Search

Samuel Multack,1 Li-Chen Pan,2 Sean K Timmons,2 Manasi Datar,2 Chia-Wen Hsiao,3 Raiju Babu,3 Sun-Ming Pan,3 Lawrence Woodard4 1Multack Eye Care and Associates, Olympia Fields, IL, USA; 2Commercial Strategy and Market Access, Boston Healthcare Associates (a Veranex Company), Boston, MA, USA; 3Health Economics and Outcomes Research, Alcon, Fort Worth, TX, USA; 4Atlanta Eye Surgery Center, Atlanta, GA, USACorrespondence: Samuel Multack, Multack Eye Care and Associates, 20303 Crawford Ave, Olympia Fields, IL, 60461, USA, Tel +1-708-898-1858, Email smultack@mecvision.comPurpose: This study aims to assess the time impact of ARGOS® (image-guided swept-source optical coherence tomography biometer integrated with operating room (OR) technologies (SS-OCT w/ORT)) compared to LENSTAR LS 900 (optical low-coherence reflectometry (OLCR)), IOLMaster 500 (partial coherence interferometry (PCI)), and IOLMaster 700 (SS-OCT) on efficiency in the cataract evaluation and surgery.Patients and Methods: Data from 212 patients (two study sites) who underwent evaluation and/or cataract surgery were collected. The primary objective was to compare the performance of four biometers; statistical analyses were conducted to compare 1) biometer measurement times for all patients (ANOVA w/post-hoc Dunnett’s test) and stratified by cataract density (ANOVA) and 2) rate of biometer acquisition failure (Chi-square test w/post-hoc Bonferroni correction). Real-world observational data collected were then used to develop a practice-based time-efficiency model to demonstrate the combined effect that adopting an SS-OCT w/ORT has on a practice’s cataract workflow. Real-world data inputs included assessment of patient’s eyes’ cataract grade density, time taken for optical biometry, Manual A-scan (ultrasound biometer) when acquisition failed, and measurement times associated with other devices used in cataract evaluation and surgery.Results: For 208 patients (56% non-dense, 44% dense), the SS-OCT w/ORT biometer had a 0% acquisition failure (SS-OCT: 3% (p = 0.05); OLCR: 5% (p = 0.004); PCI: 15% (p < 0.0001)) and an average time savings of 30 seconds/patient compared to the other biometers in this study (p < 0.05). When acquisition failed, ultrasound biometry resulted in an additional 2.5 minutes/patient. For a cohort of 1000 patients, an SS-OCT w/ORT and an image-guidance system adopted at a practice using an SS-OCT, femtosecond laser, and intraoperative aberrometer offer up to 58% efficiency gain across the cataract workflow.Conclusion: Results from this study demonstrate an SS-OCT w/ORT’s efficiencies in cataract evaluation and surgery driven by faster measurement times, reducing the need for ultrasound biometry, and its integration benefits with other devices.Keywords: cataract surgery, swept-source optical coherence tomography biometer, time-and-motion study, preoperative evaluation phase

To assess healthcare resource utilization and costs for female patients diagnosed with stress or mixed urinary incontinence (SUI/MUI) compared to a matched cohort of patients without SUI/MUI.We conducted a retrospective matched cohort study of women using the IBM MarketScan research database. Women diagnosed with SUI/MUI between July 1, 2014 and June 30, 2016 were identified using International Classification of Diseases 9 and 10 codes for SUI or MUI with the date of first diagnosis as the index date from which 2-year postindex healthcare resource use and direct cost data were derived from claims, examined, and compared 1:1 with patients without a SUI/MUI diagnosis, matched by age and Charlson's Comorbidity Index.A total of 68 636 women with SUI/MUI were matched 1:1 with controls. In the 2-year postindex date, a significantly higher proportion of SUI/MUI patients had ≥1 inpatient visit and ≥1 outpatient visit compared to the control group (inpatient: 18.89% vs. 12.10%, p 0.0001; outpatient: 88.44% vs. 73.23%, p 0.0001). Mean primary care visits were significantly higher in SUI/MUI patients compared to controls (7.33 vs. 5.53; p 0.0001) as were specialist visits (1.2 vs. 0.08; p 0.0001). Mean all-cause outpatient costs were higher in SUI/MUI patients compared to controls ($7032.10 vs. $3348.50; p 0.0001), as were inpatient costs ($3990.70 vs. $2313.70; p 0.0001).Women with SUI/MUI consume significantly higher medical resources and incur higher costs to payers, compared to women without SUI/MUI. While reasons for this are not fully understood, improved and standardized treatment for women with SUI/MUI may positively affect cost and outcomes.

