395 research outputs found
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 images at 9K images per minute. It
ranks third in the Neurofinder competition () 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
Bidirectional Type Checking for Relational Properties
Relational type systems have been designed for several applications including
information flow, differential privacy, and cost analysis. In order to achieve
the best results, these systems often use relational refinements and relational
effects to maximally exploit the similarity in the structure of the two
programs being compared. Relational type systems are appealing for relational
properties because they deliver simpler and more precise verification than what
could be derived from typing the two programs separately. However, relational
type systems do not yet achieve the practical appeal of their non-relational
counterpart, in part because of the lack of a general foundations for
implementing them.
In this paper, we take a step in this direction by developing bidirectional
relational type checking for systems with relational refinements and effects.
Our approach achieves the benefits of bidirectional type checking, in a
relational setting. In particular, it significantly reduces the need for typing
annotations through the combination of type checking and type inference. In
order to highlight the foundational nature of our approach, we develop
bidirectional versions of several relational type systems which incrementally
combine many different components needed for expressive relational analysis.Comment: 14 page
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