Deep learning-based segmentation of malignant pleural mesothelioma tumor on computed tomography scans: application to scans demonstrating pleural effusion

Tumor volume is a topic of interest for the prognostic assessment, treatment response evaluation, and staging of malignant pleural mesothelioma. Many mesothelioma patients present with, or develop, pleural fluid, which may complicate the segmentation of this disease. Deep convolutional neural networks (CNNs) of the two-dimensional U-Net architecture were trained for segmentation of tumor in the left and right hemithoraces, with the networks initialized through layers pretrained on ImageNet. Networks were trained on a dataset of 5230 axial sections from 154 CT scans of 126 mesothelioma patients. A test set of 94 CT sections from 34 patients, who all presented with both tumor and pleural effusion, in addition to a more general test set of 130 CT sections from 43 patients, were used to evaluate segmentation performance of the deep CNNs. The Dice similarity coefficient (DSC), average Hausdorff distance, and bias in predicted tumor area were calculated through comparisons with radiologist-provided tumor segmentations on the test sets. The present method achieved a median DSC of 0.690 on the tumor and effusion test set and achieved significantly higher performance on both test sets when compared with a previous deep learning-based segmentation method for mesothelioma

Evaluation of sixty-eight cases of fracture stabilisation by external hybrid fixation and a proposal for hybrid construct classification

Background: Hybrid external fixation (HEF) is an emerging technique for fracture stabilization in veterinary orthopedics, but its use has been reported in few papers in the veterinary literature. The linear and circular elements that form hybrid fixators can be connected in a very high number of combinations, and for this reason just referring to HEF without any classification is often misleading about the actual frame structure. The aim of this study was to retrospectively evaluate fracture stabilization by HEF in 58 client-owned dogs and 8 cats, and to extend the already existing classification for hybrid constructs to include all frame configurations used in this study and potentially applicable in clinical settings. Animal signalment, fracture classification, surgical procedure and frame configuration were recorded. Complications, radiographic, functional and cosmetic results were evaluated at the time of fixator removal.Results: Sixty-eight fractures in 58 dogs and eight cats were evaluated. Two dogs had bilateral fractures. Fifty-one percent were radio-ulna, 34% tibial, 9% humeral, 3% femoral and 3% scapular fractures. One ring combined with one or two linear elements was the most widely employed configuration in this case series. Radiographic results at the time of frame removal were excellent in 59% of the cases, good in 38% and fair in 3%, while functional and cosmetic results were excellent in 69% of the cases, good in 27% and fair in 4%.Conclusions: HEF is a useful option for fracture treatment in dogs and cats, particularly for peri and juxta-articular fractures. It can be applied with a minimally invasive approach, allows adjustments during the postoperative period and is a versatile system because of the large variety of combinations that can fit with the specific fracture features. The classification used enables to determine the number of linear and circular elements used in the frame

Case Report: Could Hennebert's Sign Be Evoked Despite Global Vestibular Impairment on Video Head Impulse Test? Considerations Upon Pathomechanisms Underlying Pressure-Induced Nystagmus due to Labyrinthine Fistula