To describe the characteristics of women with stress or mixed urinary incontinence (SUI/MUI) receiving physical therapy (PT) services, including referral patterns and PT utilization.Female patients with claims associated with an SUI or MUI diagnosis (International Classification of Disease-Clinical Modification [ICD-9-CM]: 625.6, 788.33, or ICD-10-CM: N39.3, N39.46) between July 01, 2014 and June 30, 2016 were identified in International business machines (IBM)'s MarketScan Research Database. Inclusion criteria included the absence of pregnancy claims and ≥80% medical and pharmacy enrollment pre- and postindex. First SUI/MUI diagnosis claim determined index. Patients were followed for 2 years, and associated UI-associated PT encounters were identified. Descriptive statistics were calculated for patients with at least one PT visit during the postindex period.In a cohort of 103,813 women with incident SUI or MUI diagnosis, 2.6% (2792/103,813) had at least one PT visit in the 2 years following their diagnosis. Mean age at index PT encounter was 50.55 years. A total of 52.36% (1462/2792) women had one to four PT visits; 21.2% (592/2792) had8 PT visits. In subanalysis of the PT cohort (1345/2792), women who received PT only had the lowest average 2-year postindex total medical cost (mean: $12,671; SD: $16,346), compared with PT plus medications (mean: $27,394; SD: $64,481), and PT plus surgery (mean: $33,656; SD: $26,245), respectively. Over 40% had their first PT visit ≥3 months after their index date.The percentage of women with a PT visit associated with an incident SUI or MUI diagnosis was low (2.6%), and 30% of this group completed three or more PT visits. This suggests poor adherence to clinical guidelines regarding supervised treatment of UI in women.Our study suggests underutilization of PT among insured women with SUI and MUI in the 2 years following diagnosis. Interventions to improve this gap in first-line care may represent an opportunity for an increased role for PTs in the care of women with UI.

Estimated glomerular filtration rate (eGFR) and albuminuria, the current standard-of-care tests that predict risk of kidney function decline in early-stage diabetic kidney disease (DKD), are only modestly useful. We evaluated the decision-making impact of an artificial intelligence-enabled prognostic test, KidneyIntelX, in the management of DKD by primary care physicians (PCPs).This was a prospective web-based survey administered among PCPs in the United States.We used conjoint analysis with multivariable logit models to estimate PCP preferences. The survey included hypothetical patient profiles with 6 attributes: albuminuria, eGFR, age, blood pressure (BP), hemoglobin A1c (HbA1c), and KidneyIntelX result. Each PCP viewed 8 patient profiles randomly selected from 42 unique profiles having 1 level from each attribute. For each patient, PCPs were asked to indicate whether they would prescribe a sodium-glucose cotransporter-2 (SGLT2) inhibitor, increase angiotensin receptor blocker (ARB) dose, and/or refer to a nephrologist.A total of 401 PCPs completed the survey (response rate, 8.8%). The relative importance of the top 2 attributes for each decision were HbA1c (52%) and KidneyIntelX result (23%) for prescribing SGLT2 inhibitors, BP (62%) and KidneyIntelX result (13%) for increasing ARB dose, and eGFR (42%) and KidneyIntelX result (27%) for nephrologist referral. A high-risk KidneyIntelX result was associated with significantly higher odds of PCPs prescribing SGLT2 inhibitors (odds ratio [OR], 1.64; 95% CI, 1.29-2.08), increasing ARB dose (OR, 1.49; 95% CI, 1.17-1.89), and referring to a nephrologist (OR, 2.47; 95% CI, 1.99-3.08) compared with no test.The KidneyIntelX test had greater relative importance than albuminuria and eGFR to PCPs in making treatment decisions and was second only to eGFR for nephrologist referrals. Because of its significant impact on decision-making, KidneyIntelX has high clinical utility in DKD management.

