Analyzing fibrous tissue pattern in fibrous dysplasia bone images using deep R-CNN networks for segmentation

Predictive health monitoring systems help to detect human health threats in the early stage. Evolving deep learning techniques in medical image analysis results in efficient feedback in quick time. Fibrous dysplasia (FD) is a genetic disorder, triggered by the mutation in Guanine Nucleotide binding protein with alpha stimulatory activities in the human bone genesis. It slowly occupies the bone marrow and converts the bone cell into fibrous tissues. It weakens the bone structure and leads to permanent disability. This paper proposes the study of FD bone image analyzing techniques with deep networks. Also, the linear regression model is annotated for predicting the bone abnormality levels with observed coefficients. Modern image processing begins with various image filters. It describes the edges, shades, texture values of the receptive field. Different types of segmentation and edge detection mechanisms are applied to locate the tumor, lesion, and fibrous tissues in the bone image. Extract the fibrous region in the bone image using the region-based convolutional neural network algorithm. The segmented results are compared with their accuracy metrics. The segmentation loss is reduced by each iteration. The overall loss is 0.24% and the accuracy is 99%, segmenting the masked region produces 98% of accuracy, and building the bounding boxes is 99% of accuracy

Pinning down loosened prostheses : imaging and planning of percutaneous hip refixation

This thesis examines how computer software can be used to analyse medical images of an aseptically loosening hip prosthesis, and subsequently to plan and guide a minimally invasive cement injection procedure to stabilize the prosthesis. We addressed the detection and measurement of periprosthetic bone lesions from CT image volumes. Post-operative CTs of patients treated at our institution were analysed. We developed tissue classification algorithms that automatically label periprosthetic bone, cement and fibrous interface tissue. An existing particle-based multi-material meshing algorithm was adapted for improved Finite Element model creation. We then presented HipRFX, a proof-of-concept software tool for planning and guidance during percutaneous cement refixation procedures.Advanced School for Computing and Imaging (ASCI), Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO), Stichting Anna Fonds, Technologiestichting STWUBL - phd migration 201

Automatic segmentation of the human thigh muscles in magnetic resonance imaging

Advances in magnetic resonance imaging (MRI) and analysis techniques have improved diagnosis and patient treatment pathways. Typically, image analysis requires substantial technical and medical expertise and MR images can su↵er from artefacts, echo and intensity inhomogeneity due to gradient pulse eddy currents and inherent e↵ects of pulse radiation on MRI radio frequency (RF) coils that complicates the analysis. Processing and analysing serial sections of MRI scans to measure tissue volume is an additional challenge as the shapes and the borders between neighbouring tissues change significantly by anatomical location. Medical imaging solutions are needed to avoid laborious manual segmentation of specified regions of interest (ROI) and operator errors. The work set out in this thesis has addressed this challenge with a specific focus on skeletal muscle segmentation of the thigh. The aim was to develop an MRI segmentation framework for the quadriceps muscles, femur and bone marrow. Four contributions of this research include: (1) the development of a semi-automatic segmentation framework for a single transverse-plane image; (2) automatic segmentation of a single transverseplane image; (3) the automatic segmentation of multiple contiguous transverse-plane images from a full MRI thigh scan; and (4) the use of deep learning for MRI thigh quadriceps segmentation. Novel image processing, statistical analysis and machine learning algorithms were developed for all solutions and they were compared against current gold-standard manual segmentation. Frameworks (1) and (3) require minimal input from the user to delineate the muscle border. Overall, the frameworks in (1), (2) and (3) o↵er very good output performance, with respective framework’s mean segmentation accuracy by JSI and processing time of: (1) 0.95 and 17 sec; (2) 0.85 and 22 sec; and (3) 0.93 and 3 sec. For the framework in (4), the ImageNet trained model was customized by replacing the fully-connected layers in its architecture to convolutional layers (hence the name of Fully Convolutional Network (FCN)) and the pre-trained model was transferred for the ROI segmentation task. With the implementation of post-processing for image filtering and morphology to the segmented ROI, we have successfully accomplished a new benchmark for thigh MRI analysis. The mean accuracy and processing time with this framework are 0.9502 (by JSI ) and 0.117 sec per image, respectively

Injury and Skeletal Biomechanics

This book covers many aspects of Injury and Skeletal Biomechanics. As the title represents, the aspects of force, motion, kinetics, kinematics, deformation, stress and strain are examined in a range of topics such as human muscles and skeleton, gait, injury and risk assessment under given situations. Topics range from image processing to articular cartilage biomechanical behavior, gait behavior under different scenarios, and training, to musculoskeletal and injury biomechanics modeling and risk assessment to motion preservation. This book, together with "Human Musculoskeletal Biomechanics", is available for free download to students and instructors who may find it suitable to develop new graduate level courses and undergraduate teaching in biomechanics

Advanced Magnetic Resonance Imaging of Osteoarthritis

This thesis examines the potential utility of magnetic resonance (MR) quantitative imaging biomarkers (QIBs) of knee osteoarthritis (OA) for rapid assessment of treatment efficacy in experimental medicine studies. The development of treatments able to modify disease in OA is hampered by an inability to evaluate treatment response over a timeframe relevant to clinical trials. There are particular challenges in the experimental medicine setting due to the small numbers of participants and short follow-up duration relative to the expected time course of OA development and progression. Multiple MR QIBs of OA exist which may help address the problem of early evaluation of treatment response. However, their use in early phase studies has remained limited. Possible reasons for this include incomplete characterisation of the performance of QIBs in this setting and lack of head-to-head comparison of candidate QIBs to determine which would be optimal. This thesis aims to address these shortcomings and provide new information on the likely utility of MR QIBs in the setting of experimental medicine studies, as well as their potential for improving our general understanding of OA pathophysiology. I start by examining the reliability and ability to discriminate between OA and healthy knees of cartilage compositional MR imaging in a systematic review and meta-analysis. I then describe the development and validation of a novel semi-automatic surface-based method for analysing articular cartilage composition and morphology at the knee which may offer improved responsiveness and spatial localisation of change. Moving to QIBs of subchondral bone, I evaluate the association between measures of subchondral bone architecture derived from MR texture analysis and OA progression in the Osteoarthritis Initiative. The remainder of the thesis describes a prospective observational study where the utility of MR QIBs of synovium, subchondral bone and cartilage in experimental medicine studies is assessed. In summary, this thesis will inform decisions regarding the use of MR-based QIBs in future longitudinal and interventional studies. Their inclusion in experimental medicine studies may allow early assessment of treatment efficacy at a structural level and improve efficiency of treatment development pipelines.Funding: Addenbrooke's Charitable Trust, Experimental Medicine Initiative, non-investigator sponsored study grant from GlaxoSmithKlin

UWOMJ Volume 67, Number 2, Summer 1998

Schulich School of Medicine & Dentistryhttps://ir.lib.uwo.ca/uwomj/1244/thumbnail.jp