We describe a case series of labyrinthine fistula, characterized by Hennebert's sign (HS) elicited by tragal compression despite global hypofunction of semicircular canals (SCs) on a video-head impulse test (vHIT), and review the relevant literature. All three patients presented with different amounts of cochleo-vestibular loss, consistent with labyrinthitis likely induced by labyrinthine fistula due to different temporal bone pathologies (squamous cell carcinoma involving the external auditory canal in one case and middle ear cholesteatoma in two cases). Despite global hypofunction on vHIT proving impaired function for each SC for high accelerations, all patients developed pressure-induced nystagmus, presumably through spared and/or recovered activity for low-velocity canal afferents. In particular, two patients with isolated horizontal SC fistula developed HS with ipsilesional horizontal nystagmus due to resulting excitatory ampullopetal endolymphatic flows within horizontal canals. Conversely, the last patient with bony erosion involving all SCs developed mainly torsional nystagmus directed contralaterally due to additional inhibitory ampullopetal flows within vertical canals. Moreover, despite impaired measurements on vHIT, we found simultaneous direction-changing positional nystagmus likely due to a buoyancy mechanism within the affected horizontal canal in a case and benign paroxysmal positional vertigo involving the dehiscent posterior canal in another case. Based on our findings, we might suggest a functional dissociation between high (impaired) and low (spared/recovered) accelerations for SCs. Therefore, it could be hypothesized that HS in labyrinthine fistula might be due to the activation of regular ampullary fibers encoding low-velocity inputs, as pressure-induced nystagmus is perfectly aligned with the planes of dehiscent SCs in accordance with Ewald's laws, despite global vestibular impairment on vHIT. Moreover, we showed how pressure-induced nystagmus could present in a rare case of labyrinthine fistulas involving all canals simultaneously. Nevertheless, definite conclusions on the genesis of pressure-induced nystagmus in our patients are prevented due to the lack of objective measurements of both low-acceleration canal responses and otolith function

CASED: Curriculum Adaptive Sampling for Extreme Data Imbalance

We introduce CASED, a novel curriculum sampling algorithm that facilitates the optimization of deep learning segmentation or detection models on data sets with extreme class imbalance. We evaluate the CASED learning framework on the task of lung nodule detection in chest CT. In contrast to two-stage solutions, wherein nodule candidates are first proposed by a segmentation model and refined by a second detection stage, CASED improves the training of deep nodule segmentation models (e.g. UNet) to the point where state of the art results are achieved using only a trivial detection stage. CASED improves the optimization of deep segmentation models by allowing them to first learn how to distinguish nodules from their immediate surroundings, while continuously adding a greater proportion of difficult-to-classify global context, until uniformly sampling from the empirical data distribution. Using CASED during training yields a minimalist proposal to the lung nodule detection problem that tops the LUNA16 nodule detection benchmark with an average sensitivity score of 88.35%. Furthermore, we find that models trained using CASED are robust to nodule annotation quality by showing that comparable results can be achieved when only a point and radius for each ground truth nodule are provided during training. Finally, the CASED learning framework makes no assumptions with regard to imaging modality or segmentation target and should generalize to other medical imaging problems where class imbalance is a persistent problem.Comment: 20th International Conference on Medical Image Computing and Computer Assisted Intervention 201

Preserving Differential Privacy in Convolutional Deep Belief Networks

The remarkable development of deep learning in medicine and healthcare domain presents obvious privacy issues, when deep neural networks are built on users' personal and highly sensitive data, e.g., clinical records, user profiles, biomedical images, etc. However, only a few scientific studies on preserving privacy in deep learning have been conducted. In this paper, we focus on developing a private convolutional deep belief network (pCDBN), which essentially is a convolutional deep belief network (CDBN) under differential privacy. Our main idea of enforcing epsilon-differential privacy is to leverage the functional mechanism to perturb the energy-based objective functions of traditional CDBNs, rather than their results. One key contribution of this work is that we propose the use of Chebyshev expansion to derive the approximate polynomial representation of objective functions. Our theoretical analysis shows that we can further derive the sensitivity and error bounds of the approximate polynomial representation. As a result, preserving differential privacy in CDBNs is feasible. We applied our model in a health social network, i.e., YesiWell data, and in a handwriting digit dataset, i.e., MNIST data, for human behavior prediction, human behavior classification, and handwriting digit recognition tasks. Theoretical analysis and rigorous experimental evaluations show that the pCDBN is highly effective. It significantly outperforms existing solutions

Deep Learning with Lung Segmentation and Bone Shadow Exclusion Techniques for Chest X-Ray Analysis of Lung Cancer

