Automated shape analysis and visualization of the human back.

Spinal and back deformities can lead to pain and discomfort, disrupting productivity, and may require prolonged treatment. The conventional method of assessing and monitoring tile de-formity using radiographs has known radiation hazards. An alternative approach for monitoring the deformity is to base the assessment on the shape of back surface. Though three-dimensional data acquisition methods exist, techniques to extract relevant information for clinical use have not been widely developed. Thi's thesis presentsthe content and progression of research into automated analysis and visu-alization of three-dimensional laser scans of the human back. Using mathematical shape analysis, methods have been developed to compute stable curvature of the back surface and to detect the anatomic landmarks from the curvature maps. Compared with manual palpation, the landmarks have been detected to within accuracy of 1.15mm and precision of 0.8111m.Based on the detected spinous process landmarks, the back midline which is the closest surface approximation of the spine, has been derived using constrained polynomial fitting and statistical techniques. Three-dimensional geometric measurementsbasedon the midline were then corn-puted to quantify the deformity. Visualization plays a crucial role in back shape analysis since it enables the exploration of back deformities without the need for physical manipulation of the subject. In the third phase,various visualization techniques have been developed, namely, continuous and discrete colour maps, contour maps and three-dimensional views. In the last phase of the research,a software system has been developed for automating the tasks involved in analysing, visualizing and quantifying of the back shape. The novel aspectsof this research lie in the development of effective noise smoothing methods for stable curvature computation; improved shape analysis and landmark detection algorithm; effective techniques for visualizing the shape of the back; derivation of the back midline using constrained polynomials and computation of three dimensional surface measurements.

Automatic recognition of gait patterns in human motor disorders using machine learning: A review

Background: automatic recognition of human movement is an effective strategy to assess abnormal gait patterns. Machine learning approaches are mainly applied due to their ability to work with multidimensional nonlinear features. Purpose: to compare several machine learning algorithms employed for gait pattern recognition in motor disorders using discriminant features extracted from gait dynamics. Additionally, this work highlights procedures that improve gait recognition performance. Methods: we conducted an electronic literature search on Web of Science, IEEE, and Scopus, using “human recognition”, “gait patterns’’, and “feature selection methods” as relevant keywords. Results: analysis of the literature showed that kernel principal component analysis and genetic algorithms are efficient at reducing dimensional features due to their ability to process nonlinear data and converge to global optimum. Comparative analysis of machine learning performance showed that support vector machines (SVMs) exhibited higher accuracy and proper generalization for new instances. Conclusions: automatic recognition by combining dimensional data reduction, cross-validation and normalization techniques with SVMs may offer an objective and rapid tool for investigating the subject's clinical status. Future directions comprise the real-time application of these tools to drive powered assistive devices in free-living conditions.This work was supported by the FCT - Fundação para a Ciência e Tecnologia - with the reference scholarship SFRH/BD/108309/2015, and the reference project UID/EEA/04436/2013, by FEDER funds through the COMPETE 2020 - Programa Operacional Competitividade e Internacionalização (POCI) - with the reference project POCI-01-0145-FEDER-006941. Also, this work was partially supported by grant RYC-2014-16613 by Spanish Ministry of Economy and Competitiveness

Large-scale Cellular Imaging of Neuronal Activity: a Study of Neural Individuality and a Method for Imaging Mouse Cortex

The brain contains an enormous number of neurons with diverse gene expression, morphology, and connectivity. These neurons exhibit distinct activity in the course of behaviors. The study of neural coding of a specific behavior necessitates recording activity from multifarious neurons in the circuit.One appealing approach is to simultaneously image the activity of a very large neuronal population at cellular resolution. However, recording calcium signals from tens of thousands of neurons at one time is not trivial. The gold standard technique, two-photon laser-scanning microscopy, typically permits recording from hundreds of neurons. Recently, we developed objective-coupled planar illumination (OCPI) microscopy, which uses thin sheets of light to image whole volumes of ∼ 10, 000 neurons within 2 seconds. Mydissertation includes an application and a further methodological development of such a fast large-scale imaging technique: 1) Large-scale functional imaging revealsindividuality, dimorphism, and plasticity of mouse pheromone-sensing neurons.Different individuals exhibit distinctive behaviors, which is presumably attributed to the neuronal differences between brains. However, studying neural individuality, especially at the level of the function of single neurons, requires an effective approach to measure cellular activity of a diverse neuronal population in a circuit. Here using OCPI microscopy, I performed calcium imaging of pheromone-sensing neurons in the intact mouse vomeronasal organ. Exhaustive recording enabled robust detection of 17 functionally-defined neuronal types in each animal. Inter-animal differences were much larger than expected from random sampling, and different cell types showed distinct degrees of variability. One prominent difference was a neuronal type present in males and virtually absent in females, and animals exhibited a corresponding dimorphism in investigatory behavior. Surprisingly, this dimorphism was not innate but generated by plasticity, as exposure to female scents led to both the elimination of this cell type and alterations in behavior. The finding that an all-or-none dimorphism in neuronal types is controlled by experience--even in a sensory system devoted to innate responses--highlights the extraordinary role of nurture in neural individuality. 2) A new generation of OCPI microscopy enables unprecedentedlarge-scalein vivoimaging of mouse brain activity by light-sheet microscopy. I have built a new variant of OCPI microscope, horizontal scanning objective-coupled planar lumination (hsOCPI) microscope, with enhanced imaging volume and speed by ∼ 15 fold compared to OCPI, thereby capable of recording ∼ 100, 000 neurons simultaneously. Using this technique, I imaged the entire nervous system of the larval zebrafish (including the spinal cord) and a square-millimeter patch of mouse cortexex vivo. The miniaturized optics around the specimen allowed in vivo imaging through a cranial window of a head-fixed mouse. This technique is the first application of light-sheet microscopy in calcium imaging of mouse cortexin vivo. The exceptional large-scale sampling of cortical activity with cellular resolution should usher new insights into the functions of brain circuits

