Anatomy-specific classification of medical images using deep convolutional nets

Automated classification of human anatomy is an important prerequisite for many computer-aided diagnosis systems. The spatial complexity and variability of anatomy throughout the human body makes classification difficult. "Deep learning" methods such as convolutional networks (ConvNets) outperform other state-of-the-art methods in image classification tasks. In this work, we present a method for organ- or body-part-specific anatomical classification of medical images acquired using computed tomography (CT) with ConvNets. We train a ConvNet, using 4,298 separate axial 2D key-images to learn 5 anatomical classes. Key-images were mined from a hospital PACS archive, using a set of 1,675 patients. We show that a data augmentation approach can help to enrich the data set and improve classification performance. Using ConvNets and data augmentation, we achieve anatomy-specific classification error of 5.9 % and area-under-the-curve (AUC) values of an average of 0.998 in testing. We demonstrate that deep learning can be used to train very reliable and accurate classifiers that could initialize further computer-aided diagnosis.Comment: Presented at: 2015 IEEE International Symposium on Biomedical Imaging, April 16-19, 2015, New York Marriott at Brooklyn Bridge, NY, US

Generating expressive speech for storytelling applications

Work on expressive speech synthesis has long focused on the expression of basic emotions. In recent years, however, interest in other expressive styles has been increasing. The research presented in this paper aims at the generation of a storytelling speaking style, which is suitable for storytelling applications and more in general, for applications aimed at children. Based on an analysis of human storytellers' speech, we designed and implemented a set of prosodic rules for converting "neutral" speech, as produced by a text-to-speech system, into storytelling speech. An evaluation of our storytelling speech generation system showed encouraging results

Variability of vertical ground reaction forces in patients with chronic low back pain, before and after chiropractic care.

Introduction Many chiropractic articles and textbooks discuss gait, but there actually has been little research into the effects of chiropractic adjustment on gait. This pilot study used a quantitative method of gait evaluation before and after a series of chiropractic visits. Hypotheses: (1) adults with chronic low back pain (CLBP) would show increased variability in vertical ground reaction forces (VGRF) while walking, as compared to healthy control subjects, and (2) that, following chiropractic care, will show decreased variability. Methods VGRF data were collected for 6 controls and compared to 9 CLBP participants, who were also evaluated before and after the first visit of care and over 7 visits. Data were analyzed by Mean Standard Deviation (MSD), Mean Coefficient of Variation (MCV), and the Coefficient of Variation of loading rate. Chiropractic care consisted of “high velocity low amplitude” thrust type procedures, flexion-distraction, pelvic wedges, light mobilization, and stretching. Results CLBP participants had somewhat greater variability and became slightly less variable post-care; differences were not significant. Limitations: Some participants had no impairment of walking at baseline; MSD is an uncommon measure, and more research is needed; these results (small group seen by a single doctor) may not be generalizable. Conclusions Participants with CLBP had slightly more variability and had slight decreases in variability following chiropractic care. Differences were not statistically significant. With this small pilot study as a guide, more research should be done with larger groups and improved participant selection

Acylation of glucosaminyl phosphatidylinositol revisited. Palmitoyl-CoA dependent palmitoylation of the inositol residue of a synthetic dioctanoyl glucosaminyl phosphatidylinositol by hamster membranes permits efficient mannosylation of the glucosamine residue

