Fast, Simple Calcium Imaging Segmentation with Fully Convolutional Networks

Calcium imaging is a technique for observing neuron activity as a series of images showing indicator fluorescence over time. Manually segmenting neurons is time-consuming, leading to research on automated calcium imaging segmentation (ACIS). We evaluated several deep learning models for ACIS on the Neurofinder competition datasets and report our best model: U-Net2DS, a fully convolutional network that operates on 2D mean summary images. U-Net2DS requires minimal domain-specific pre/post-processing and parameter adjustment, and predictions are made on full $512\times512$ images at $\approx$9K images per minute. It ranks third in the Neurofinder competition ($F_1=0.569$) and is the best model to exclusively use deep learning. We also demonstrate useful segmentations on data from outside the competition. The model's simplicity, speed, and quality results make it a practical choice for ACIS and a strong baseline for more complex models in the future.Comment: Accepted to 3rd Workshop on Deep Learning in Medical Image Analysis (http://cs.adelaide.edu.au/~dlmia3/

INTERNET USAGE PURPOSES OF PRIMARY SCHOOL STUDENTS: THE CASE STUDY OF ERZURUM PROVINCE, TURKEY

The objective of this study is to carry out a research on the internet usage purposes of primary school students. In line with this objective, the internet usage frequency and purposes of students, including the intervention of their parents were studied. In this study, a descriptive research model was used, as it was aimed at making an assessment in line with the views of students. Within this scope, a questionnaire with open ended questions was used. 143 students participated in the students, from 3rd and 4th grade, studying at two state schools in the center of Erzurum Province, who were randomly selected. The answers given by students for 5 questions were categorized based on similarity and differences, as well as calculating the percentage rates and frequency values. The findings obtained from the study suggest that the students use internet with certain intervals, and that they mostly use internet via mobile phones. It was also detected that the parents intervene in the internet usage of their children by imposing a time limit. It was detected that the students mostly use internet for “accessing information” and “making research”, but still with a high frequency of usage for playing games and watching cartoons. These results show that the educational institutions and the parents bear tremendous responsibility in order to ensure that the children use internet effectively and that they are protected against the dangers they may face during the time they spend surfing on the internet. The educational institutions should bring the students with computer skills, as well as training them on the reasons and manners of using internet, the problems they may face, internet usage rules, the manners on how to make use of the information obtained from internet.  Article visualizations

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

3DQ: Compact Quantized Neural Networks for Volumetric Whole Brain Segmentation

Model architectures have been dramatically increasing in size, improving performance at the cost of resource requirements. In this paper we propose 3DQ, a ternary quantization method, applied for the first time to 3D Fully Convolutional Neural Networks (F-CNNs), enabling 16x model compression while maintaining performance on par with full precision models. We extensively evaluate 3DQ on two datasets for the challenging task of whole brain segmentation. Additionally, we showcase our method's ability to generalize on two common 3D architectures, namely 3D U-Net and V-Net. Outperforming a variety of baselines, the proposed method is capable of compressing large 3D models to a few MBytes, alleviating the storage needs in space critical applications.Comment: Accepted to MICCAI 201

Comparison of Single Versus Double Intrauterine Insemination

SummaryObjectiveTo compare the outcomes of single versus double intrauterine insemination.Materials and MethodsThis prospective randomized study was carried out in 100 infertile patients. One intrauterine insemination was applied 36 hours after human chorionic gonadotropin (hCG) injection to 50 patients in the first group. To 50 patients in the second group, two intrauterine inseminations were applied, of which the first was applied 24 hours after and the second 48 hours after the hCG injection.ResultsIn the first group, pregnancies were detected in eight patients (pregnancy rate per patient was 16%, pregnancy rate per cycle was 10.6%). In the second group, pregnancies were detected in five patients (pregnancy rate per patient was 10%, pregnancy rate per cycle was 6.4%). There was no statistically significant difference between the two groups (p>0.05).ConclusionSingle intrauterine insemination can be considered to be more reasonable than double intrauterine insemination treatment, taking into consideration the economic cost and the psychologic trauma to the patients. However, further studies with larger sample sizes are needed in order to reveal any actual differences between the two methods

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

Revisiting Rubik's Cube: Self-supervised Learning with Volume-wise Transformation for 3D Medical Image Segmentation

Deep learning highly relies on the quantity of annotated data. However, the annotations for 3D volumetric medical data require experienced physicians to spend hours or even days for investigation. Self-supervised learning is a potential solution to get rid of the strong requirement of training data by deeply exploiting raw data information. In this paper, we propose a novel self-supervised learning framework for volumetric medical images. Specifically, we propose a context restoration task, i.e., Rubik's cube++, to pre-train 3D neural networks. Different from the existing context-restoration-based approaches, we adopt a volume-wise transformation for context permutation, which encourages network to better exploit the inherent 3D anatomical information of organs. Compared to the strategy of training from scratch, fine-tuning from the Rubik's cube++ pre-trained weight can achieve better performance in various tasks such as pancreas segmentation and brain tissue segmentation. The experimental results show that our self-supervised learning method can significantly improve the accuracy of 3D deep learning networks on volumetric medical datasets without the use of extra data.Comment: Accepted by MICCAI 202

Waist Circumference and Mid−Upper Arm Circumference in Evaluation of Obesity in Children Aged Between 6 and 17 Years

Objective: The purpose of this study was to determine the cut−off values for waist circumference (WC) and mid−upper arm circumference (MUAC) and to assess their use in screening for obesity in children