The objective of this analysis was to describe longitudinal adherence with recommended urinary incontinence (UI) evaluation and treatment guidelines over a 2-year period in patients newly diagnosed with stress (SUI) or mixed UI (MUI), and average 2-year cost associated with initial treatment.A retrospective claims analysis using the IBM MarketScan database was conducted. Women diagnosed with SUI/MUI between July 1, 2014 and June 30, 2016 were identified using the International Classification of Diseases (ICD) 9 and 10 codes for SUI or MUI. Newly diagnosed SUI/MUI patients who did not have a UI-related diagnosis for at least 1 year before their index date were assessed.103 813 patients with newly diagnosed SUI or MUI were identified. Of those, 96.15% (99 821/103 813) received an initial evaluation in accordance with professional guidelines (e.g., patient history, physical examination, urinalysis). Only 6.8% (5086/74 925) and 7.7% (2229/28 888) of patients with SUI and MUI, respectively, received a first-line behavioral treatment (e.g., pelvic floor muscle exercises, bladder training), according to guidelines. The 2-year average UI-related medical costs associated with guideline adherence for SUI were $5770.93 ± $9454.81 and for MUI, $4416.16 ± $7401.53. Nonadherence was observed in 59.2% (44 382/74 925) of SUI and 64.1% (18 530/28 888) of MUI patients. Two-year average UI-related medical costs for the nonadherent group were $8568.00 ± $11 275.52 for SUI and $6986.66 ± $10 765.55 for MUI, significantly more than the adherent group (p 0.0001).The majority of SUI or MUI patients do not receive a documented behavioral intervention as their first-line treatment, which is a recommendation by professional society guidelines. This was found to affect the cost burden for payers; those that were nonadherent had significantly higher costs 2-year postindex.

Background We describe and evaluate a deep network algorithm which automatically contours organs at risk in the thorax and pelvis on computed tomography (CT) images for radiation treatment planning. Methods The algorithm identifies the region of interest (ROI) automatically by detecting anatomical landmarks around the specific organs using a deep reinforcement learning technique. The segmentation is restricted to this ROI and performed by a deep image-to-image network (DI2IN) based on a convolutional encoder-decoder architecture combined with multi-level feature concatenation. The algorithm is commercially available in the medical products “syngo.via RT Image Suite VB50” and “AI-Rad Companion Organs RT VA20” (Siemens Healthineers). For evaluation, thoracic CT images of 237 patients and pelvic CT images of 102 patients were manually contoured following the Radiation Therapy Oncology Group (RTOG) guidelines and compared to the DI2IN results using metrics for volume, overlap and distance, e.g., Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD95). The contours were also compared visually slice by slice. Results We observed high correlations between automatic and manual contours. The best results were obtained for the lungs (DSC 0.97, HD95 2.7 mm/2.9 mm for left/right lung), followed by heart (DSC 0.92, HD95 4.4 mm), bladder (DSC 0.88, HD95 6.7 mm) and rectum (DSC 0.79, HD95 10.8 mm). Visual inspection showed excellent agreements with some exceptions for heart and rectum. Conclusions The DI2IN algorithm automatically generated contours for organs at risk close to those by a human expert, making the contouring step in radiation treatment planning simpler and faster. Few cases still required manual corrections, mainly for heart and rectum.

