Simulation of fatigue-initiated subacromial impingement: clarifying mechanisms

AbstractSubacromial impingement in the shoulder precedes many cases of rotator cuff pathology. However, debate exists regarding the mechanism, and even existence, of fatigue-initiated impingement. The controversy centers on two primary impingement mechanisms: 1) superior humeral head migration and 2) scapular reorientation. A linked series of in vivo experiments and in silica simulations accomplishes the integration of stochastic, orthopedic, geometric, kinematic, physiologic, literature-derived, and experimental data sources to help resolve the mechanism debate. A major focus is the multi-scale modeling of relevant variability. The described techniques have direct implications for musculoskeletal modeling and simulation of the shoulder region, with specific application to assessing occupational and activities of daily living in diverse populations

EVALUATING DIFFERENTIAL NUCLEAR DNA YIELD RATES AMONG HUMAN BONE TISSUE TYPES: A SYNCHROTRON MICRO-CT APPROACH

Molecular human identification has conventionally focused on DNA sampling from dense, weight-bearing cortical bone tissue from femora or tibiae. A comparison of skeletal elements from three contemporary individuals demonstrated that elements with high quantities of cancellous bone yielded nuclear DNA at the highest rates, suggesting that preferentially sampling cortical bone is suboptimal (Mundorff & Davoren, 2014). Despite these findings, the reason for the differential DNA yields between cortical and cancellous bone tissues remains unknown. The primary goal of this research is to ascertain whether differences in bone microstructure can be used to explain differential nuclear DNA yield among bone tissue types, with a focus on osteocytes and the 3D quantification of their associated lacunae. Osteocytes and other bone cells are recognized to house DNA in bone tissue, thus examining the density of their lacunae may explain why nuclear DNA yield rates differ among bone tissue types. Methods included: (1) quantifying cortical and cancellous bone volume from each bone-sampling site using Computed Tomography (CT), and (2) visualizing and quantifying osteocyte lacunae using synchrotron radiation micro-Computed Tomographic imaging (SR micro-CT). Regions of interest (ROIs) from cortical and cancellous bone tissues (n=129) were comparatively analyzed from the three skeletons sampled for Mundorff and Davoren’s (2014) study. Analyses tested the primary hypothesis that the abundance and density of bone’s cellular spaces vary between cortical and cancellous bone tissue types. Results demonstrated that osteocyte lacunar abundance and density vary between cortical and cancellous bone tissue types, with cortical bone ROIs containing a higher lacunar abundance and density. The osteocyte lacunar density values are independent of nuclear DNA yield, suggesting an alternative explanation for the higher nuclear DNA yields from predominantly cancellous bones. It is hypothesized that soft tissue remnants within the medullary cavities of primarily cancellous skeletal elements are driving the high nuclear DNA yields. These findings have significant implications for bone-sample selection for nuclear DNA analysis in a forensic context. The procurement of small, primarily cancellous bones with associated soft tissues should be preferentially sampled, and no longer dismissed as potential DNA sources in favor of cortical bone tissue

Three Dimensional Nonlinear Statistical Modeling Framework for Morphological Analysis

This dissertation describes a novel three-dimensional (3D) morphometric analysis framework for building statistical shape models and identifying shape differences between populations. This research generalizes the use of anatomical atlases on more complex anatomy as in case of irregular, flat bones, and bones with deformity and irregular bone growth. The foundations for this framework are: 1) Anatomical atlases which allow the creation of homologues anatomical models across populations; 2) Statistical representation for output models in a compact form to capture both local and global shape variation across populations; 3) Shape Analysis using automated 3D landmarking and surface matching. The proposed framework has various applications in clinical, forensic and physical anthropology fields. Extensive research has been published in peer-reviewed image processing, forensic anthropology, physical anthropology, biomedical engineering, and clinical orthopedics conferences and journals. The forthcoming discussion of existing methods for morphometric analysis, including manual and semi-automatic methods, addresses the need for automation of morphometric analysis and statistical atlases. Explanations of these existing methods for the construction of statistical shape models, including benefits and limitations of each method, provide evidence of the necessity for such a novel algorithm. A novel approach was taken to achieve accurate point correspondence in case of irregular and deformed anatomy. This was achieved using a scale space approach to detect prominent scale invariant features. These features were then matched and registered using a novel multi-scale method, utilizing both coordinate data as well as shape descriptors, followed by an overall surface deformation using a new constrained free-form deformation. Applications of output statistical atlases are discussed, including forensic applications for the skull sexing, as well as physical anthropology applications, such as asymmetry in clavicles. Clinical applications in pelvis reconstruction and studying of lumbar kinematics and studying thickness of bone and soft tissue are also discussed

