Observation on the age, growth and somatic condition of Carasobarbus luteus (Heckel, 1843) and Capoeta trutta (Heckel, 1843) (Cyprinidae) in the Tigris River, Turkey

This study was carried out to determine some biological characteristics including age, growth and somatic condition of Carasobarbus luteus and Capoeta trutta in the Turkish part of the Tigris River. The examined samples of C. luteus were distributed between II-IX years of age. The length-weigth relations of females and males were calculated as Log W =-4.7314 +3.0113 Log FL and Log W = -4.7631 +3.0263 Log FL respectively. Von Bertalanffy growth equations were estimated as Lt=40.09 [1-e^-0.087036 (t+1.55004)] for females and Lt=38.14 [1-e^-0.080056 (t+2.34838)] for males. The somatic condition was 1.9667 ± 0.1751 for females and 1.9967 ± 0.4205 for males. The observed samples of C. trutta were distributed between I-VI years of age. The length-weigth relationship of females and males were calculated as Log W = -4.6845 + 2.9303 Log FL, Log W = -4.7784 + 2.9746 Log FL, respectively. Von Bertalanffy growth equations were estimated as Lt=35.36 [1-e^-0.082817 (t+4.82738)] for females and Lt=28.82 [1-e^-0.12380 (t+4.40235)] for males. The somatic condition in female and male individuals were determined as; 1.4434 ± 0.1682 and 1.4722 ± 0.1984 respectively. Both species are economic fish in the Tigris River. Biological characteristics of the species determined in the present study, may contribute to a better understanding of the life cycle, thus providing useful data for its conservation and management

The unique coclique extension property for apartments of buildings

We show that the Kneser graph of objects of a fixed type in a building of spherical type has the unique coclique extension property when the corresponding representation has minuscule weight and also when the diagram is simply laced and the representation is adjoint

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

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

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

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

The evaluation of morphology of renal pelvicalyceal system’s and infundibulopelvic anatomy of kidney’s lower pole in post-mortem series

Background: Urinary system stones are frequently encountered in the community. Together with technological developments, introduction of new treatment procedures such as extracorporeal shock wave lithotripsy, percutaneous nephrolithotomy and retrograde intrarenal surgery has furtherly reduced morbidity, mortality and hospitalization time of patients. In order to maximize success and to reduce complications of these procedures, it is necessary to evaluate anatomy and morphological differences of kidney collector system before the procedure. This study was conducted for the purpose of determining the morphology of the kidney collector system and the negative anatomic factors of the lower pole in autopsy cases performed in our institution. Materials and methods: 82 kidney units obtained from 41 autopsy cases conducted in Faculty of Medicine Department of Forensic Medicine, Sivas Cumhuriyet University between September 2017 and September 2018 were included in the study. Percentages were found as 78% for intrarenal pelvis, 13.4% for borderline pelvis, %6.1 for extrarenal pelvis and 2.4% for pelvic nonexistence. When pelvicalyceal anatomy was evaluated, percentages were found as 32.9% for bicalyceal, 26.8% for tricalyceal, 20.7% for multicalyceal and 19.5% for unclassified calyceality. When it is evaluated according to opening of calyces into the renal pelvis based on Sampaio classification, percentages were found as 30.5% for AI, 17.1% for Type II, 28% for BI, 18.3% for BII and 6.1% for unevaluated part. Infundibular lengths of kidney’s lower pole were detected as under 3 cm in 39% and over 3 cm in 61% of all cases. Infundibulopelvic angles of kidney’s lower pole were measured as under 700 in 42.7% and over 700 in 57.3% of all cases. Results: In our study, there was no statistically significant difference between the right and left kidneys in terms of collecting system morphology and lower pole’s negative anatomical factors. Only infindibular lengths which is one of the collecting system morphology and lower pole’s negative anatomical factors were statistically shorter in females than males. There was no difference in terms of other parameters. Conclusions: In conclusion, the findings of this study are largely consistent with the results of similar studies. This reveals that renal collecting system morphology and negative anatomic factors in the lower pole collecting system in human are roughly similar. In clinical practice, pre-treatment CT and, if necessary, MR urography evaluation of the lower pole negative anatomic factors may contribute to gain preliminary information about both the clearance of stone fragments especially after SWL and RIRS procedures and perioperative complications proactively

Deep Learning for Brain Tumor Segmentation in Radiosurgery: Prospective Clinical Evaluation

Stereotactic radiosurgery is a minimally-invasive treatment option for a large number of patients with intracranial tumors. As part of the therapy treatment, accurate delineation of brain tumors is of great importance. However, slice-by-slice manual segmentation on T1c MRI could be time-consuming (especially for multiple metastases) and subjective. In our work, we compared several deep convolutional networks architectures and training procedures and evaluated the best model in a radiation therapy department for three types of brain tumors: meningiomas, schwannomas and multiple brain metastases. The developed semiautomatic segmentation system accelerates the contouring process by 2.2 times on average and increases inter-rater agreement from 92.0% to 96.5%