Physical and biochemical quality properties of fermented beef sausages: Bez sucuk

In this study, sucuk samples were obtained from 12 different manufacturers to evaluate some physical and biochemical properties of fermented beef sausages named as “bez sucuk”. It was seen that the titratable acidity values were between 1.02% and 2.25% lactic acid, and pH values of the samples ranged from 5.08 to 5.63 (P2/kg fat, and 0.75–1.17 mg malonaldehyde/kg sample, respectively (P<0.05)

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

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

3D FCN Feature Driven Regression Forest-Based Pancreas Localization and Segmentation

This paper presents a fully automated atlas-based pancreas segmentation method from CT volumes utilizing 3D fully convolutional network (FCN) feature-based pancreas localization. Segmentation of the pancreas is difficult because it has larger inter-patient spatial variations than other organs. Previous pancreas segmentation methods failed to deal with such variations. We propose a fully automated pancreas segmentation method that contains novel localization and segmentation. Since the pancreas neighbors many other organs, its position and size are strongly related to the positions of the surrounding organs. We estimate the position and the size of the pancreas (localized) from global features by regression forests. As global features, we use intensity differences and 3D FCN deep learned features, which include automatically extracted essential features for segmentation. We chose 3D FCN features from a trained 3D U-Net, which is trained to perform multi-organ segmentation. The global features include both the pancreas and surrounding organ information. After localization, a patient-specific probabilistic atlas-based pancreas segmentation is performed. In evaluation results with 146 CT volumes, we achieved 60.6% of the Jaccard index and 73.9% of the Dice overlap.Comment: Presented in MICCAI 2017 workshop, DLMIA 2017 (Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support

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%

Automated Detection of Cortical Lesions in Multiple Sclerosis Patients with 7T MRI

The automated detection of cortical lesions (CLs) in patients with multiple sclerosis (MS) is a challenging task that, despite its clinical relevance, has received very little attention. Accurate detection of the small and scarce lesions requires specialized sequences and high or ultra- high field MRI. For supervised training based on multimodal structural MRI at 7T, two experts generated ground truth segmentation masks of 60 patients with 2014 CLs. We implemented a simplified 3D U-Net with three resolution levels (3D U-Net-). By increasing the complexity of the task (adding brain tissue segmentation), while randomly dropping input channels during training, we improved the performance compared to the baseline. Considering a minimum lesion size of 0.75 μL, we achieved a lesion-wise cortical lesion detection rate of 67% and a false positive rate of 42%. However, 393 (24%) of the lesions reported as false positives were post-hoc confirmed as potential or definite lesions by an expert. This indicates the potential of the proposed method to support experts in the tedious process of CL manual segmentation

Ectopic opening of the common bile duct and duodenal stenosis: an overlooked association

<p>Abstract</p> <p>Background</p> <p>Ectopic opening of the common bile duct into the duodenal bulb (EO-CBD-DB) is a rare disease that may be complicated by duodenal ulcer, deformity, stenosis and biliary stones. The aim of this study is to report clinical presentations, endoscopic diagnosis and treatment of this entity as well as to investigate its association with duodenal stenosis.</p> <p>Methods</p> <p>Gastroduodenoscopic findings and radiological imaging were evaluated for ectopic papilla and duodenal stenosis. Diagnostic methods, endoscopic procedures and long-term outcomes of the endoscopic treatment were presented.</p> <p>Results</p> <p>EO-CBD-DB was found in 74 (77.1%) of the 96 patients with duodenal deformity/stenosis (79 male, 17 female, mean age: 58.5, range: 30-87 years). The papilla with normal appearance was retracted to the bulb in 11 while it was at its usual location in the remaining 11. The history of biliodigestive surgery was more common in patients with EO-CBD-DB who were frequently presented with the common bile duct stone-related symptoms than the other patients. Thirteen (17.6%) of the patients with EO-CBD-DB were referred to surgery. Endoscopic treatment was completed in 60 (81.1%) patients after an average of 1.7 (range: 1-6) procedures. These patients were on follow-up for 24.8 (range: 2-46) months. Endoscopic intervention was required in 12 (20%) of them because of recurrent biliary problems. Treatment of the patient who had stricture due to biliary injury during laparoscopic cholecystectomy is still continued.</p> <p>Conclusions</p> <p>The presence of EO-CBD-DB should be considered particularly in middle-aged male patients who have duodenal deformity/stenosis. Endoscopic treatment is feasible in these patients. The long-term outcomes of endoscopic therapy need to be compared with surgical treatment.</p

Deep learning is widely applicable to phenotyping embryonic development and disease

Genome editing simplifies the generation of new animal models for congenital disorders. However, the detailed and unbiased phenotypic assessment of altered embryonic development remains a challenge. Here, we explore how deep learning (U-Net) can automate segmentation tasks in various imaging modalities, and we quantify phenotypes of altered renal, neural and craniofacial development in Xenopus embryos in comparison with normal variability. We demonstrate the utility of this approach in embryos with polycystic kidneys (pkd1 and pkd2) and craniofacial dysmorphia (six1). We highlight how in toto light-sheet microscopy facilitates accurate reconstruction of brain and craniofacial structures within X. tropicalis embryos upon dyrk1a and six1 loss of function or treatment with retinoic acid inhibitors. These tools increase the sensitivity and throughput of evaluating developmental malformations caused by chemical or genetic disruption. Furthermore, we provide a library of pre-trained networks and detailed instructions for applying deep learning to the reader's own datasets. We demonstrate the versatility, precision and scalability of deep neural network phenotyping on embryonic disease models. By combining light-sheet microscopy and deep learning, we provide a framework for higher-throughput characterization of embryonic model organisms. This article has an associated 'The people behind the papers' interview