An atlas of classifiers-a machine learning paradigm for brain MRI segmentation.

We present the Atlas of Classifiers (AoC)-a conceptually novel framework for brain MRI segmentation. The AoC is a spatial map of voxel-wise multinomial logistic regression (LR) functions learned from the labeled data. Upon convergence, the resulting fixed LR weights, a few for each voxel, represent the training dataset. It can, therefore, be considered as a light-weight learning machine, which despite its low capacity does not underfit the problem. The AoC construction is independent of the actual intensities of the test images, providing the flexibility to train it on the available labeled data and use it for the segmentation of images from different datasets and modalities. In this sense, it does not overfit the training data, as well. The proposed method has been applied to numerous publicly available datasets for the segmentation of brain MRI tissues and is shown to be robust to noise and outreach commonly used methods. Promising results were also obtained for multi-modal, cross-modality MRI segmentation. Finally, we show how AoC trained on brain MRIs of healthy subjects can be exploited for lesion segmentation of multiple sclerosis patients.

Multidimensional co-segmentation of longitudinal brain MRI ensembles in the presence of a neurodegenerative process.

MRI Segmentation of a pathological brain poses a significant challenge, as the available anatomical priors that provide top-down information to aid segmentation are inadequate in the presence of abnormalities. This problem is further complicated for longitudinal data capturing impaired brain development or neurodegenerative conditions, since the dynamic of brain atrophies has to be considered as well. For these cases, the absence of compatible annotated training examples renders the commonly used multi-atlas or machine-learning approaches impractical. We present a novel segmentation approach that accounts for the lack of labeled data via multi-region multi-subject co-segmentation (MMCoSeg) of longitudinal MRI sequences. The underlying, unknown anatomy is learned throughout an iterative process, in which the segmentation of a region is supported both by the segmentation of the neighboring regions, which share common boundaries, and by the segmentation of corresponding regions, in the other jointly segmented images. A 4D multi-region atlas that models the spatio-temporal deformations and can be adapted to different subjects undergoing similar degeneration processes is reconstructed concurrently. An inducible mouse model of p25 accumulation (the CK-p25 mouse) that displays key pathological hallmarks of Alzheimer disease (AD) is used as a gold-standard to test the proposed algorithm by providing a conditional control of rapid neurodegeneration. Applying the MMCoSeg to a cohort of CK-p25 mice and littermate controls yields promising segmentation results that demonstrate high compatibility with expertise manual annotations. An extensive comparative analysis with respect to current well-established, atlas-based segmentation methods highlights the advantage of the proposed approach, which provides accurate segmentation of longitudinal brain MRIs in pathological conditions, where only very few annotated examples are available.

Primary headaches, attention deficit disorder and learning disabilities in children and adolescents.

BACKGROUND: Primary headaches and Learning difficulties are both common in the pediatric population. The goal of our study was to assess the prevalence of learning disabilities and attention deficit disorder in children and adolescents with migraine and tension type headaches. METHODS: Retrospective review of medical records of children and adolescents who presented with headache to the outpatient pediatric neurology clinics of Bnai-Zion Medical Center and Meyer Children's Hospital, Haifa, during the years 2009-2010. Demographics, Headache type, attention deficit disorder (ADHD), learning disabilities and academic achievements were assessed. RESULTS: 243 patients met the inclusion criteria and were assessed: 135 (55.6%) females and 108 (44.4%) males. 44% were diagnosed with migraine (35.8% of the males, 64.2% of the females, p = 0.04), 47.7% were diagnosed with tension type headache (50.4% of the males, 49.6% of the females). Among patients presenting with headache for the first time, 24% were formerly diagnosed with learning disabilities and 28% were diagnosed with attention deficit disorder (ADHD). ADHD was more prevalent among patients with tension type headache when compared with patients with migraine (36.5% vs. 19.8%, p = 0.006). Poor to average school academic performance was more prevalent among children with tension type headache, whereas good to excellent academic performance was more prevalent among those with migraine. CONCLUSIONS: Learning disabilities and ADHD are more common in children and adolescents who are referred for neurological assessment due to primary headaches than is described in the general pediatric population. There is an association between headache diagnosis and school achievements.

