Clawing the Way to a New Life: Narratives of Divorced Women in Turkey

Basierend auf ihrer MA-Thesis in „Kritischen Kulturwissenschaften“ geht M. Esra Yıldırım in dem vorliegenden Buch auf die Erzählung der Lebensgeschichte von 28 geschiedenen Frauen aus der Türkei ein. Ausgehend vom analytischen Rahmen geschlechts- und altersbezogener Hierarchien in der Türkei, setzt sich die Autorin mit den Erfahrungen der Teilnehmerinnen zu Vaterschaft im Elternhaus, Maskulinität und Femininität in der Ehe und den unterschiedlichen Formen von Sexismus nach der Scheidung auseinander. Mit einer Vielzahl von Erzählungen, die die Lesenden in ihren Bann ziehen, trägt Yeni Bir Hayat Kurmak nicht nur zur kulturwissenschaftlichen und feministischen Literatur bei, sondern spricht auch ein größeres nicht-akademisches Publikum an.Based on her MA research in critical and cultural studies, M. Esra Yıldırım unrolls the life story narratives of 28 divorced women in Turkey. Departing from an analytical framework of age and gender hierarchies in Turkey, the author tackles the participants’ experiences with fatherhood in their families of origin, with masculinity and femininity in marriage, and with diverse peculiar forms of sexism after divorce. Through a variety of narratives that captivate the reader, Yeni Bir Hayat Kurmak not only contributes to cultural studies and feminist literature but also speaks to a large non-academic audience

GP-Unet: Lesion Detection from Weak Labels with a 3D Regression Network

We propose a novel convolutional neural network for lesion detection from weak labels. Only a single, global label per image - the lesion count - is needed for training. We train a regression network with a fully convolutional architecture combined with a global pooling layer to aggregate the 3D output into a scalar indicating the lesion count. When testing on unseen images, we first run the network to estimate the number of lesions. Then we remove the global pooling layer to compute localization maps of the size of the input image. We evaluate the proposed network on the detection of enlarged perivascular spaces in the basal ganglia in MRI. Our method achieves a sensitivity of 62% with on average 1.5 false positives per image. Compared with four other approaches based on intensity thresholding, saliency and class maps, our method has a 20% higher sensitivity.Comment: Article published in MICCAI 2017. We corrected a few errors from the first version: padding, loss, typos and update of the DOI numbe

Comparison of type I collagens and MMP-2 proteins in temporomandibular joint of young and old mice

Background: The effects of ageing on the histopathological changes of tem­poromandibular joint (TMJ) and the existence and age related alterations of immunochemical expressions of type I collagen and matrix metalloproteinase-2 (MMP-2) proteins was aimed to be displayed. Materials and methods: In this study, 14 Balb/C type white mice (50– –80 g) were included. Groups were organised as group 1 — 2-month-old young animals (n = 7) and group 2 — 18-month-old old animals (n = 7). Of the paraffin embedded tissues 4–5 μm thick sections were taken and immunohisto­chemical stainings of haematoxylin-eosin, type-1 collagen and MMP-2 were performed. Results: Collagen bundles showed sagittal and oblique localisations in the young mice, which were comprised of compact collagen bundle layers positioned alterna­tely. While collagen bundle fragmentation was observed in the disks of old mice, some disk regions showed ruptures. In the old mice a decrease in blood vessels, structural impairments and dilatation in arterioles and venules were detected. In the TMJ tissues of the young mice type I collagen and MMP-2 expressions were increased, while they were decreased in old mice. In the MMP-2 H-score evaluation young mice showed significant increase compared to the old mice. Conclusions: Occurrence of degenerations in the collagen structure of TMJ and decimation in the matrix metalloproteases were observed with age. (Folia Morphol 2018; 77, 2: 329–334

Robust inference of kinase activity using functional networks

Mass spectrometry enables high-throughput screening of phosphoproteins across a broad range of biological contexts. When complemented by computational algorithms, phospho-proteomic data allows the inference of kinase activity, facilitating the identification of dysregulated kinases in various diseases including cancer, Alzheimer’s disease and Parkinson’s disease. To enhance the reliability of kinase activity inference, we present a network-based framework, RoKAI, that integrates various sources of functional information to capture coordinated changes in signaling. Through computational experiments, we show that phosphorylation of sites in the functional neighborhood of a kinase are significantly predictive of its activity. The incorporation of this knowledge in RoKAI consistently enhances the accuracy of kinase activity inference methods while making them more robust to missing annotations and quantifications. This enables the identification of understudied kinases and will likely lead to the development of novel kinase inhibitors for targeted therapy of many diseases. RoKAI is available as web-based tool at http://rokai.io

Tversky loss function for image segmentation using 3D fully convolutional deep networks