Visualisation of articular motion in orthopaedics

Shouder replacement surgery is difficult surgery, with a relatively large risk on limited post-operative range of motion for patients. Adaptations to the anatomy of joints by placing a prosthesis affects the articulation of the joint. In this thesis we present a software system that simulates and visualises these effects. By loading a CT-scan of the shoulder of a patient we can simulate the range of motion of the joint and visualize limitations as a result of rigid structures of the joint. Surgeons may set up an operation plan and see what the consequences of the operation will be for the range of motion of the patient. The thesis investigates aspects that are relevant for the system. We describe an algorithm to convert the scan data to bone models. In addition, a validation experiment is presented. A method for motion registration and visualisation of recorded kinematic data is presented. Finally, this thesis concerns the application of the system to different surgical problems, such as hip arthroplasty and shoulder fractures.Annafonds Biomet Nederland Clinical Graphics DePuy JTE Johnson & Johnson Dutch Arthritis Association Litos/ Motek Medical TornierUBL - phd migration 201

Neural and Collagen Tissue Properties As They Relate to Glenohumeral Joint Laxity and Stiffness

Shoulder instability is the most frequently occurring shoulder injury. Instability is caused by repetitive stress or a traumatic event that leads to excessive joint laxity. A major concern with instability is the high recurrence rate post treatment. Although the occurrence and recurrence of instability are primarily associated with capsule and labrum injury, the tissue characteristics of the capsule and labrum are not well understood. Therefore, we sought to explore the tissue properties of the capsule and labrum. We also explored the relationship between the tissue properties and passive joint motion. To carry out this body of work, we dissected five fresh and three frozen human cadaveric shoulder pairs, removing the muscle and subcutaneous tissue while leaving the capsule and labrum intact. The cadaveric specimens included four males and four females of ages 23, 55, 58, 62, 76, 81 (x2), and 98. We then assessed glenohumeral joint laxity and joint stiffness using a materials testing machine. Following this assessment, we further dissected the shoulder removing the capsule and labrum from the anteroinferior (most common region of injury) and posteroinferior (least common region of injury) areas of the glenohumeral joint. The majority of the tissue was used for studying mechanoreceptors. A small portion was stored and used later for determining collagen content. Mechanoreceptor distribution and neural count were determined by first staining the tissue using our improved gold chloride staining method and light microscopy. Collagen content was determined using an acid-pepsin digestion for extracting collagen and the Sircol Collagen Dye assay for quantifying collagen. Joint laxity and joint stiffness assessment revealed a significant relationship (r = -.824, P<.001) between joint laxity and joint stiffness where joint stiffness decreased as joint laxity increased. From the neural staining, we created neuroanatomical maps for the capsule and labrum based on mechanoreceptor distribution. Not only did we discover a mechanoreceptor distribution pattern, but we also observed a strong positive relationship between neural count and joint laxity specifically in the capsule (r = .710, p = .003). Though this relationship was not significant in the labrum, when taking the capsule and labrum neural count into consideration, the overall relationship was significant (r =.646, p =.009). Unlike neural count and joint laxity, we did not observe a significant relationship between collagen content and joint stiffness in the capsule or labrum except when injury was taken into consideration. In the presence of injury, we observed strong negative relationships in the capsule (r = -.803, p =.016), labrum (r = -.755, p = .030), and overall, when including the capsule and labrum (r = -.814, p = .014). Based on our results, we were able to identify potential contributing factors to the unstable shoulder that may help guide future treatment thereby reducing the recurrence rate. We identified mechanoreceptor scarce regions from the neuroanatomical map that may be more apt for surgical repair that allows for mechanoreceptor preservation. We also observed a relationship between neural count and joint laxity that further supports the need to preserve neural structure integrity during treatment. The relationship between collagen content and joint stiffness, in the presence of injury, suggests that changes in collagen content should be considered during rehabilitative treatment when joint stiffness is compromised. Joint laxity, joint stiffness, mechanoreceptor distribution, neural count, and collagen content are all factors that should be studied in greater detail when developing treatments for the unstable shoulder