Two critical steps in the assembly of yeast and mammalian glycosylphosphatidylinositol (GPI) anchor precursors are palmitoylation of the inositol residue and mannosylation of the glucosamine residue of the glucosaminyl phosphatidylinositol (GlcNα-PI) intermediate. Palmitoylation has been reported to be acyl-CoA dependent in yeast membranes (Costello, L. C., and Orlean, P. (1992) J. Biol. Chem. 267, 8599-8603) but strictly acyl- CoA independent in rodent membranes (Stevens, V. L., and Zhang, H. (1994) J. Biol. Chem. 269, 31397-31403), and thus poorly conserved. In addition, it was suggested that acylation must precede mannosylation in both yeast (Costello, L. C., and Orlean, P. (1992) J. Biol. Chem. 276, 8599-8603) and rodent (Urakaze, M., Kamitani, T., DeGasperi, R., Sugiyama, E., Chang, H.-M., Warren, C. D., and Yeh, E. T. H. (1992) J. Biol. Chem. 267, 6459-6462) cells because GlcNα-acyl-PI accumulates in vivo when mannosylation is blocked. However, GlcNα-acyl-PI accumulation would also be expected if mannosylation and acylation were independent of each other. These issues were addressed by the use of a synthetic dioctanoyl GlcNα-PI analogue (GlcNα-PI(C8)) as an in vitro substrate for GPI-synthesizing enzymes in Chinese hamster ovary cell membranes. GlcNα-PI(C8) was acylated in an manner requiring acyl-CoA. Thus, the process involving acyl-CoA reported for yeast has been conserved in mammals. Furthermore, both GlcNα-PI(C8) and GlcNα-acyl-PI(C8) could be mannosylated in vitro, but mannosylation of the latter was significantly more efficient. This provides direct support for the earlier suggestion that acylation precedes mannosylation in rodents cells. A similar result was also observed with the Saccharomyces cerevisiae mannosyltransferase. In contrast, it has been reported that mannosylation of endogenous GlcNα-PI by Trypansoma brucei membranes occurs without prior acylation. The same result was obtained with GlcNα-PI(C8), confirming that the mannosyltransferase of trypanosomes is divergent from those in yeasts and rodents

Nonrigid Medical Image Registration by Finite-Element Deformable Sheet-Curve Models

Image-based change quantitation has been recognized as a promising tool for accurate assessment of tumor's early response to chemoprevention in cancer research. For example, various changes on breast density and vascularity in glandular tissue are the indicators of early response to treatment. Accurate extraction of glandular tissue from pre- and postcontrast magnetic resonance (MR) images requires a nonrigid registration of sequential MR images embedded with local deformations. This paper reports a newly developed registration method that aligns MR breast images using finite-element deformable sheet-curve models. Specifically, deformable curves are constructed to match the boundaries dynamically, while a deformable sheet of thin-plate splines is designed to model complex local deformations. The experimental results on both digital phantoms and real MR breast images using the new method have been compared to point-based thin-plate-spline (TPS) approach, and have demonstrated a significant and robust improvement in both boundary alignment and local deformation recovery

Language Maintenance among Selected Immigrant Groups in Canada 1971-1991

Abstract not availabl

Automatic Segmentation, Localization, and Identification of Vertebrae in 3D CT Images Using Cascaded Convolutional Neural Networks

This paper presents a method for automatic segmentation, localization, and identification of vertebrae in arbitrary 3D CT images. Many previous works do not perform the three tasks simultaneously even though requiring a priori knowledge of which part of the anatomy is visible in the 3D CT images. Our method tackles all these tasks in a single multi-stage framework without any assumptions. In the first stage, we train a 3D Fully Convolutional Networks to find the bounding boxes of the cervical, thoracic, and lumbar vertebrae. In the second stage, we train an iterative 3D Fully Convolutional Networks to segment individual vertebrae in the bounding box. The input to the second networks have an auxiliary channel in addition to the 3D CT images. Given the segmented vertebra regions in the auxiliary channel, the networks output the next vertebra. The proposed method is evaluated in terms of segmentation, localization, and identification accuracy with two public datasets of 15 3D CT images from the MICCAI CSI 2014 workshop challenge and 302 3D CT images with various pathologies introduced in [1]. Our method achieved a mean Dice score of 96%, a mean localization error of 8.3 mm, and a mean identification rate of 84%. In summary, our method achieved better performance than all existing works in all the three metrics

The HMW effect in Noncommutative Quantum Mechanics

The HMW effect in non-commutative quantum mechanics is studied. By solving the Dirac equations on non-commutative (NC) space and non-commutative phase space, we obtain topological HMW phase on NC space and NC phase space respectively, where the additional terms related to the space-space and momentum-momentum non-commutativity are given explicitly.Comment: 8 Latex page