The recent progress of computing, machine learning, and especially deep learning, for image recognition brings a meaningful effect for automatic detection of various diseases from chest X-ray images (CXRs). Here efficiency of lung segmentation and bone shadow exclusion techniques is demonstrated for analysis of 2D CXRs by deep learning approach to help radiologists identify suspicious lesions and nodules in lung cancer patients. Training and validation was performed on the original JSRT dataset (dataset #01), BSE-JSRT dataset, i.e. the same JSRT dataset, but without clavicle and rib shadows (dataset #02), original JSRT dataset after segmentation (dataset #03), and BSE-JSRT dataset after segmentation (dataset #04). The results demonstrate the high efficiency and usefulness of the considered pre-processing techniques in the simplified configuration even. The pre-processed dataset without bones (dataset #02) demonstrates the much better accuracy and loss results in comparison to the other pre-processed datasets after lung segmentation (datasets #02 and #03).Comment: 10 pages, 7 figures; The First International Conference on Computer Science, Engineering and Education Applications (ICCSEEA2018) (www.uacnconf.org/iccseea2018) (accepted

Assessment of Radiologist Performance in the Detection of Lung Nodules

RATIONALE AND OBJECTIVES: Studies that evaluate the lung-nodule-detection performance of radiologists or computerized methods depend on an initial inventory of the nodules within the thoracic images (the “truth”). The purpose of this study was to analyze (1) variability in the “truth” defined by different combinations of experienced thoracic radiologists and (2) variability in the performance of other experienced thoracic radiologists based on these definitions of “truth” in the context of lung nodule detection on computed tomography (CT) scans. MATERIALS AND METHODS: Twenty-five thoracic CT scans were reviewed by four thoracic radiologists, who independently marked lesions they considered to be nodules ≥ 3 mm in maximum diameter. Panel “truth” sets of nodules then were derived from the nodules marked by different combinations of two and three of these four radiologists. The nodule-detection performance of the other radiologists was evaluated based on these panel “truth” sets. RESULTS: The number of “true” nodules in the different panel “truth” sets ranged from 15–89 (mean: 49.8±25.6). The mean radiologist nodule-detection sensitivities across radiologists and panel “truth” sets for different panel “truth” conditions ranged from 51.0–83.2%; mean false-positive rates ranged from 0.33–1.39 per case. CONCLUSION: Substantial variability exists across radiologists in the task of lung nodule identification in CT scans. The definition of “truth” on which lung nodule detection studies are based must be carefully considered, since even experienced thoracic radiologists may not perform well when measured against the “truth” established by other experienced thoracic radiologists

Wegener's granulomatosis: an update on diagnosis and therapy

Wegener's granulomatosis (WG) is a unique clinicopathological disease characterized by necrotizing granulomatous vasculitis of the respiratory tract, pauci-immune necrotizing glomerulonephritis and small-vessel vasculitis. Owing to its wide range of clinical manifestations, WG has a broad spectrum of severity that includes the potential for alveolar hemorrhage or rapidly progressive glomerulonephritis, which are immediately life threatening. WG is associated with the presence of circulating antineutrophil cytoplasm antibodies (c-ANCAs). The most widely accepted pathogenetic model suggests that c-ANCA-activated cytokine-primed neutrophils induce microvascular damage and a rapid escalation of inflammation with recruitment of mononuclear cells. The diagnosis of WG is made on the basis of typical clinical and radiologic findings, by biopsy of involved organ, the presence of c-ANCA and exclusion of all other small-vessel vasculitis. Currently, a regimen consisting of daily cyclophosphamide and corticosteroids is considered standard therapy. A number of trials have evaluated the efficacy of less-toxic immunosuppressants and antibacterials for treating patients with WG, resulting in the identification of effective alternative regimens to induce or maintain remission in certain subpopulations of patients. Recent investigation has focused on other immunomodulatory agents (e.g., TNF-alpha inhibitors and anti-CD20 antibodies), intravenous immunoglobulins and antithymocyte globulins for treating patients with resistant WG