A Latent Source Model for Patch-Based Image Segmentation

Despite the popularity and empirical success of patch-based nearest-neighbor and weighted majority voting approaches to medical image segmentation, there has been no theoretical development on when, why, and how well these nonparametric methods work. We bridge this gap by providing a theoretical performance guarantee for nearest-neighbor and weighted majority voting segmentation under a new probabilistic model for patch-based image segmentation. Our analysis relies on a new local property for how similar nearby patches are, and fuses existing lines of work on modeling natural imagery patches and theory for nonparametric classification. We use the model to derive a new patch-based segmentation algorithm that iterates between inferring local label patches and merging these local segmentations to produce a globally consistent image segmentation. Many existing patch-based algorithms arise as special cases of the new algorithm.Comment: International Conference on Medical Image Computing and Computer Assisted Interventions 201

Keypoint Transfer for Fast Whole-Body Segmentation

We introduce an approach for image segmentation based on sparse correspondences between keypoints in testing and training images. Keypoints represent automatically identified distinctive image locations, where each keypoint correspondence suggests a transformation between images. We use these correspondences to transfer label maps of entire organs from the training images to the test image. The keypoint transfer algorithm includes three steps: (i) keypoint matching, (ii) voting-based keypoint labeling, and (iii) keypoint-based probabilistic transfer of organ segmentations. We report segmentation results for abdominal organs in whole-body CT and MRI, as well as in contrast-enhanced CT and MRI. Our method offers a speed-up of about three orders of magnitude in comparison to common multi-atlas segmentation, while achieving an accuracy that compares favorably. Moreover, keypoint transfer does not require the registration to an atlas or a training phase. Finally, the method allows for the segmentation of scans with highly variable field-of-view.Comment: Accepted for publication at IEEE Transactions on Medical Imagin

BrainPainter: A software for the visualisation of brain structures, biomarkers and associated pathological processes

We present BrainPainter, a software that automatically generates images of highlighted brain structures given a list of numbers corresponding to the output colours of each region. Compared to existing visualisation software (i.e. Freesurfer, SPM, 3D Slicer), BrainPainter has three key advantages: (1) it does not require the input data to be in a specialised format, allowing BrainPainter to be used in combination with any neuroimaging analysis tools, (2) it can visualise both cortical and subcortical structures and (3) it can be used to generate movies showing dynamic processes, e.g. propagation of pathology on the brain. We highlight three use cases where BrainPainter was used in existing neuroimaging studies: (1) visualisation of the degree of atrophy through interpolation along a user-defined gradient of colours, (2) visualisation of the progression of pathology in Alzheimer's disease as well as (3) visualisation of pathology in subcortical regions in Huntington's disease. Moreover, through the design of BrainPainter we demonstrate the possibility of using a powerful 3D computer graphics engine such as Blender to generate brain visualisations for the neuroscience community. Blender's capabilities, e.g. particle simulations, motion graphics, UV unwrapping, raster graphics editing, raytracing and illumination effects, open a wealth of possibilities for brain visualisation not available in current neuroimaging software. BrainPainter is customisable, easy to use, and can run straight from the web browser: https://brainpainter.csail.mit.edu , as well as from source-code packaged in a docker container: https://github.com/mrazvan22/brain-coloring . It can be used to visualise biomarker data from any brain imaging modality, or simply to highlight a particular brain structure for e.g. anatomy courses.Comment: Accepted at the MICCAI Multimodal Brain Imaging Analysis (MBIA) workshop, 201

Strong Readers’ Beginnings: Identifying the Agencies and Individuals Who Influence Reading Lives

While there exists substantial research on struggling and developmental readers, few research-practitioners have sought to examine the histories and circumstances that result in strong readers. This study drew upon the reading autobiographies of doctoral students in an English Education program at an Ivy League institution, in order to discover what can be learned from first-hand narrative accounts of their reading lives about the early literacy experiences, reading practices, family, community, school and cultural influences of a group of “strong” adult readers. Also examined for comparative and contrasting data are the reading lives of remedial and honors first-year college composition students at a 2-year community college. An understanding of how the environments, people, institutions, circumstances, and texts encountered in the literacy lives of the three different groups studied here can assist literacy educators in bridging theory with practice for the teaching of reading in early grades and for the teaching of college-level reading in first-year college writing classes. A central term and concept to help explain the trajectory of the reading lives of the populations studied here was Deborah Brandt’s (1998) theory of “sponsors of literacy.” Brandt’s terminology and the notion of sponsorship along with a sociocultural theoretical framework are used to interpret the reading autobiographies in this study. Methods employed were based on Connelly and Clandinin’s narrative inquiry approach, a methodology steeped in the richness of the storied lives of the participants. The three patterns that emerged in the strong readers’ memories were: (a) being read to in the home prior to school age; (b) dichotomous attitudes toward in-school and out-of-school reading, especially around the middle school years; and (c) evidence of firm productive habits of mind toward complex reading that extends into the higher education years. The early literacy sponsorship and productive habits of mind were less evidenced in the remedial population. The findings of certain common characteristics and practices in the backgrounds of strong readers, many of which were not present at the same level in the remedial readers, can help literacy educators and caregivers re-examine their role as literacy sponsors and offer approaches for how we might sponsor literacy differently to create strong readers at any stage of their education

