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

Quantum Separability of the vacuum for Scalar Fields with a Boundary

Using the Green's function approach we investigate separability of the vacuum state of a massless scalar field with a single Dirichlet boundary. Separability is demonstrated using the positive partial transpose criterion for effective two-mode Gaussian states of collective operators. In contrast to the vacuum energy, entanglement of the vacuum is not modified by the presence of the boundary.Comment: 4 pages, 1 figure, Revtex, minor corrections. submitted to Phy. Rev.

Phenotypic impacts of CSF1R deficiencies in humans and model organisms

Mϕ proliferation, differentiation, and survival are controlled by signals from the Mϕ CSF receptor (CSF1R). Mono‐allelic gain‐of‐function mutations in CSF1R in humans are associated with an autosomal‐dominant leukodystrophy and bi‐allelic loss‐of‐function mutations with recessive skeletal dysplasia, brain disorders, and developmental anomalies. Most of the phenotypes observed in these human disease states are also observed in mice and rats with loss‐of‐function mutations in Csf1r or in Csf1 encoding one of its two ligands. Studies in rodent models also highlight the importance of genetic background and likely epistatic interactions between Csf1r and other loci. The impacts of Csf1r mutations on the brain are usually attributed solely to direct impacts on microglial number and function. However, analysis of hypomorphic Csf1r mutants in mice and several other lines of evidence suggest that primary hydrocephalus and loss of the physiological functions of Mϕs in the periphery contribute to the development of brain pathology. In this review, we outline the evidence that CSF1R is expressed exclusively in mononuclear phagocytes and explore the mechanisms linking CSF1R mutations to pleiotropic impacts on postnatal growth and development

Anterior segment mesenchymal dysgenesis in a large Australian family is associated with the recurrent 17 bp duplication in PITX3

Purpose: A recurrent 17 bp duplication (c.657ins17bp) of a segment of the paired-like homeodomain transcription factor 3 (PITX3) gene on human chromosome 10 has been reported in seven families with autosomal dominant posterior polar cataracts with or without anterior segment mesenchymal dysgenesis (ASMD). ASMD can include Peters anomaly with corneal clouding, iridolenticular corneal adhesions, displaced Schwalbe's line, and cataract as described previously in a large Australian family. This study reports the examination of PITX3 in this Australian family. Methods: Clinical examinations of the proband and her relatives were performed as part of routine follow up. A polymerase chain reaction (PCR) based test for the duplication in PITX3 was developed, and DNA from 21 members of the proband's family was tested. Results: All clinically affected members of the family had the same 17 bp duplication of PITX3. There was no difference in the size of the duplication between the severely affected individuals and the more mildly affected individuals. Prenatal diagnosis was performed for two offspring of one affected person. In the first pregnancy, the fetus was shown to carry the duplication while in the second pregnancy, the fetus was shown to be homozygous for the normal allele. Conclusions: The results show that in some individuals within one family, duplication of this segment of PITX3 can result in severe symptoms leading to functional blindness while in other individuals in the same family or in other families, the same duplication leads to treatable cataract with minimal visual impairment

Interleaved text/image Deep Mining on a large-scale radiology database

Despite tremendous progress in computer vision, effec-tive learning on very large-scale (> 100K patients) medi-cal image databases has been vastly hindered. We present an interleaved text/image deep learning system to extract and mine the semantic interactions of radiology images and reports from a national research hospital’s picture archiv-ing and communication system. Instead of using full 3D medical volumes, we focus on a collection of representa-tive ~216K 2D key images/slices (selected by clinicians for diagnostic reference) with text-driven scalar and vector la-bels. Our system interleaves between unsupervised learn-ing (e.g., latent Dirichlet allocation, recurrent neural net language models) on document- and sentence-level texts to generate semantic labels and supervised learning via deep convolutional neural networks (CNNs) to map from images to label spaces. Disease-related key words can be predicted for radiology images in a retrieval manner. We have demon-strated promising quantitative and qualitative results. The large-scale datasets of extracted key images and their cat-egorization, embedded vector labels and sentence descrip-tions can be harnessed to alleviate the deep learning “data-hungry ” obstacle in the medical domain

Comparative Transcriptomic Profiling and Gene Expression for Myxomatous Mitral Valve Disease in the Dog and Human

Myxomatous mitral valve disease is the single most important mitral valve disease in both dogs and humans. In the case of the dog it is ubiquitous, such that all aged dogs will have some evidence of the disease, and for humans it is known as Barlow’s disease and affects up to 3% of the population, with an expected increase in prevalence as the population ages. Disease in the two species show many similarities and while both have the classic myxomatous degeneration only in humans is there extensive fibrosis. This dual pathology of the human disease markedly affects the valve transcriptome and the difference between the dog and human is dominated by changes in genes associated with fibrosis. This review will briefly examine the comparative valve pathology and then, in more detail, the transcriptomic profiling and gene expression reported so far for both species

Clinical and Echocardiographic Findings in an Aged Population of Cavalier King Charles Spaniels

Myxomatous mitral valve disease (MMVD) is the most common cardiac disease in dogs. It varies from dogs without clinical signs to those developing left-sided congestive heart failure, leading to death. Cavalier King Charles Spaniels (CKCSs) are particularly susceptible to MMVD. We hypothesised that within the elderly CKCS population, there is a sub-cohort of MMVD-affected dogs that do not have cardiac remodelling. The objectives of the present study were (i) to determine the prevalence and the degree of cardiac remodelling associated with MMVD; and (ii) assess the effect of age, gender, and body weight on echocardiographic status in a population of aged CKCSs. A total of 126 CKCSs ≥ 8 years old were prospectively included. They all had a physical and echocardiographic examination. A systolic murmur was detected in 89% of dogs; the presence of clinical signs was reported in 19% of them; and echocardiographic evidence of MMVD was described in 100%. Despite the high prevalence, 44.4% of the dogs were clear of echocardiographic signs of cardiac remodelling. Age was significantly associated with the presence and severity of cardiac remodelling and mitral valve prolapse. Our results showed that a proportion of elderly CKCS with confirmed MMVD did not undergo advanced stages of this pathology.This research was funded by the British CKCS Breed Club and The Roslin Institute, University of Edinburgh

Using Calibrated Peer Review⢠to Assess and Improve the Quality of Student Documentation of Clinical Encounters at the University of New Mexico School of Medicine (UNMSOM).

The UNMSOM adapted Calibrated Peer Review™, an internet based writing tool, to assist medical students in assessing the structure and content of their clinical notes. Students watch videotaped clinical encounters and write notes based on these patient visits. Students then apply faculty-established standards to assess three calibration notes, the notes of three peers, and their own note. CRR will be demonstrated and student satisfaction described