Fully convolutional deep neural networks carry out excellent potential for fast and accurate image segmentation. One of the main challenges in training these networks is data imbalance, which is particularly problematic in medical imaging applications such as lesion segmentation where the number of lesion voxels is often much lower than the number of non-lesion voxels. Training with unbalanced data can lead to predictions that are severely biased towards high precision but low recall (sensitivity), which is undesired especially in medical applications where false negatives are much less tolerable than false positives. Several methods have been proposed to deal with this problem including balanced sampling, two step training, sample re-weighting, and similarity loss functions. In this paper, we propose a generalized loss function based on the Tversky index to address the issue of data imbalance and achieve much better trade-off between precision and recall in training 3D fully convolutional deep neural networks. Experimental results in multiple sclerosis lesion segmentation on magnetic resonance images show improved F2 score, Dice coefficient, and the area under the precision-recall curve in test data. Based on these results we suggest Tversky loss function as a generalized framework to effectively train deep neural networks

Synaptic Cleft Segmentation in Non-Isotropic Volume Electron Microscopy of the Complete Drosophila Brain

Neural circuit reconstruction at single synapse resolution is increasingly recognized as crucially important to decipher the function of biological nervous systems. Volume electron microscopy in serial transmission or scanning mode has been demonstrated to provide the necessary resolution to segment or trace all neurites and to annotate all synaptic connections. Automatic annotation of synaptic connections has been done successfully in near isotropic electron microscopy of vertebrate model organisms. Results on non-isotropic data in insect models, however, are not yet on par with human annotation. We designed a new 3D-U-Net architecture to optimally represent isotropic fields of view in non-isotropic data. We used regression on a signed distance transform of manually annotated synaptic clefts of the CREMI challenge dataset to train this model and observed significant improvement over the state of the art. We developed open source software for optimized parallel prediction on very large volumetric datasets and applied our model to predict synaptic clefts in a 50 tera-voxels dataset of the complete Drosophila brain. Our model generalizes well to areas far away from where training data was available

Neural Style Transfer Improves 3D Cardiovascular MR Image Segmentation on Inconsistent Data

Three-dimensional medical image segmentation is one of the most important problems in medical image analysis and plays a key role in downstream diagnosis and treatment. Recent years, deep neural networks have made groundbreaking success in medical image segmentation problem. However, due to the high variance in instrumental parameters, experimental protocols, and subject appearances, the generalization of deep learning models is often hindered by the inconsistency in medical images generated by different machines and hospitals. In this work, we present StyleSegor, an efficient and easy-to-use strategy to alleviate this inconsistency issue. Specifically, neural style transfer algorithm is applied to unlabeled data in order to minimize the differences in image properties including brightness, contrast, texture, etc. between the labeled and unlabeled data. We also apply probabilistic adjustment on the network output and integrate multiple predictions through ensemble learning. On a publicly available whole heart segmentation benchmarking dataset from MICCAI HVSMR 2016 challenge, we have demonstrated an elevated dice accuracy surpassing current state-of-the-art method and notably, an improvement of the total score by 29.91\%. StyleSegor is thus corroborated to be an accurate tool for 3D whole heart segmentation especially on highly inconsistent data, and is available at https://github.com/horsepurve/StyleSegor.Comment: 22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2019) early accep

Uncertainty quantification in medical image segmentation with normalizing flows

Medical image segmentation is inherently an ambiguous task due to factors such as partial volumes and variations in anatomical definitions. While in most cases the segmentation uncertainty is around the border of structures of interest, there can also be considerable inter-rater differences. The class of conditional variational autoencoders (cVAE) offers a principled approach to inferring distributions over plausible segmentations that are conditioned on input images. Segmentation uncertainty estimated from samples of such distributions can be more informative than using pixel level probability scores. In this work, we propose a novel conditional generative model that is based on conditional Normalizing Flow (cFlow). The basic idea is to increase the expressivity of the cVAE by introducing a cFlow transformation step after the encoder. This yields improved approximations of the latent posterior distribution, allowing the model to capture richer segmentation variations. With this we show that the quality and diversity of samples obtained from our conditional generative model is enhanced. Performance of our model, which we call cFlow Net, is evaluated on two medical imaging datasets demonstrating substantial improvements in both qualitative and quantitative measures when compared to a recent cVAE based model.Comment: 12 pages. Accepted to be presented at 11th International Workshop on Machine Learning in Medical Imaging. Source code will be updated at https://github.com/raghavian/cFlo

Automated fetal brain extraction from clinical Ultrasound volumes using 3D Convolutional Neural Networks

To improve the performance of most neuroimiage analysis pipelines, brain extraction is used as a fundamental first step in the image processing. But in the case of fetal brain development, there is a need for a reliable US-specific tool. In this work we propose a fully automated 3D CNN approach to fetal brain extraction from 3D US clinical volumes with minimal preprocessing. Our method accurately and reliably extracts the brain regardless of the large data variation inherent in this imaging modality. It also performs consistently throughout a gestational age range between 14 and 31 weeks, regardless of the pose variation of the subject, the scale, and even partial feature-obstruction in the image, outperforming all current alternatives.Comment: 13 pages, 7 figures, MIUA conferenc