3D Depthwise Convolution: Reducing Model Parameters in 3D Vision Tasks

Standard 3D convolution operations require much larger amounts of memory and computation cost than 2D convolution operations. The fact has hindered the development of deep neural nets in many 3D vision tasks. In this paper, we investigate the possibility of applying depthwise separable convolutions in 3D scenario and introduce the use of 3D depthwise convolution. A 3D depthwise convolution splits a single standard 3D convolution into two separate steps, which would drastically reduce the number of parameters in 3D convolutions with more than one order of magnitude. We experiment with 3D depthwise convolution on popular CNN architectures and also compare it with a similar structure called pseudo-3D convolution. The results demonstrate that, with 3D depthwise convolutions, 3D vision tasks like classification and reconstruction can be carried out with more light-weighted neural networks while still delivering comparable performances.Comment: Work in progres

Globally Guided Progressive Fusion Network for 3D Pancreas Segmentation

Recently 3D volumetric organ segmentation attracts much research interest in medical image analysis due to its significance in computer aided diagnosis. This paper aims to address the pancreas segmentation task in 3D computed tomography volumes. We propose a novel end-to-end network, Globally Guided Progressive Fusion Network, as an effective and efficient solution to volumetric segmentation, which involves both global features and complicated 3D geometric information. A progressive fusion network is devised to extract 3D information from a moderate number of neighboring slices and predict a probability map for the segmentation of each slice. An independent branch for excavating global features from downsampled slices is further integrated into the network. Extensive experimental results demonstrate that our method achieves state-of-the-art performance on two pancreas datasets.Comment: MICCAI201

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

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

OXA- and GES-type beta-lactamases predominate in extensively drug-resistant Acinetobacter baumannii isolates from a Turkish University Hospital

This study was supported by grants from Recep Tayyip Erdogan University (BAP-2012.106.01.11 and BAP-2011.102.03.3). AYP was supported by the Australian National Health and Medical Research Council (APP1047916 and APP1010114).We determined the antibiotic susceptibility and genetic mechanisms of resistance in clinical strains of Acinetobacter baumannii from Istanbul, Turkey. A total of 101 clinical strains were collected between November 2011 and July 2012. Antimicrobial susceptibility was performed using the Vitek 2 Compact system and E-test. Multiplex PCR was used for detecting bla(OXA-51-like), bla(OXA-23-like), bla(OXA-40-like) and bla(OXA-58-like) genes. ISAba1, bla(IMP-like), bla(VIM-like), bla(GES), bla(VEB), bla(PER-2), aac-3-Ia and aac-6'-Ib and NDM-1 genes were detected by PCR and sequencing. By multiplex PCR, all strains were positive for bla(OXA-51), 79 strains carried bla(OXA-23) and one strain carried bla(OXA-40). bla(OXA-51) and bla(OXA-23) were found together in 79 strains. ISAba1 element was detected in 81 strains, and in all cases it was found upstream of bla(OXA-51). GES-type carbapenemases were found in 24 strains (GES-11 in 16 strains and GES-22 in 8 strains) while bla(PER-2), bla(VEB-1), bla(NDM-1), bla(IMP)- and bla(VIM)-type carbapenemases were not observed. Aminoglycoside modifying enzyme (aac-3-Ia and aac-6'Ib) genes were detected in 13 and 15 strains, respectively. Ninety-seven (96%) A. baumannii strains were defined as MDR and of these, 98% were extensively drug resistant (sensitive only to colistin). Colistin remains the only active compound against all clinical strains. As seen in other regions, OXA-type carbapenemases, with or without an upstream ISAba1, predominate but GES-type carbapenemases also appear to have a significant presence. REP-PCR analysis was performed for molecular typing and all strains were collected into 12 different groups. To our knowledge, this is the first report of GES-11 and OXA-40 in A. baumannii from Turkey

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%

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

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

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