Adrenal failure followed by status epilepticus and hemolytic anemia in primary antiphospholipid syndrome

We report on a 14 year old boy who presented with the symptoms abdominal pain, fever and proteinuria. A hematoma in the region of the right pararenal space was diagnosed. Prothrombin time and activated partial thromboplastin time were prolonged, lupus anticoagulant and anticardiolipin antibodies were positive and serum cortisol was normal. Ten days after admission the boy suddenly suffered generalized seizures due to low serum sodium. As well, the patient developed hemolytic anemia, acute elevated liver enzymes, hematuria and increased proteinuria. At this time a second hemorrhage of the left adrenal gland was documented. Adrenal function tests revealed adrenal insufficiency. We suspected microthromboses in the adrenals and secondary bleeding and treated the boy with hydrocortisone, fludrocortisone and phenprocoumon. CONCLUSION: Adrenal failure is a rare complication of APS in children with only five cases reported to date. As shown in our patient, this syndrome can manifest in a diverse set of simultaneously occurring symptoms

CNN-based Lung CT Registration with Multiple Anatomical Constraints

Deep-learning-based registration methods emerged as a fast alternative to conventional registration methods. However, these methods often still cannot achieve the same performance as conventional registration methods because they are either limited to small deformation or they fail to handle a superposition of large and small deformations without producing implausible deformation fields with foldings inside. In this paper, we identify important strategies of conventional registration methods for lung registration and successfully developed the deep-learning counterpart. We employ a Gaussian-pyramid-based multilevel framework that can solve the image registration optimization in a coarse-to-fine fashion. Furthermore, we prevent foldings of the deformation field and restrict the determinant of the Jacobian to physiologically meaningful values by combining a volume change penalty with a curvature regularizer in the loss function. Keypoint correspondences are integrated to focus on the alignment of smaller structures. We perform an extensive evaluation to assess the accuracy, the robustness, the plausibility of the estimated deformation fields, and the transferability of our registration approach. We show that it achieves state-of-the-art results on the COPDGene dataset compared to conventional registration method with much shorter execution time. In our experiments on the DIRLab exhale to inhale lung registration, we demonstrate substantial improvements (TRE below $1.2$ mm) over other deep learning methods. Our algorithm is publicly available at https://grand-challenge.org/algorithms/deep-learning-based-ct-lung-registration/

Accuracy of registration algorithms in subtraction CT of the lungs: A digital phantom study

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Deep learning-based segmentation of brain parenchyma and ventricular system in CT scans in the presence of anomalies

IntroductionThe automatic segmentation of brain parenchyma and cerebrospinal fluid-filled spaces such as the ventricular system is the first step for quantitative and qualitative analysis of brain CT data. For clinical practice and especially for diagnostics, it is crucial that such a method is robust to anatomical variability and pathological changes such as (hemorrhagic or neoplastic) lesions and chronic defects. This study investigates the increase in overall robustness of a deep learning algorithm that is gained by adding hemorrhage training data to an otherwise normal training cohort.MethodsA 2D U-Net is trained on subjects with normal appearing brain anatomy. In a second experiment the training data includes additional subjects with brain hemorrhage on image data of the RSNA Brain CT Hemorrhage Challenge with custom reference segmentations. The resulting networks are evaluated on normal and hemorrhage test casesseparately, and on an independent test set of patients with brain tumors of the publicly available GLIS-RT dataset.ResultsAdding data with hemorrhage to the training set significantly improves the segmentation performance over an algorithm trained exclusively on normally appearing data, not only in the hemorrhage test set but also in the tumor test set. The performance on normally appearing data is stable. Overall, the improved algorithm achieves median Dice scores of 0.98 (parenchyma), 0.91 (left ventricle), 0.90 (right ventricle), 0.81 (third ventricle), and 0.80 (fourth ventricle) on the hemorrhage test set. On the tumor test set, the median Dice scores are 0.96 (parenchyma), 0.90 (left ventricle), 0.90 (right ventricle), 0.75 (third ventricle), and 0.73 (fourth ventricle).ConclusionTraining on an extended data set that includes pathologies is crucial and significantly increases the overall robustness of a segmentation algorithm for brain parenchyma and ventricular system in CT data, also for anomalies completely unseen during training. Extension of the training set to include other diseases may further improve the generalizability of the algorithm

A robust similarity measure for volumetric image registration with outliers

Image registration under challenging realistic conditions is a very important area of research. In this paper, we focus on algorithms that seek to densely align two volumetric images according to a global similarity measure. Despite intensive research in this area, there is still a need for similarity measures that are robust to outliers common to many different types of images. For example, medical image data is often corrupted by intensity inhomogeneities and may contain outliers in the form of pathologies. In this paper we propose a global similarity measure that is robust to both intensity inhomogeneities and outliers without requiring prior knowledge of the type of outliers. We combine the normalised gradients of images with the cosine function and show that it is theoretically robust against a very general class of outliers. Experimentally, we verify the robustness of our measures within two distinct algorithms. Firstly, we embed our similarity measures within a proof-of-concept extension of the Lucas–Kanade algorithm for volumetric data. Finally, we embed our measures within a popular non-rigid alignment framework based on free-form deformations and show it to be robust against both simulated tumours and intensity inhomogeneities

Cytokines as Therapeutic Targets in Rheumatoid Arthritis and Other Inflammatory Diseases

The human immune system involves highly complex and coordinated processes in which small proteins named cytokines play a key role. Cytokines have been implicated in the pathogenesis of a number of inflammatory and autoimmune diseases. Cytokines are therefore attractive therapeutic targets in these conditions. Anticytokine therapy for inflammatory diseases became a clinical reality with the introduction of tumor necrosis factor (TNF) inhibitors for the treatment of severe rheumatoid arthritis. Although these therapies have transformed the treatment of patients with severe inflammatory arthritis, there remain significant limiting factors: treatment failure is commonly seen in the clinic; safety concerns remain; there is uncertainty regarding the relevance of immunogenicity; the absence of biomarkers to direct therapy decisions and high drug costs limit availability in some healthcare systems. In this article, we provide an overview of the key efficacy and safety trials for currently approved treatments in rheumatoid arthritis and review the major lessons learned from a decade of use in clinical practice, focusing mainly on anti-TNF and anti–interleukin (IL)-6 agents. We also describe the clinical application of anticytokine therapies for other inflammatory diseases, particularly within the spondyloarthritis spectrum, and highlight differential responses across diseases. Finally, we report on the current state of trials for newer therapeutic targets, focusing mainly on the IL-17 and IL-23 pathways