Isointense infant brain MRI segmentation with a dilated convolutional neural network

Quantitative analysis of brain MRI at the age of 6 months is difficult because of the limited contrast between white matter and gray matter. In this study, we use a dilated triplanar convolutional neural network in combination with a non-dilated 3D convolutional neural network for the segmentation of white matter, gray matter and cerebrospinal fluid in infant brain MR images, as provided by the MICCAI grand challenge on 6-month infant brain MRI segmentation.Comment: MICCAI grand challenge on 6-month infant brain MRI segmentatio

Routes to optical activity. Catalytic stereoslective syntesis of chiral epoxides selenides and sulfides.

The principle aim of this work described in this thesis was the study of catalytic enantioselective reactions and the factors wich determinate the extent of induction. ... Zie: p. 9-1

Exploring the similarity of medical imaging classification problems

Supervised learning is ubiquitous in medical image analysis. In this paper we consider the problem of meta-learning -- predicting which methods will perform well in an unseen classification problem, given previous experience with other classification problems. We investigate the first step of such an approach: how to quantify the similarity of different classification problems. We characterize datasets sampled from six classification problems by performance ranks of simple classifiers, and define the similarity by the inverse of Euclidean distance in this meta-feature space. We visualize the similarities in a 2D space, where meaningful clusters start to emerge, and show that the proposed representation can be used to classify datasets according to their origin with 89.3\% accuracy. These findings, together with the observations of recent trends in machine learning, suggest that meta-learning could be a valuable tool for the medical imaging community

Selling Ourselves Short: A Discussion of Water-Markets in Alberta

The issue of water management has become one of increasing importance. Any new policy regarding resource management must balance the needs of the environment, the municipalities, and industry. In an effort to reconcile these needs this report will review the best-practices of water policy. Specifically, the reason for undertaking this report is to research policy options available to the Alberta government to provide a framework for improving the Water for Life strategy. One generalization that can be made across the spectrum of privatization models is that whenever a resource is labelled a commodity, the objective to sell it for a profit invariably undermines the aquatic ecology at the source. The report identifies a common practice where industrial entities pay for water on a sliding scale, (if they are made to pay anything at all) with bulk water purchases becoming cheaper as more water is consumed. By applying a conservation-orientated system to industrial users, the minimum amount of water is available, but heavy fees are to be assigned for exceeding the allotment. An industrial system resembling the conservation-orientated approach could also add an extra incentive to recycle water used for industrial purposes. A policy of conservation-orientated charging applied to both municipalities and industry offers the best aspects of water leases and conservation enforcement. Ultimately, the research finds that private ownership of water offers more detrimental than beneficial for the people of Alberta

Citizenship Education in a Fragile State: NGO Programs for Democratic Development and Youth Participation in Haiti

This research centres on NGO citizenship education programs in Haiti to better understand youth experiences, outcomes, and perceptions of democracy. The findings from this study illustrate how programs from Western-based NGOs with liberal democratic traditions typically construct citizenship education in relation to the individual agency of the learners, whereas youth living in the context of fragility note the prerequisite for stable social structures as a foundation for citizenship. Through multi-dimensional analyses, this article highlights the importance of historical perspectives, the value of comparing disparate societies, and the necessity to explicate social locations in cross-cultural research. The concluding proposition states that not only does context matter in international research, but illustrates specifically how context affects youth participants subject to curriculum emanating from competing ideological environments. The issues explored here are among the key concerns for the future of comparative and international research in a globalizing and diverse world

Domain-adversarial neural networks to address the appearance variability of histopathology images

Preparing and scanning histopathology slides consists of several steps, each with a multitude of parameters. The parameters can vary between pathology labs and within the same lab over time, resulting in significant variability of the tissue appearance that hampers the generalization of automatic image analysis methods. Typically, this is addressed with ad-hoc approaches such as staining normalization that aim to reduce the appearance variability. In this paper, we propose a systematic solution based on domain-adversarial neural networks. We hypothesize that removing the domain information from the model representation leads to better generalization. We tested our hypothesis for the problem of mitosis detection in breast cancer histopathology images and made a comparative analysis with two other approaches. We show that combining color augmentation with domain-adversarial training is a better alternative than standard approaches to improve the generalization of deep learning methods.Comment: MICCAI 2017 Workshop on Deep Learning in Medical Image Analysi

Inferring a Third Spatial Dimension from 2D Histological Images

Histological images are obtained by transmitting light through a tissue specimen that has been stained in order to produce contrast. This process results in 2D images of the specimen that has a three-dimensional structure. In this paper, we propose a method to infer how the stains are distributed in the direction perpendicular to the surface of the slide for a given 2D image in order to obtain a 3D representation of the tissue. This inference is achieved by decomposition of the staining concentration maps under constraints that ensure realistic decomposition and reconstruction of the original 2D images. Our study shows that it is possible to generate realistic 3D images making this method a potential tool for data augmentation when training deep learning models.Comment: IEEE International Symposium on Biomedical Imaging (ISBI), 201