Automated prostate multi-regional segmentation in magnetic resonance using fully convolutional neural networks.

Objective: Automatic MR imaging segmentation of the prostate provides relevant clinical benefits for prostate cancer evaluation such as calculation of automated PSA density and other critical imaging biomarkers. Further, automated T2-weighted image segmentation of central-transition zone (CZ-TZ), peripheral zone (PZ), and seminal vesicle (SV) can help to evaluate clinically significant cancer following the PI-RADS v2.1 guidelines.

The use of μCT and fractal dimension for fracture prediction in osteoporotic individuals.

Osteoporosis (OP) is a widespread condition with commonly associated fracture sites at the hip, vertebra and wrist. This study examines the effects of age and osteoporosis on bone quality by comparing the efficacy of using parameters which indicate bone quality (both traditional clinical parameters such as bone mineral density (BMD), as well as apparent Young's modulus determined by finite element analysis, among others) to predict fracture. Non-fracture samples were collected from the femoral heads of 83 donors (44 males, 39 females), and fracture samples were obtained from the femoral heads of 17 donors (female).

Automated vertebrae localization and identification by decision forests and image-based refinement on real-world CT data.

Purpose: Development of a fully automatic algorithm for the automatic localization and identification of vertebral bodies in computed tomography (CT).

Materials And Methods: This algorithm was developed using a dataset based on real-world data of 232 thoraco-abdominopelvic CT scans retrospectively collected. In order to achieve an accurate solution, a two-stage automated method was developed: decision forests for a rough prediction of vertebral bodies position, and morphological image processing techniques to refine the previous detection by locating the position of the spinal canal.

Parametric finite element model of medial patellofemoral ligament reconstruction model development and clinical validation.

Background: Currently, there is uncertainty regarding the long-term outcome of medial patellofemoral ligament reconstructions (MPFLr). Our objectives were: (1) to develop a parametric model of the patellofemoral joint (PFJ) enabling us to simulate different surgical techniques for MPFLr; (2) to determine the negative effects on the PFJ associated with each technique, which could be related to long-term deterioration of the PFJ.

Methods: A finite element model of the PFJ was created based on CT data from 24 knees with chronic lateral patellar instability.

Allopurinol partially prevents disuse muscle atrophy in mice and humans.

Disuse muscle wasting will likely affect everyone in his or her lifetime in response to pathologies such as joint immobilization, inactivity or bed rest. There are no good therapies to treat it. We previously found that allopurinol, a drug widely used to treat gout, protects muscle damage after exhaustive exercise and results in functional gains in old individuals.

Development of imaging biomarkers and generation of big data.

Several image processing algorithms have emerged to cover unmet clinical needs but their application to radiological routine with a clear clinical impact is still not straightforward. Moving from local to big infrastructures, such as Medical Imaging Biobanks (millions of studies), or even more, Federations of Medical Imaging Biobanks (in some cases totaling to hundreds of millions of studies) require the integration of automated pipelines for fast analysis of pooled data to extract clinically relevant conclusions, not uniquely linked to medical imaging, but in combination to other information such as genetic profiling. A general strategy for the development of imaging biomarkers and their integration in the cloud for the quantitative management and exploitation in large databases is herein presented.