Fabric Image Representation Encoding Networks for Large-scale 3D Medical Image Analysis

Deep neural networks are parameterised by weights that encode feature representations, whose performance is dictated through generalisation by using large-scale feature-rich datasets. The lack of large-scale labelled 3D medical imaging datasets restrict constructing such generalised networks. In this work, a novel 3D segmentation network, Fabric Image Representation Networks (FIRENet), is proposed to extract and encode generalisable feature representations from multiple medical image datasets in a large-scale manner. FIRENet learns image specific feature representations by way of 3D fabric network architecture that contains exponential number of sub-architectures to handle various protocols and coverage of anatomical regions and structures. The fabric network uses Atrous Spatial Pyramid Pooling (ASPP) extended to 3D to extract local and image-level features at a fine selection of scales. The fabric is constructed with weighted edges allowing the learnt features to dynamically adapt to the training data at an architecture level. Conditional padding modules, which are integrated into the network to reinsert voxels discarded by feature pooling, allow the network to inherently process different-size images at their original resolutions. FIRENet was trained for feature learning via automated semantic segmentation of pelvic structures and obtained a state-of-the-art median DSC score of 0.867. FIRENet was also simultaneously trained on MR (Magnatic Resonance) images acquired from 3D examinations of musculoskeletal elements in the (hip, knee, shoulder) joints and a public OAI knee dataset to perform automated segmentation of bone across anatomy. Transfer learning was used to show that the features learnt through the pelvic segmentation helped achieve improved mean DSC scores of 0.962, 0.963, 0.945 and 0.986 for automated segmentation of bone across datasets.Comment: 12 pages, 10 figure

Ontogeny and Adaptation: A Cross-Sectional Study of Primate Limb Elements

How primates achieve their adult skeletal form can be ascribed to two broad biological mechanisms: genetic inheritance, where morphological characters are regulated by an individual's phenotype over development; and plastic adaptation, where morphology responds to extrinsic factors engendered by the physical environment. While skeletal morphology should reflect an individual’s ecological demands throughout its life, only a limited amount of published research has considered how ontogeny and locomotor behaviour influence limb element form together. This thesis presents an investigation of long bone cross-sectional shape, size and strength, to inform how five catarrhine taxa adapt their limbs over development, and further, evaluate which limb regions more readily emit signals of plasticity or constraint along them. The sample includes Pan, Gorilla, Pongo, Hylobatidae and Macaca, subdivided into three developmental stages: infancy, juvenility and adulthood. Three-dimensional models of four upper (humerus and ulna) and lower (femur and tibia) limb elements were generated using a laser scanner and sectioned at proximal, midshaft and distal locations along each diaphysis. Three methods were used to compare geometry across the sample: 1) principal and anatomical axis ratios served as indices of section circularity, 2) polar section moduli evaluated relative strength between limb sections and 3) a geometric morphometric approach was developed to define section form. The results demonstrated that irrespective of taxonomic affinity, forelimb elements serve as strong indicators of posture and locomotor ontogenetic transitions, while hindlimb form is more reflective of body size and developmental shifts in body mass. Moreover, geometric variation at specific regions like the midhumerus was indistinguishable across all infant taxa in the sample, only exhibiting posture-specific signals among mature groups, while sections like the distal ulna exhibited little or no intraspecific variation over development. Identifying patterns of plasticity and constraint across taxonomic and developmental groups informs how limb cross-sections either allometrically or isometrically scale their form as they grow. These findings have direct implications to extant and extinct primate research pertaining to body mass estimation, functional morphology and behavioural ecology

Fully automatic algorithm for detecting and tracking anatomical shoulder landmarks on fluoroscopy images with artificial intelligence.

