Grey matter volume correlates with virtual water maze task performance in boys with androgen excess

Major questions remain about the specific role of testosterone in human spatial navigation. We tested 10 boys (mean age 11.65 years) with an extremely rare disorder of androgen excess (Familial Male Precocious Puberty, FMPP) and 40 healthy boys (mean age 12.81 years) on a virtual version of the Morris Water Maze task. In addition, anatomical magnetic resonance images were collected for all patients and a subsample of the controls (n=21) after task completion. Behaviourally, no significant differences were found between both groups. However, in the MRI analyses, grey matter volume (GMV) was correlated with performance using voxel-based morphometry (VBM). Group differences in correlations of performance with GMV were apparent in medial regions of the prefrontal cortex as well as the middle occipital gyrus and the cuneus. By comparison, similar correlations for both groups were found in the inferior parietal lobule. These data provide novel insight into the relation between testosterone and brain development and suggest that morphological differences in a spatial navigation network covary with performance in spatial ability. Published by Elsevier Ltd on behalf of IBRO

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Semi-automated stereoradiographic upper limb 3D reconstructions using a combined parametric and statistical model: a preliminary study

PURPOSE: Quantitative assessment of 3D clinical indices may be crucial for elbow surgery planning. 3D parametric modeling from bi-planar radiographs was successfully proposed for spine and lower limb clinical investigation as an alternative for CT-scan. The aim of this study was to adapt this method to the upper limb with a preliminary validation. METHODS: CT-scan 3D models of humerus, radius and ulna were obtained from 20 cadaveric upper limbs and yielded parametric models made of geometric primitives. Primitives were defined by descriptor parameters (diameters, angles...) and correlations between these descriptors were found. Using these correlations, a semi-automated reconstruction method of humerus using bi-planar radiographs was achieved: a 3D personalized parametric model was built, from which clinical parameters were computed [orientation and projections on bone surface of trochlea sulcus to capitulum (CTS) axis, trochlea sulcus anterior offset and width of distal humeral epiphysis]. This method was evaluated by accuracy compared to CT-scan and reproducibility. RESULTS: Points-to-surface mean distance was 0.9 mm (2 RMS = 2.5 mm). For clinical parameters, mean differences were 0.4-1.9 mm and from 1.7° to 2.3°. All parameters except from angle formed by CTS axis and bi-epicondylar axis in transverse plane were reproducible. Reconstruction time was about 5 min. CONCLUSIONS: The presented method provides access to morphological upper limb parameters with very low level of radiation. Preliminary in vitro validation for humerus showed that it is fast and accurate enough to be used in clinical daily practice as an alternative to CT-scan for total elbow arthroplasty pre operative evaluation

Assessing knee OA severity with CNN attention-based end-to-end architectures

This work proposes a novel end-to-end convolutional neural network (CNN) architecture to automatically quantify the severity of knee osteoarthritis (OA) using X-Ray images, which incorporates trainable attention modules acting as unsupervised fine-grained detectors of the region of interest (ROI). The proposed attention modules can be applied at different levels and scales across any CNN pipeline helping the network to learn relevant attention patterns over the most informative parts of the image at different resolutions. We test the proposed attention mechanism on existing state-of-the-art CNN architectures as our base models, achieving promising results on the benchmark knee OA datasets from the osteoarthritis initiative (OAI) and multicenter osteoarthritis study (MOST).Postprint (published version