Book of Abstracts 15th International Symposium on Computer Methods in Biomechanics and Biomedical Engineering and 3rd Conference on Imaging and Visualization

In this edition, the two events will run together as a single conference, highlighting the strong connection with the Taylor & Francis journals: Computer Methods in Biomechanics and Biomedical Engineering (John Middleton and Christopher Jacobs, Eds.) and Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization (JoãoManuel R.S. Tavares, Ed.). The conference has become a major international meeting on computational biomechanics, imaging andvisualization. In this edition, the main program includes 212 presentations. In addition, sixteen renowned researchers will give plenary keynotes, addressing current challenges in computational biomechanics and biomedical imaging. In Lisbon, for the first time, a session dedicated to award the winner of the Best Paper in CMBBE Journal will take place. We believe that CMBBE2018 will have a strong impact on the development of computational biomechanics and biomedical imaging and visualization, identifying emerging areas of research and promoting the collaboration and networking between participants. This impact is evidenced through the well-known research groups, commercial companies and scientific organizations, who continue to support and sponsor the CMBBE meeting series. In fact, the conference is enriched with five workshops on specific scientific topics and commercial software.info:eu-repo/semantics/draf

Imaging Sensors and Applications

In past decades, various sensor technologies have been used in all areas of our lives, thus improving our quality of life. In particular, imaging sensors have been widely applied in the development of various imaging approaches such as optical imaging, ultrasound imaging, X-ray imaging, and nuclear imaging, and contributed to achieve high sensitivity, miniaturization, and real-time imaging. These advanced image sensing technologies play an important role not only in the medical field but also in the industrial field. This Special Issue covers broad topics on imaging sensors and applications. The scope range of imaging sensors can be extended to novel imaging sensors and diverse imaging systems, including hardware and software advancements. Additionally, biomedical and nondestructive sensing applications are welcome

Studies on Spinal Fusion from Computational Modelling to ‘Smart’ Implants

Low back pain, the worldwide leading cause of disability, is commonly treated with lumbar interbody fusion surgery to address degeneration, instability, deformity, and trauma of the spine. Following fusion surgery, nearly 20% experience complications requiring reoperation while 1 in 3 do not experience a meaningful improvement in pain. Implant subsidence and pseudarthrosis in particular present a multifaceted challenge in the management of a patient’s painful symptoms. Given the diversity of fusion approaches, materials, and instrumentation, further inputs are required across the treatment spectrum to prevent and manage complications. This thesis comprises biomechanical studies on lumbar spinal fusion that provide new insights into spinal fusion surgery from preoperative planning to postoperative monitoring. A computational model, using the finite element method, is developed to quantify the biomechanical impact of temporal ossification on the spine, examining how the fusion mass stiffness affects loads on the implant and subsequent subsidence risk, while bony growth into the endplates affects load-distribution among the surrounding spinal structures. The computational modelling approach is extended to provide biomechanical inputs to surgical decisions regarding posterior fixation. Where a patient is not clinically pre-disposed to subsidence or pseudarthrosis, the results suggest unilateral fixation is a more economical choice than bilateral fixation to stabilise the joint. While finite element modelling can inform pre-surgical planning, effective postoperative monitoring currently remains a clinical challenge. Periodic radiological follow-up to assess bony fusion is subjective and unreliable. This thesis describes the development of a ‘smart’ interbody cage capable of taking direct measurements from the implant for monitoring fusion progression and complication risk. Biomechanical testing of the ‘smart’ implant demonstrated its ability to distinguish between graft and endplate stiffness states. The device is prepared for wireless actualisation by investigating sensor optimisation and telemetry. The results show that near-field communication is a feasible approach for wireless power and data transfer in this setting, notwithstanding further architectural optimisation required, while a combination of strain and pressure sensors will be more mechanically and clinically informative. Further work in computational modelling of the spine and ‘smart’ implants will enable personalised healthcare for low back pain, and the results presented in this thesis are a step in this direction

