voting. Among these three approaches, fusion using majority voting provided the best results. Finally, we present a detailed evaluation on a dataset of eight images (provided as a part of H&N auto segmentation challenge conducted in conjunction with MICCAI-2010 conference) using majority voting strategy.

Customer Focus Newsletter, Spring, 2012, Vol. 9, no. 1

A bi-monthly bulletin to keep the department/agency management teams of state government better informed. We hope to consolidate most of the service update messages we send throughout the month and keep you updated about the work of the Customer Councils. If yours is one of the many departments who participated in the second annual DAS customer satisfaction survey recently, we thank you for taking the time to give us this important feedback. We look forward to sharing survey results with you, and pledge to consider responses carefully as we work to determine benchmarks and set future priorities.

A fully-automatic caudate nucleus segmentation of brain MRI: application in volumetric analysis of pediatric attention-deficit/hyperactivity disorder

Background Accurate automatic segmentation of the caudate nucleus in magnetic resonance images (MRI) of the brain is of great interest in the analysis of developmental disorders. Segmentation methods based on a single atlas or on multiple atlases have been shown to suitably localize caudate structure. However, the atlas prior information may not represent the structure of interest correctly. It may therefore be useful to introduce a more flexible technique for accurate segmentations. Method We present Cau-dateCut: a new fully-automatic method of segmenting the caudate nucleus in MRI. CaudateCut combines an atlas-based segmentation strategy with the Graph Cut energy-minimization framework. We adapt the Graph Cut model to make it suitable for segmenting small, low-contrast structures, such as the caudate nucleus, by defining new energy function data and boundary potentials. In particular, we exploit information concerning the intensity and geometry, and we add supervised energies based on contextual brain structures. Furthermore, we reinforce boundary detection using a new multi-scale edgeness measure. Results We apply the novel CaudateCut method to the segmentation of the caudate nucleus to a new set of 39 pediatric attention-deficit/hyperactivity disorder (ADHD) patients and 40 control children, as well as to a public database of 18 subjects. We evaluate the quality of the segmentation using several volumetric and voxel by voxel measures. Our results show improved performance in terms of segmentation compared to state-of-the-art approaches, obtaining a mean overlap of 80.75%. Moreover, we present a quantitative volumetric analysis of caudate abnormalities in pediatric ADHD, the results of which show strong correlation with expert manual analysis. Conclusion CaudateCut generates segmentation results that are comparable to gold-standard segmentations and which are reliable in the analysis of differentiating neuroanatomical abnormalities between healthy controls and pediatric ADHD.

Customer Focus Newsletter, Summer, 2012, Vol. 9, no. 1

A bi-monthly bulletin to keep the department/agency management teams of state government better informed. We hope to consolidate most of the service update messages we send throughout the month and keep you updated about the work of the Customer Councils. If yours is one of the many departments who participated in the second annual DAS customer satisfaction survey recently, we thank you for taking the time to give us this important feedback. We look forward to sharing survey results with you, and pledge to consider responses carefully as we work to determine benchmarks and set future priorities.