Volumetric Attention for 3D Medical Image Segmentation and Detection

A volumetric attention(VA) module for 3D medical image segmentation and detection is proposed. VA attention is inspired by recent advances in video processing, enables 2.5D networks to leverage context information along the z direction, and allows the use of pretrained 2D detection models when training data is limited, as is often the case for medical applications. Its integration in the Mask R-CNN is shown to enable state-of-the-art performance on the Liver Tumor Segmentation (LiTS) Challenge, outperforming the previous challenge winner by 3.9 points and achieving top performance on the LiTS leader board at the time of paper submission. Detection experiments on the DeepLesion dataset also show that the addition of VA to existing object detectors enables a 69.1 sensitivity at 0.5 false positive per image, outperforming the best published results by 6.6 points.Comment: Accepted by MICCAI 201

The Impact of a Failed Coup d’État on Happiness, Life Satisfaction, and Trust: The Case of the Plot in Turkey on July 15, 2016

This paper examines the impact of the failed coup d’état attempt in Turkey on July 15, 2016, on people’s happiness, life satisfaction, and trust and finds that the plot had a significant negative effect on all three variables. This paper is the first to show that coups d’état can have a significant adverse effect on people’s well-being, as in the case of terrorist attacks

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

Platyceps collaris (Müller 1878), P. najadum (Eichwald 1831), Zamenis hohenackeri

Abstract. The present study is on the morphologies and sizes of peripheral blood cells (erythrocytes, leucocytes and thrombocytes) of thirty two Turkish snake species from blood smears, stained with Wright's stain

Automatic Brain Tumor Segmentation using Convolutional Neural Networks with Test-Time Augmentation

Automatic brain tumor segmentation plays an important role for diagnosis, surgical planning and treatment assessment of brain tumors. Deep convolutional neural networks (CNNs) have been widely used for this task. Due to the relatively small data set for training, data augmentation at training time has been commonly used for better performance of CNNs. Recent works also demonstrated the usefulness of using augmentation at test time, in addition to training time, for achieving more robust predictions. We investigate how test-time augmentation can improve CNNs' performance for brain tumor segmentation. We used different underpinning network structures and augmented the image by 3D rotation, flipping, scaling and adding random noise at both training and test time. Experiments with BraTS 2018 training and validation set show that test-time augmentation helps to improve the brain tumor segmentation accuracy and obtain uncertainty estimation of the segmentation results.Comment: 12 pages, 3 figures, MICCAI BrainLes 201

Shelf life: Neritic habitat use of a turtle population highly threatened by fisheries

This is the final version. Available on open access from Wiley via the DOI in this recordAim: It is difficult to mitigate threats to marine vertebrates until their habitat use is understood. We report on a decade of satellite tracking loggerhead turtles (Caretta caretta) from an important nesting site to determine priority habitats for their protection in a region where they are known to be heavily impacted by fisheries. Location: Cyprus, Eastern Mediterranean. Method: We tracked 27 adult female loggerheads between 2001 and 2012 from North Cyprus nesting beaches. To eliminate potential biases, we included females nesting on all coasts of our study area, at different periods of the nesting season and from a range of size classes. Results: Foraging sites were distributed over the continental shelf of Cyprus, the Levant and North Africa, up to a maximum distance of 2100 km from nesting sites. Foraging sites were clustered in (1) near-shore waters of Cyprus and Syria, (2) offshore waters of Egypt and (3) offshore and near-shore regions of Libya and Tunisia. The North Cyprus and west Egypt/east Libyan coasts are important areas for loggerhead turtles during migration. Movement patterns within foraging sites strongly suggest benthic feeding in discrete areas. Early nesters visited other rookeries in Turkey, Syria and Israel where they likely laid further clutches. Tracking suggests minimum annual mortality of 11%, comparable to other fishery-impacted loggerhead populations. Main conclusions: This work further highlights the importance of neritic habitats of Libya and Tunisia as areas likely used by loggerhead turtles from many of the Mediterranean rookeries and where the threat of fisheries bycatch is high. Our tracking data also suggest that anthropogenic mortalities may have occurred in North Cyprus, Syria and Egypt; all within near-shore marine areas where small-scale fisheries operate. Protection of this species across many geopolitical units is a major challenge and documenting their distribution is an important first step.Peoples Trust for Endangered SpeciesBritish Chelonia GroupUnited States Agency for International DevelopmentBP EgyptApacheNatural Environment Research Council (NERC)Erwin Warth FoundationKuzey Kıbrıs TurkcellEktam KıbrısSEATURTLE.orgMEDASSETDarwin InitiativeBritish High Commission in CyprusBritish Residents Society of North CyprusMarine Turtle Conservation ProjectMarine Turtle Research GroupSociety for the Protection of Turtles in North Cyprus (SPOT)North Cyprus Department of Environmental Protectio

Automatic C-Plane Detection in Pelvic Floor Transperineal Volumetric Ultrasound

© 2020, Springer Nature Switzerland AG. Transperineal volumetric ultrasound (US) imaging has become routine practice for diagnosing pelvic floor disease (PFD). Hereto, clinical guidelines stipulate to make measurements in an anatomically defined 2D plane within a 3D volume, the so-called C-plane. This task is currently performed manually in clinical practice, which is labour-intensive and requires expert knowledge of pelvic floor anatomy, as no computer-aided C-plane method exists. To automate this process, we propose a novel, guideline-driven approach for automatic detection of the C-plane. The method uses a convolutional neural network (CNN) to identify extreme coordinates of the symphysis pubis and levator ani muscle (which define the C-plane) directly via landmark regression. The C-plane is identified in a postprocessing step. When evaluated on 100 US volumes, our best performing method (multi-task regression with UNet) achieved a mean error of 6.05 mm and 4.81 and took 20 s. Two experts blindly evaluated the quality of the automatically detected planes and manually defined the (gold standard) C-plane in terms of their clinical diagnostic quality. We show that the proposed method performs comparably to the manual definition. The automatic method reduces the average time to detect the C-plane by 100 s and reduces the need for high-level expertise in PFD US assessment

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

Assessing the Abundance of Caucasian Salamander, Mertensiella caucasica (Caudata, Salamandridae), with N-mixture Model in Northeastern Anatolia

The endangered Caucasian salamander, Mertensiella caucasica (Waga, 1876), is endemic to the western Lesser Caucasus. Here, we used N-mixed models to analyse repeated count data of Caucasian salamanders from the eastern Black Sea region of Turkey. We estimated a mean detection probability of 0.29, a population size of 21 individuals, and a range of 9 to 36 individuals per 20 × 10 m plot. Our results provide preliminary data on the population status of the Caucasian salamander in northeastern Anatolia. These results would contribute to the effective management and conservation of the species

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