Evaluation of uterine cervix segmentations using ground truth from multiple experts.

This work is focused on the generation and utilization of a reliable ground truth (GT) segmentation for a large medical repository of digital cervicographic images (cervigrams) collected by the National Cancer Institute (NCI). NCI invited twenty experts to manually segment a set of 939 cervigrams into regions of medical and anatomical interest. Based on this unique data, the objectives of the current work are to: (1) Automatically generate a multi-expert GT segmentation map; (2) Use the GT map to automatically assess the complexity of a given segmentation task; (3) Use the GT map to evaluate the performance of an automated segmentation algorithm. The multi-expert GT map is generated via the STAPLE (Simultaneous Truth and Performance Level Estimation) algorithm, which is a well-known method to generate a GT segmentation from multiple observations. A new measure of segmentation complexity, which relies on the inter-observer variability within the GT map, is defined. This measure is used to identify images that were found difficult to segment by the experts and to compare the complexity of different segmentation tasks. An accuracy measure, which evaluates the performance of automated segmentation algorithms is presented. Two algorithms for cervix boundary detection are compared using the proposed accuracy measure. The measure is shown to reflect the actual segmentation quality achieved by the algorithms. The methods and conclusions presented in this work are general and can be applied to different images and segmentation tasks. Here they are applied to the cervigram database including a thorough analysis of the available data.

Automatic detection of anatomical landmarks in uterine cervix images.

The work focuses on a unique medical repository of digital cervicographic images ("Cervigrams") collected by the National Cancer Institute (NCI) in longitudinal multiyear studies. NCI, together with the National Library of Medicine (NLM), is developing a unique web-accessible database of the digitized cervix images to study the evolution of lesions related to cervical cancer. Tools are needed for automated analysis of the cervigram content to support cancer research. We present a multistage scheme for segmenting and labeling regions of anatomical interest within the cervigrams. In particular, we focus on the extraction of the cervix region and fine detection of the cervix boundary; specular reflection is eliminated as an important preprocessing step; in addition, the entrance to the endocervical canal (the "os"), is detected. Segmentation results are evaluated on three image sets of cervigrams that were manually labeled by NCI experts.

Shape priors for segmentation of the cervix region within uterine cervix images.

The work focuses on a unique medical repository of digital uterine cervix images ("cervigrams") collected by the National Cancer Institute (NCI), National Institute of Health, in longitudinal multiyear studies. NCI together with the National Library of Medicine is developing a unique web-based database of the digitized cervix images to study the evolution of lesions related to cervical cancer. Tools are needed for the automated analysis of the cervigram content to support the cancer research. In recent works, a multistage automated system for segmenting and labeling regions of medical and anatomical interest within the cervigrams was developed. The current paper concentrates on incorporating prior-shape information in the cervix region segmentation task. In accordance with the fact that human experts mark the cervix region as circular or elliptical, two shape models (and corresponding methods) are suggested. The shape models are embedded within an active contour framework that relies on image features. Experiments indicate that incorporation of the prior shape information augments previous results.

Unsupervised image-set clustering using an information theoretic framework.

In this paper, we combine discrete and continuous image models with information-theoretic-based criteria for unsupervised hierarchical image-set clustering. The continuous image modeling is based on mixture of Gaussian densities. The unsupervised image-set clustering is based on a generalized version of a recently introduced information-theoretic principle, the information bottleneck principle. Images are clustered such that the mutual information between the clusters and the image content is maximally preserved. Experimental results demonstrate the performance of the proposed framework for image clustering on a large image set. Information theoretic tools are used to evaluate cluster quality. Particular emphasis is placed on the application of the clustering for efficient image search and retrieval.