Glucagon in hyperandrogenic women: relationships to abnormalities of insulin

In order to investigate whether glucagon is implicated in the reduced insulin sensitivity in hyperandrogenic women, concentrations of glucagon and insulin in serum were measured during a 75g oral glucose tolerance test (oGTT) in 24 obese (body mass index, BMI, >25kgm~2) and 20 non-obese women with polycystic ovary syndrome (PCO) , and 10 obese and 13 non-obese control subjects. The oGTT was repeated during alteration of concentrations of endogenous androgens by administration of buserelin, spironolactone or a combination of cyproterone acetate and ethinyl oestradiol to women with PCO, and by administration of goserelin or danazol to control subjects. The relationships between glucose, insulin, C-peptide and glucagon values and those of testosterone, androstenedione, dehydroepiandrosterone sulphate and sex hormone binding globulin were examined before and during treatment. Additionally, the relationship between basal and glucose-stimulated glucose concentrations and that of haemoglobin A^ (HbA^) was examined to assess the value of HbA-^ estimation for monitoring glycaemic control in women with PCO.Obese women with PCO had higher serum concentrations of insulin and glucose than did non-obese women with PCO and control subjects, but plasma concentrations of glucagon were greater in obese control women. There were no significant relationships between fasting concentrations of insulin or insulin responses to oral glucose and those of testosterone or androstenedione in either group of PCO subjects, but the glucagon response to oral glucose was significantly related to both testosterone and androstenedione in obese women with PCO. No significant relationship was demonstrated between change in serum concentrations of androgens as a result of treatment and those of insulin or glucagon in any of the groups. In no case was the HbA^ concentration above normal laboratory values although a significant correlation between HbA^ and summed glucose levels was apparent.Glucagon does not appear to be implicated in the insulin resistance exhibited by women with PCO nor do androgens appear to directly effect concentrations of insulin or glucagon in normal women or in those with PCO. Measurement of HbA-t is not sufficiently discriminatory for identification of impaired glucose tolerance in PCO

Co-Clustering with Generative Models

In this paper, we present a generative model for co-clustering and develop algorithms based on the mean field approximation for the corresponding modeling problem. These algorithms can be viewed as generalizations of the traditional model-based clustering; they extend hard co-clustering algorithms such as Bregman co-clustering to include soft assignments. We show empirically that these model-based algorithms offer better performance than their hard-assignment counterparts, especially with increasing problem complexity

Atlas-Based Under-Segmentation

We study the widespread, but rarely discussed, tendency of atlas-based segmentation to under-segment the organs of interest. Commonly used error measures do not distinguish between under- and over-segmentation, contributing to the problem. We explicitly quantify over- and under-segmentation in several typical examples and present a new hypothesis for the cause. We provide evidence that segmenting only one organ of interest and merging all surrounding structures into one label creates bias towards background in the label estimates suggested by the atlas. We propose a generative model that corrects for this effect by learning the background structures from the data. Inference in the model separates the background into distinct structures and consequently improves the segmentation accuracy. Our experiments demonstrate a clear improvement in several applications.National Alliance for Medical Image Computing (U.S.) (U54-EB005149)Neuroimaging Analysis Center (U.S.) (P41-EB015902

Permutation Tests for Classification

We introduce and explore an approach to estimating statistical significance of classification accuracy, which is particularly useful in scientific applications of machine learning where high dimensionality of the data and the small number of training examples render most standard convergence bounds too loose to yield a meaningful guarantee of the generalization ability of the classifier. Instead, we estimate statistical significance of the observed classification accuracy, or the likelihood of observing such accuracy by chance due to spurious correlations of the high-dimensional data patterns with the class labels in the given training set. We adopt permutation testing, a non-parametric technique previously developed in classical statistics for hypothesis testing in the generative setting (i.e., comparing two probability distributions). We demonstrate the method on real examples from neuroimaging studies and DNA microarray analysis and suggest a theoretical analysis of the procedure that relates the asymptotic behavior of the test to the existing convergence bounds