To evaluate the U.S. payer budget-impact of KidneyIntelX, an artificial intelligence-enabledWe developed an Excel-based model according to International Society of Pharmacoeconomics and Outcomes Research (ISPOR) good practices to assess U.S. payer budget impact associated with the use of the KidneyIntelX test to optimize therapy T2DKD patients compared to standard of care (SOC) (without KidneyIntelX). A hypothetical cohort of 100,000 stages 1-3b T2DKD patients was followed for 5 years. Peer-reviewed publications were used to identify model parameter estimates. KidneyIntelX costs incremental to SOC (without KidneyIntelX) included test cost, additional prescription medication use, specialist referrals and PCP office visits. Patients managed with KidneyIntelX experienced a 20% slowed progression rate compared to SOC (without KidneyIntelX) attributed to slowed DKD progression, delayed or prevented dialysis and transplants, and reduced dialysis crashes. Associated costs were compared to SOC (without KidneyIntelX). Sensitivity analyses were conducted by varying the definition of progression and the DKD progression rate associated with KidneyIntelX testing and related interventions.Projected undiscounted base case 5-year savings for 100,000 patients tested with KidneyIntelX were $1.052 billion, attributed mostly to slowed progression through DKD stages. The breakeven point for the health plan adopting KidneyIntelX is expected to occur prior to year 2 after adoption. Sensitivity analysis based on the assessment of the most conservative definition of progression and a 5% reduction in progression rate attributed to KidneyIntelX, resulted in a projected 5-year savings of $145 million associated with KidneyIntelX.Limitations included reliance on literature-based parameter estimates, including effect size of delayed progression supported by the literature. Incorporating KidneyIntelX in contemporary care of early-stage T2DKD patients is projected to result in substantial savings to payers.

Diabetic kidney disease (DKD) develops in 1 in 3 people with type 2 diabetes (T2D) and is the leading cause of end-stage renal disease (ESRD). DKD currently costs the US healthcare system USD 50 billion annually. PromarkerD is a simple biomarker-based blood test that can predict future renal function decline in T2D patients who have no or mild existing DKD (eGFR >30). This study estimated the net savings to US payers from covering the PromarkerD test versus current standard of care (SOC). Model inputs included costs and frequency of testing, costs associated with changes to patient medication strategies, and cost-savings from slowed DKD progression and averted ESRD interventions (dialysis and kidney transplants). All estimates and inputs for the model were collected from recent peer-reviewed literature, established insurance payment rates, and PromarkerD clinical studies. Results showed that over ten years the net savings for US payers by adopting PromarkerD testing for T2D patients (KDIGO DKD category G1-3b) would exceed USD 14 billion and the equilibrium point (costs equal savings) can be achieved after two years. Primarily, savings arise from slower DKD stage progression, with contributions from delayed dialysis and transplants, and also from fewer unplanned dialyses. This economic study demonstrates that improved management of T2D patients through the use of early, accurate and cost-effective prognosis with the PromarkerD test could result in substantial savings to US payers in the treatment of DKD. Employing this alternative PromarkerD testing regime over the current SOC would enable early interventions for at-risk patients, thereby decreasing the need for expensive interventions such as dialysis and transplants, or unnecessary adoption of new therapeutic treatments. Disclosure W. Burchenal: Research Support; Self; Proteomics International. M. Datar: Research Support; Self; Proteomics International. K. E. Peters: Employee; Self; Proteomics International, Stock/Shareholder; Self; Proteomics International. G. C. Fernandez: Employee; Self; Proteomics International, Stock/Shareholder; Self; Proteomics International. J. C. Morrison: Consultant; Self; Proteomics International. R. J. Lipscombe: Employee; Self; Proteomics International.

Purpose To compare patients with atrial fibrillation (AF) initiating direct oral anticoagulants (DOACs) versus warfarin on clinical outcomes including stroke, systemic embolism (SE), bleeding events, and cost of care. Methods This retrospective observational study used Medicare Advantage Prescription Drug and fully insured commercial claims from the Humana Research Database. Patients with AF who initiated a DOAC or warfarin from January 1, 2012, through September 30, 2015, were included. Date of the first prescription of DOAC or warfarin was the index date. Patients in the DOAC and warfarin groups were matched on propensity scores. Patients were censored at end of enrollment or study period, discontinuation, or switch of index medication. Clinical outcomes were compared in the matched groups using Cox proportional hazards models. Annualized costs and costs adjusted for censoring using Lin's interval method were also compared between the two cohorts. Results Patients on DOACs had a significantly lower risk of ischemic stroke (hazard ratio [HR], 0.88; 95% confidence interval [CI], 0.79-0.98), hemorrhagic stroke (HR, 0.65; CI, 0.46-0.92), SE (HR, 0.53; 95% CI, 0.43-0.65), and composite outcome of stroke or SE (HR, 0.78; 95% CI, 0.71-0.86) compared with patients on warfarin. Bleeding risk was not statistically significant (HR, 0.85; 95% CI, 0.71-1.01). While annualized pharmacy costs were higher, annualized medical and total costs were lower in the DOAC group compared with the warfarin group. Conclusion The results of the study indicated that patients on DOACs had lower rates of ischemic stroke, hemorrhagic stroke, SE, and composite outcome of stroke or SE compared with patients on warfarin. No significant differences in bleeding rates between the DOAC and warfarin groups were observed, while total cost of care was lower in the DOAC group.

