ST-V-Net: incorporating shape prior into convolutional neural networks for proximal femur segmentation

We aim to develop a deep-learning-based method for automatic proximal femur segmentation in quantitative computed tomography (QCT) images. We proposed a spatial transformation V-Net (ST-V-Net), which contains a V-Net and a spatial transform network (STN) to extract the proximal femur from QCT images. The STN incorporates a shape prior into the segmentation network as a constraint and guidance for model training, which improves model performance and accelerates model convergence. Meanwhile, a multi-stage training strategy is adopted to fine-tune the weights of the ST-V-Net. We performed experiments using a QCT dataset which included 397 QCT subjects. During the experiments for the entire cohort and then for male and female subjects separately, 90% of the subjects were used in ten-fold stratified cross-validation for training and the rest of the subjects were used to evaluate the performance of models. In the entire cohort, the proposed model achieved a Dice similarity coefficient (DSC) of 0.9888, a sensitivity of 0.9966 and a specificity of 0.9988. Compared with V-Net, the Hausdorff distance was reduced from 9.144 to 5.917 mm, and the average surface distance was reduced from 0.012 to 0.009 mm using the proposed ST-V-Net. Quantitative evaluation demonstrated excellent performance of the proposed ST-V-Net for automatic proximal femur segmentation in QCT images. In addition, the proposed ST-V-Net sheds light on incorporating shape prior to segmentation to further improve the model performance

Presence of Reactive Microglia and Neuroinflammatory Mediators in a Case of Frontotemporal Dementia with P301S Mutation

Background: Recent findings, showing the presence of an inflammatory process in the brain of transgenic mice expressing P301S mutated human tau protein, indicate that neuroinflammation may contribute to tau-related degeneration in frontotemporal dementia and parkinsonism linked to chromosome 17 with tau mutations (FTDP-17T). Objective: To investigate the occurrence of neuroinflammatory changes in the brain of a patient affected by FTDP-17T associated with the P301S mutation and showing a frontotemporal dementia phenotype as well as in the brain of a patient affected by another FTDP-17T phenotype: multiple system tauopathy with presenile dementia. Methods: We used immunohistochemical methods to visualize activated microglia, interleukin-1 beta (IL-1 beta)-, cyclooxygenase-2 (COX-2)-expressing cells. Results: In the brain of the patient with the P301S mutation, a strong neuroinflammatory reaction was present. Activated microglia/infiltrating macrophages expressing the cluster of differentiation 68 and major histo-campatibility complex class II cell surface receptors, encoded by the human leukocyte antigen DP-DQ-DR, were detected in the cortex and hippocampus. IL-1 beta and COX-2 expression were induced in neuronal and glial cells. These neuroinflammatory changes were different from those observed in the brain of the patient bearing the +3 mutation, where macrophage infiltration was absent, microglial cells displayed an earlier stage of activation and COX-2 was not detected. Conclusions: Our findings suggest that microglial activation and the production of proinflammatory mediators by phospho-tau-positive neurons and glial cells may differentially contribute to neuronal death and disease progression in neurodegenerative tauopathies