OBJECTIVE Patients with rotator cuff tears present often with glenohumeral joint instability. Assessing anatomic angles and shoulder kinematics from fluoroscopy requires labelling of specific landmarks in each image. This study aimed to develop an artificial intelligence model for automatic landmark detection from fluoroscopic images for motion tracking of the scapula and humeral head. MATERIALS AND METHODS Fluoroscopic images were acquired for both shoulders of 25 participants (N = 12 patients with unilateral rotator cuff tear, 6 men, mean (standard deviation) age: 63.7 ± 9.7 years; 13 asymptomatic subjects, 7 men, 58.2 ± 8.9 years) during a 30° arm abduction and adduction movement in the scapular plane with and without handheld weights of 2 and 4 kg. A 3D full-resolution convolutional neural network (nnU-Net) was trained to automatically locate five landmarks (glenohumeral joint centre, humeral shaft, inferior and superior edges of the glenoid and most lateral point of the acromion) and a calibration sphere. RESULTS The nnU-Net was trained with ground-truth data from 6021 fluoroscopic images of 40 shoulders and tested with 1925 fluoroscopic images of 10 shoulders. The automatic landmark detection algorithm achieved an accuracy above inter-rater variability and slightly below intra-rater variability. All landmarks and the calibration sphere were located within 1.5 mm, except the humeral landmark within 9.6 mm, but differences in abduction angles were within 1°. CONCLUSION The proposed algorithm detects the desired landmarks on fluoroscopic images with sufficient accuracy and can therefore be applied to automatically assess shoulder motion, scapular rotation or glenohumeral translation in the scapular plane. CLINICAL RELEVANCE STATEMENT This nnU-net algorithm facilitates efficient and objective identification and tracking of anatomical landmarks on fluoroscopic images necessary for measuring clinically relevant anatomical configuration (e.g. critical shoulder angle) and enables investigation of dynamic glenohumeral joint stability in pathological shoulders. KEY POINTS • Anatomical configuration and glenohumeral joint stability are often a concern after rotator cuff tears. • Artificial intelligence applied to fluoroscopic images helps to identify and track anatomical landmarks during dynamic movements. • The developed automatic landmark detection algorithm optimised the labelling procedures and is suitable for clinical application

Investigating Middle Stone Age foraging behaviour in the Karoo, South Africa

The Middle Stone Age (MSA) in Africa ~500- 50 kyr is recognised as a key time-period associated with important developments in hominin evolution, including the appearance of earliest genetic markers for Homo sapiens. Despite advances, our knowledge of the behaviour of hominins during this period is limited, especially for the early MSA (EMSA) pre-160ka. This study presents new data on animal bones recovered at the Bundu Farm site, in the upper Karoo region of the Northern Cape, South Africa, dated to circa ~300ka and found in association with EMSA type lithic facies, burning and hearth-like features. Previous analysis of the Bundu fauna compared the site to a G/wi hunter-gatherer 'biltong' processing locale, implying primary access to animal carcasses and socially complex hunting behaviour, circa 400-300 ka. An interpretation at odds with other interpretations of the EMSA data that suggest limited hunting and social complexity, and which would therefore have significant implications for MSA archaeology. To test the biltong hypothesis my study presents new data on the fracture characteristics of non-fresh animal bone broken by hammerstone and new environmental data for the site from an analysis of ostrich eggshell isotopes. Experimental and environmental data are used to provide a new interpretation of the Bundu fauna and my conclusion is that the data while not supporting the biltong model, does indicate evidence of delayed communal food consumption, use of fire and the transformation of foodstuffs into meals presaging and echoing social and ecological adaptations seen in the later MSA and LSA. The data also highlights a greater role for carnivores in the accumulation of the faunal assemblage and expedient hominin foraging similar to the preceding ESA and brings attention to the ecological relationships between hominins and carnivores in a Pleistocene Karoo environment that was markedly different from that of today. The study therefore rejects the biltong hypothesis for Bundu Farm as both inconsistent with likely EMSA social structures and ecology and instead proposes the site as evidence for novel behaviour indicative of a transition from ESA to MSA lifeways. The Bundu Farm site reflecting a rare archaeological occurrence where the shift in the behavioural trajectory that led to our species is observed