Background A majority of diabetic kidney disease (DKD) patients receive medical care in the primary care setting, making it an important opportunity to improve patient management. There is limited evidence evaluating whether primary care physicians (PCPs) are equipped to effectively manage these patients in routine clinical practice. The present study was undertaken to identify gaps in primary care and unmet needs in the diagnosis and monitoring of DKD in type 2 diabetes (T2D) patients among PCPs. Methods This was a qualitative analysis based on 30–45-min interviews with PCPs treating T2D patients. PCPs were recruited via email and were board-certified, in practice for more than 3 years, spent most of their time in direct clinical care, and provided care for more than three T2D patients in a week. Descriptive data analysis was conducted to identify and examine themes that were generated by interviews. Two reviewers evaluated interview data to identify themes and developed consensus on the priority themes identified. Results A total of 16 PCPs satisfying the inclusion criteria were recruited for qualitative interviews. Although the PCPs recognized kidney disease as an important comorbidity in T2D patients, testing for kidney disease was not consistently top of mind, with 56% reportedly performing kidney function testing in their T2D patients. PCPs most frequently reported using estimated glomerular filtration rate (eGFR) alone to monitor and stage DKD; only 25% PCPs reported testing for albuminuria. Most PCPs incorrectly believed that a majority of DKD patients are diagnosed in early stages. Also, early stages of DKD emerged as ambiguous areas of decision-making, wherein treatments prescribed greatly varied among PCPs. Lastly, early and accurate risk stratification of DKD patients emerged as the most important unmet need; which, if it could be overcome, was consistently identified by PCPs as a key to monitoring, appropriate nephrologist referrals, and intervening to improve outcomes in patients with DKD. Conclusions Our study highlights important unmet needs in T2D DKD testing, staging, and stratification in the PCP setting that limit effective patient care. Health systems and insurers in the U.S. should prioritize the review and approval of new strategies that can improve DKD staging and risk stratification.

Additional file 1: Table 1. Summary of interview guide.

Aim To evaluate the U.S. payer budget-impact of KidneyIntelX, an artificial intelligence-enabled in vitro diagnostic to predict kidney function decline in Type 2 Diabetic Kidney Disease (T2DKD) patients, stages 1-3b. Materials and methods We developed an Excel-based model according to International Society of Pharmacoeconomics and Outcomes Research (ISPOR) good practices to assess U.S. payer budget impact associated with the use of the KidneyIntelX test to optimize therapy T2DKD patients compared to standard of care (SOC) (without KidneyIntelX). A hypothetical cohort of 100,000 stages 1-3b T2DKD patients was followed for 5 years. Peer-reviewed publications were used to identify model parameter estimates. KidneyIntelX costs incremental to SOC (without KidneyIntelX) included test cost, additional prescription medication use, specialist referrals and PCP office visits. Patients managed with KidneyIntelX experienced a 20% slowed progression rate compared to SOC (without KidneyIntelX) attributed to slowed DKD progression, delayed or prevented dialysis and transplants, and reduced dialysis crashes. Associated costs were compared to SOC (without KidneyIntelX). Sensitivity analyses were conducted by varying the definition of progression and the DKD progression rate associated with KidneyIntelX testing and related interventions. Results Projected undiscounted base case 5-year savings for 100,000 patients tested with KidneyIntelX were $1.052 billion, attributed mostly to slowed progression through DKD stages. The breakeven point for the health plan adopting KidneyIntelX is expected to occur prior to year 2 after adoption. Sensitivity analysis based on the assessment of the most conservative definition of progression and a 5% reduction in progression rate attributed to KidneyIntelX, resulted in a projected 5-year savings of $145 million associated with KidneyIntelX. Limitations and conclusions Limitations included reliance on literature-based parameter estimates, including effect size of delayed progression supported by the literature. Incorporating KidneyIntelX in contemporary care of early-stage T2DKD patients is projected to result in substantial savings to payers.

