An integrated approach for reconstructing a surface model of the proximal femur from sparse input data and a multi-resolution point distribution model: an in vitro study

Background: Accurate reconstruction of a patient-specific surface model of the proximal femur from preoperatively or intraoperatively available sparse data plays an important role in planning and supporting various computer-assisted surgical procedures. Methods: In this paper, we present an integrated approach using a multi-resolution point distribution model (MR-PDM) to reconstruct a patient-specific surface model of the proximal femur from sparse input data, which may consist of sparse point data or a limited number of calibrated X-ray images. Depending on the modality of the input data, our approach chooses different PDMs. When 3D sparse points are used, which may be obtained intraoperatively via a pointer-based digitization or from a calibrated ultrasound, a fine level point distribution model (FL-PDM) is used in the reconstruction process. In contrast, when calibrated X-ray images are used, which may be obtained preoperatively or intraoperatively, a coarse level point distribution model (CL-PDM) will be used. Results: The present approach was verified on 31 femurs. Three different types of input data, i.e., sparse points, calibrated fluoroscopic images, and calibrated X-ray radiographs, were used in our experiments to reconstruct a surface model of the associated bone. Our experimental results demonstrate promising accuracy of the present approach. Conclusions: A multi-resolution point distribution model facilitate the reconstruction of a patient-specific surface model of the proximal femur from sparse input dat

Synthetic content generation for auto-stereoscopic displays

Due to the appearance of auto-stereoscopic visualization as one of the most emerging tendencies used in displays, new content generation techniques for this kind of visualization are required. In this paper we present a study for the generation of multi-view synthetic content, studying several camera setups (planar, cylindrical and hyperbolic) and their configurations. We discuss the different effects obtained varying the parameters of these setups. A study with several users was made to analyze visual perceptions, asking them for their optimal visualization. To create the virtual content, a multi-view system has been integrated in a powerful game engine, which allows us to use the latest graphics hardware advances. This integration is detailed and several demos and videos are attached with this paper, which represent a virtual world for auto-stereoscopic displays and the same scenario in a two-view anaglyph representation for being visualized in any conventional display. In all these demos, the parameters studied can be modified offering the possibility of easily appreciate their effects in a virtual scene

Muscular tension significantly affects stability in standing posture

Muscular co-contraction is a strategy commonly used by elders with the aim to increase stability. However, co-contraction leads to stiffness which in turns reduces stability. Some literature seems to suggest an opposite approach and to point out relaxation as a way to improve stability. Teaching relaxation is therefore becoming the aim of many studies letting unclear whether tension or relaxation are the most effective muscular strategy to improve stability. Relaxation is a misleading concept in our society. It is often confused with rest, while it should be addressed during stressing tasks, where it should aim to reduce energetic costs and increase stability. The inability to relax can be related to sub-optimal neuro-motor control, which can lead to increased stresses. Research question The objective of the study is to investigate the effect of voluntary muscle contraction and relaxation over the stability of human standing posture, answering two specific research questions: (1) Does the muscular tension have an impact on stability of standing posture? (2) Could this impact be estimated by using a minimally invasive procedure? Methods By using a force plate, we analysed the displacement of the center of pressure of 30 volunteers during state of tension and relaxation in comparison with a control state, and with open and closed eyes. Results We found that tension significantly reduced the stability of subjects (15 out of 16 parameters, p¿<¿0.003). Significance Our results show that daily situations of stress can lead to decreased stability. Such a loss might actually increase the risk of chronic joint overload or fall. Finally, breathing has direct effect over the management of pain and stress, and the results reported here point out the need to explicitly explore the troubling fact that a large portion of population might not be able to properly breath.Peer ReviewedPostprint (published version

Switching operation modes in the neocortex via cholinergic neuromodulation

In order to deal with the uncertainty in the world, our brains need to be able to flexibly switch between the exploration of new sensory representations and exploitation of previously acquired ones. This requires forming accurate estimations of what and how much something is expected. While modeling has allowed for the development of several ways to form predictions, how the brain could implement those is still under debate. Here, we recognize acetylcholine as one of the main neuromodulators driving learning based on uncertainty, promoting the exploration of new sensory representations. We identify its interactions with cortical inhibitory interneurons and derive a biophysically grounded computational model able to capture and learn from uncertainty. This model allows us to understand inhibition beyond gain control by suggesting that different interneuron subtypes either encode predictions or estimate their uncertainty, facilitating detection of unexpected cues. Moreover, we show how acetylcholine-like neuromodulation uniquely interacts with global and local sources of inhibition, disrupting perceptual certainty and promoting the rapid acquisition of new perceptual cues. Altogether, our model proposes that cortical acetylcholine favors sensory exploration over exploitation in a cortical microcircuit dedicated to estimating sensory uncertainty

Medical-based Deep Curriculum Learning for Improved Fracture Classification

International audienceAbstract. Current deep-learning-based methods do not easily integrate into clinical protocols, neither take full advantage of medical knowledge.In this work, we propose and compare several strategies relying on curriculum learning, to support the classification of proximal femur fracturefrom X-ray images, a challenging problem as reflected by existing intra- and inter-expert disagreement. Our strategies are derived from knowledgesuch as medical decision trees and inconsistencies in the annotations of multiple experts, which allows us to assign a degree of diculty to eachtraining sample. We demonstrate that if we start learning \easy" examples and move towards \hard", the model can reach better performance,even with fewer data. The evaluation is performed on the classification of a clinical dataset of about 1000 X-ray images. Our results show that,compared to class-uniform and random strategies, the proposed medical knowledge-based curriculum, performs up to 15% better in terms ofaccuracy, achieving the performance of experienced trauma surgeons. Keywords: Curriculum learning, multi-label classification, bone fractures, computer-aided diagnosis, medical decision tre