Artificial Intelligence in Oral Health

This Special Issue is intended to lay the foundation of AI applications focusing on oral health, including general dentistry, periodontology, implantology, oral surgery, oral radiology, orthodontics, and prosthodontics, among others

Neurophysiological effects of ischaemia

Spinal cord injury (SCI) and paralysis remains a tragic complication of thoraco-abdominal aortic aneurysm (TAAA) surgery, despite advances in surgical and medical management. A survey of vascular anaesthetists showed availability of intra-operative spinal cord monitoring to detect an injury and subsequently guide remedial interventions, is variable across the United Kingdom and Ireland, despite clear evidence of its benefit. This research sought to explore the potential benefits of transcranial magnetic stimulation (TMS) and near infrared spectroscopy (NIRS) as alternative, more accessible monitors of ischaemic SCI. TAAA surgery has several nuances that required greater investigation if TMS was to be utilised in theatre. Firstly, the motor evoked potentials (MEPs) of peripheral vascular disease (PVD) patients were characterised. PVD is the primary pathology underlying TAAA and the MEPs of this cohort of patients showed no difference beyond that which would accountable by aging compared to healthy, younger controls. Also, it was demonstrated that over an hour of repeated single-pulse TMS, a time-frame similar to when the spinal cord is at greatest risk intra-operatively and a need for intense monitoring, the variability of the MEPs was no different to controls. A second feature of TAAA surgery is the need to render the surgical field bloodless, thus providing a clear operative space for the surgeons to work in. This is achieved using arterial clamps, the unintended consequence of which is an ischaemic nerve block (INB). An INB has been used as a research tool to initiate changes in cortical excitability. Deafferentation of distal limb structures and subsequent disinhibition of the motor cortical output to non-ischaemic muscles ipsilateral to the INB, manifested as increased MEPs. Through the use of a novel, low pressure INB applied to the lower limb, an increase in MEP amplitude in muscles proximal to the INB occurred. It was further shown that this increase in cortical excitability extended to the contralateral legs muscles and to arm muscles. Simultaneous recordings of somatosensory evoked potentials (SSEP) from stimulation of the tibial nerve, also distal to the INB, demonstrated a reduction in SSEP amplitude but not a complete deafferentation as previously assumed. Investigations into the mechanisms underlying these finding was then performed. Using quantitative sensory testing whilst an INB was performed, the loss of Aβ and Aδ indicated the deafferentation required to initiate changes in motor cortical excitability. The preservation of C-fibre function could account for the unexpected finding where participants with exaggerated punctate sensation had greater increases in MEPs and possible cortical excitability. Paired-pulse TMS paradigms explored the potential neuronal networks responsible for the increase in MEPs of the contralateral muscles. A reduction in interhemispheric inhibition was seen from the deafferented motor cortex to the intact motor cortex, whilst no change in intrahemispheric pathways was seen. The final chapter of this thesis explores the use of TMS and NIRS under surgical conditions. Despite numerous obstacles to patient recruitment, not withstanding a pandemic, a case series is presented with meaningful data which can be used to guide future study. Under the correct anaesthetic regimen, TMS induced MEPs can be recorded. The limited sample size was unable to determine if changes in cortical excitability occur in these conditions during surgery utilising a thigh INB however. In the second clinical investigation, NIRS was used to measure paraspinal muscle oxygen saturations levels (rO2), believed to correlate with intra-spinal oxygenation. This was performed alongside traditional intraoperative neuromonitoring of spinal cord with transcranial electrical stimulation (TES) MEPs. Paraspinal rO2 appeared to follow changes in the haemodynamic status of the patients, where a low rO2 would reflect a low blood pressure. One patient experienced a paraparesis, with a recoverable reduction in MEP amplitude and paraspinal rO2. Another patient who later died without clinical confirmation of paralysis, had a precipitous and permanent reduction in both MEPs and rO2, likely reflecting a SCI. A third patient where a decrease in MEPs and paraspinal rO2 was seen had remedial interventions initiated to prevent a possible SCI, which resulted in a return of both measures close to baseline. Future work should look to explore the changes in cortical excitability secondary to iatrogenic limb ischaemic during TAAA surgery and how this impacts TMS-induced MEP characteristics and their interpretation in detecting a SCI. It should also explore their use alongside NIRS to detect both intra-operative and post-operative SCI and to guide their management.Open Acces