Analysis of effects of meteorological factors on air pollutant concentrations in Ankara, Turkey

In big cities, the air pollution has become an important problem in parallel with the increasing energy use. The sources of the pollutants are the emissions from the industrial facilities, motor vehicles and heating systems. The climatologic factors play important roles on the concentration of the air pollutants. In this study, the relations between air pollutant (SO2, PM10, NO, NO2 and CO) concentrations and the meteorological factors (wind speed, temperature and relative humidity)w ere statistically analyzed for the period of November 2001 and April 2002. The multi-linear regression analysis was applied to quantify the relationship between the air-polluting elements and the climatic factors by using a SPSS program. The results of the analysis show that the concentrations of all the pollutants considered decrease with increasing wind speed. With the increasing temperature, SO2, PM10 and CO concentrations decrease. However, there is not a clear relation between temperature, and NO and NO2 concentrations. Changes in SO2, PM10, NO and NO2 concentrations with the changing relative humidity are insignificant. However, CO concentration increases with increasing relative humidity

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

Early growth performances of various seed sources of black (Prunus serotina Erhr.) and wild cherry (Prunus avium L.) seedlings on low and high elevation sites in the western Black Sea Region of Turkey

The growth performances of one-year old seedlings of various black cherry (BC) and wild cherry (WC) seed sources (SSs) that were planted on low elevation sites (LES) and high elevation sites (HES) in the western Black Sea Region (BSR) of Turkey were assessed one and five years after planting (YAP). Significance between and within-species variations were found for seedling growth. On species basis, WC was superior to BC for seedling groundline diameter and height growth for the low elevation sites(LES) of one and five years after planting (YAP), whereas no substantial survival and growth differences were found between the species for the high elevation sites (HES) of five YAP. Generally, seedlings averaged a greater survival on the LES, when compared with those on the HES. Local WC SSs (Tefen, Yayla and Dirgine) demonstrated an enhanced seedling survival and growth on LES than the other SSs. Unlike the LES results, a collection of BC (Michigan 1 and Ukraine) and WC SSs (Dirgine, Germany, and Tefen) displayed the best seedling growth over five years. The HES seedlings frequently experienced diebacks and forking due to heavy snow fall and wildlife browsing. Selection of the local WC SSs was vital for the LES. However, BC SSs may present a potential for planting on the HES with harsher environmental conditions.Keywords: Black cherry, provenance test, seedling growth and survival, wild cherry

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

RS-Net: Regression-Segmentation 3D CNN for Synthesis of Full Resolution Missing Brain MRI in the Presence of Tumours

Accurate synthesis of a full 3D MR image containing tumours from available MRI (e.g. to replace an image that is currently unavailable or corrupted) would provide a clinician as well as downstream inference methods with important complementary information for disease analysis. In this paper, we present an end-to-end 3D convolution neural network that takes a set of acquired MR image sequences (e.g. T1, T2, T1ce) as input and concurrently performs (1) regression of the missing full resolution 3D MRI (e.g. FLAIR) and (2) segmentation of the tumour into subtypes (e.g. enhancement, core). The hypothesis is that this would focus the network to perform accurate synthesis in the area of the tumour. Experiments on the BraTS 2015 and 2017 datasets [1] show that: (1) the proposed method gives better performance than state-of-the-art methods in terms of established global evaluation metrics (e.g. PSNR), (2) replacing real MR volumes with the synthesized MRI does not lead to significant degradation in tumour and sub-structure segmentation accuracy. The system further provides uncertainty estimates based on Monte Carlo (MC) dropout [11] for the synthesized volume at each voxel, permitting quantification of the system's confidence in the output at each location.Comment: Accepted at Workshop on Simulation and Synthesis in Medical Imaging - SASHIMI2018 held in conjunction with the 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2018

Brain Tumor Segmentation and Tractographic Feature Extraction from Structural MR Images for Overall Survival Prediction

This paper introduces a novel methodology to integrate human brain connectomics and parcellation for brain tumor segmentation and survival prediction. For segmentation, we utilize an existing brain parcellation atlas in the MNI152 1mm space and map this parcellation to each individual subject data. We use deep neural network architectures together with hard negative mining to achieve the final voxel level classification. For survival prediction, we present a new method for combining features from connectomics data, brain parcellation information, and the brain tumor mask. We leverage the average connectome information from the Human Connectome Project and map each subject brain volume onto this common connectome space. From this, we compute tractographic features that describe potential neural disruptions due to the brain tumor. These features are then used to predict the overall survival of the subjects. The main novelty in the proposed methods is the use of normalized brain parcellation data and tractography data from the human connectome project for analyzing MR images for segmentation and survival prediction. Experimental results are reported on the BraTS2018 dataset.Comment: 14 pages, 5 figures, 4 tables, accepted by BrainLes 2018 MICCAI worksho

Prediction of Thrombectomy Functional Outcomes using Multimodal Data

Recent randomised clinical trials have shown that patients with ischaemic stroke {due to occlusion of a large intracranial blood vessel} benefit from endovascular thrombectomy. However, predicting outcome of treatment in an individual patient remains a challenge. We propose a novel deep learning approach to directly exploit multimodal data (clinical metadata information, imaging data, and imaging biomarkers extracted from images) to estimate the success of endovascular treatment. We incorporate an attention mechanism in our architecture to model global feature inter-dependencies, both channel-wise and spatially. We perform comparative experiments using unimodal and multimodal data, to predict functional outcome (modified Rankin Scale score, mRS) and achieve 0.75 AUC for dichotomised mRS scores and 0.35 classification accuracy for individual mRS scores.Comment: Accepted at Medical Image Understanding and Analysis (MIUA) 202

Retrospective head motion estimation in structural brain MRI with 3D CNNs

Head motion is one of the most important nuisance variables in neuroimaging, particularly in studies of clinical or special populations, such as children. However, the possibility of estimating motion in structural MRI is limited to a few specialized sites using advanced MRI acquisition techniques. Here we propose a supervised learning method to retrospectively estimate motion from plain MRI. Using sparsely labeled training data, we trained a 3D convolutional neural network to assess if voxels are corrupted by motion or not. The output of the network is a motion probability map, which we integrate across a region of interest (ROI) to obtain a scalar motion score. Using cross-validation on a dataset of n=48 healthy children scanned at our center, and the cerebral cortex as ROI, we show that the proposed measure of motion explains away 37% of the variation in cortical thickness. We also show that the motion score is highly correlated with the results from human quality control of the scans. The proposed technique can not only be applied to current studies, but also opens up the possibility of reanalyzing large amounts of legacy datasets with motion into consideration: we applied the classifier trained on data from our center to the ABIDE dataset (autism), and managed to recover group differences that were confounded by motion