Aim: To compare adherence, healthcare utilization and costs among real world, Medicare-eligible patients with schizophrenia using long-acting injectable paliperidone palmitate (PP) versus oral atypical antipsychotics. Patients & methods: Historical cohort study used Medicare Advantage claims data. Inverse probability of treatment weighting was applied to adjust for baseline differences. 12-month adherence, healthcare utilization and costs were compared. Results: Patients using PP were more adherent (proportion of days covered ≥0.8; 48.1 vs 32.6%; p Conclusion: Compared with patients using oral atypical antipsychotics, PP had lower hospitalizations and medical costs with greater medication adherence accompanied by higher pharmacy costs.

International audience; Computer models of the heart are of increasing interest for clinical applications due to their discriminative and predictive abilities. However the number of simulation parameters in these models can be high and expert knowledge is required to properly design studies involving these models, and analyse the results. In particular it is important to know how the parameters vary in various clinical or physiological settings. In this paper we build a data-driven model of cardiovascular parameter evolution during digestion, from a clinical study involving more than 80 patients. We first present a method for longitudinal parameter estimation in 3D cardiac models, which we apply to 21 patient-specific hearts geometries at two instants of the study, for 6 parameters (two fixed and four time-varying parameters). From these personalised hearts, we then extract and validate a law which links the changes of cardiac output and heart rate under constant arterial pressure to the evolution of these parameters, thus enabling the fast simulation of hearts during digestion for future patients.

International audience; Personalised 3D modelling of the heart is of increasing interest in order to better characterise pathologies and predict evolution. The personalisation consists in estimating the parameter values of an electromechanical model in order to reproduce the observed cardiac motion. However, the number of parameters in these models can be high and their estimation may not be unique. This variability can be an obstacle to further analyse the estimated parameters and for their clinical interpretation. In this paper we present a method to perform consistent estimations of electromechanical parameters with prior probabilities on the estimated values, which we apply on a large database of 84 different heartbeats. We show that the use of priors reduces considerably the variance in the estimated parameters, enabling better conditioning of the parameters for further analysis of the cardiac function. This is demonstrated by the application to longitudinal data of paediatric cardiomyopathies, where the estimated parameters provide additional information on the pathology and its evolution.

International audience; In this paper, we propose an innovative approach for registration based on the deterministic prediction of the parameters from both images instead of the optimization of a energy criteria. The method relies on a fully convolutional network whose architecture consists of contracting layers to detect relevant features and a symmetric expanding path that matches them together and outputs the transformation parametriza-tion. Whereas convolutional networks have seen a widespread expansion and have been already applied to many medical imaging problems such as segmentation and classification, its application to registration has so far faced the challenge of defining ground truth data on which to train the algorithm. Here, we present a novel training strategy to build reference deformations which relies on the registration of segmented regions of interest. We apply this methodology to the problem of inter-patient heart registration and show an important improvement over a state of the art optimization based algorithm. Not only our method is more accurate but it is also faster-registration of two 3D-images taking less than 30ms second on a GPU-and more robust to outliers.

Statistical shape modeling (SSM) was used to quantify 3D variation and morphologic differences between femurs with and without cam femoroacetabular impingement (FAI). 3D surfaces were generated from CT scans of femurs from 41 controls and 30 cam FAI patients. SSM correspondence particles were optimally positioned on each surface using a gradient descent energy function. Mean shapes for groups were defined. Morphological differences between group mean shapes and between the control mean and individual patients were calculated. Principal component analysis described anatomical variation. Among all femurs, the first six modes (or principal components) captured significant variations, which comprised 84% of cumulative variation. The first two modes, which described trochanteric height and femoral neck width, were significantly different between groups. The mean cam femur shape protruded above the control mean by a maximum of 3.3 mm with sustained protrusions of 2.5-3.0 mm along the anterolateral head- neck junction/distal anterior neck. SSM described variations in femoral morphology that corresponded well with areas prone to damage. Shape variation described by the first two modes may facilitate objective characterization of cam FAI deformities; variation beyond may be inherent population variance. SSM could characterize disease severity and guide surgical resection of bone. 2013 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 31:1620-1626, 2013

Solid voxelization represents the process of transforming a polygonal mesh into a voxel representation by associating each polygon of a mesh with the cells in the voxel grid. We introduce a novel approach for the voxelization of solid objects, designed for Graphics Processing Units (GPU). The method is based on a heuristic approach that computes an approximate distance field instead of using mesh surface normals or exact point-to-triangle distances. Two main steps are required: voxel marking and distance field computation. In the first step, each voxel is marked based on its location relative to the mesh (inside, outside of the domain or on its boundary), and, during the second step, a signed distance field is computed. Experiments focused on meshes encountered in medical imaging applications: a left ventricle and a coronary artery. The proposed method is found to be exceptionally robust as it is able to handle meshes with severe defects such as self intersections and holes. The GPU based implementation is on average 20 times faster than the multi-core CPU based implementation.

Individuals with multiple osteochondromas (MO) demonstrate shortened long bones. Ext1 or Ext2 haploinsufficiency cannot recapitulate the phenotype in mice. Loss of heterozygosity for Ext1 may induce shortening by steal of longitudinal growth into osteochondromas or by a general derangement of physeal signaling. We induced osteochondromagenesis at different time points during skeletal growth in a mouse genetic model, then analyzed femora and tibiae at 12 weeks using micro-CT and a point-distribution-based shape analysis. Bone lengths and volumes were compared. Metaphyseal volume deviations from normal, as a measure of phenotypic widening, were tested for correlation with length deviations. Mice with osteochondromas had shorter femora and tibiae than controls, more consistently when osteochondromagenesis was induced earlier during skeletal growth. Volumetric metaphyseal widening did not correlate with longitudinal shortening, although some of the most severe shortening was in bones with abundant osteochondromas. Loss of heterozygosity for Ext1 was sufficient to drive bone shortening in a mouse model of MO, but shortening did not correlate with osteochondroma volumetric growth. While a steal phenomenon seems apparent in individual cases, some other mechanism must also be capable of contributing to the short bone phenotype, independent of osteochondroma formation. Clones of chondrocytes lacking functional heparan sulfate must blunt physeal signaling generally, rather than stealing growth potential focally. © 2012 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 31: 651–657, 2013

Statistical shape analysis of anatomical structures plays an important role in many medical image analysis applications such as understanding the structural changes in anatomy in various stages of growth or disease. Establishing accurate correspondence across object populations is essential for such statistical shape analysis studies. In this paper, we present an entropy-based correspondence framework for computing point-based correspondence among populations of surfaces in a groupwise manner. This robust framework is parameterization-free and computationally efficient. We review the core principles of this method as well as various extensions to deal effectively with surfaces of complex geometry and application-driven correspondence metrics. We apply our method to synthetic and biological datasets to illustrate the concepts proposed and compare the performance of our framework to existing techniques. Through the numerous extensions and variations presented here, we create a very flexible framework that can effectively handle objects of various topologies, multi-object complexes, open surfaces, and objects of complex geometry such as high-curvature regions or extremely thin features.

International audience; Computer models of the heart are of increasing interest for clinical applications due to their discriminative and predictive power. However the personalisation step to go from a generic model to a patient-specific one is still a scientific challenge. In particular it is still difficult to quantify the uncertainty on the estimated parameters and predicted values. In this manuscript we present a new pipeline to evaluate the impact of fibre uncertainty on the personalisation of an electromechanical model of the heart from ECG and medical images. We detail how we estimated the variability of the fibre architecture among a given population and how the uncertainty generated by this variability impacts the following personalisation. We first show the variability of the personalised simulations, with respect to the principal variations of the fibres. Then discussed how the variations in this (small) healthy population of fibres impact the parameters of the personalised simulations.

This paper presents two approaches to quantifying and visualizing variation in datasets of trees. The first approach localizes subtrees in which significant population differences are found through hypothesis testing and sparse classifiers on subtree features. The second approach visualizes the global metric structure of datasets through low-distortion embedding into hyperbolic planes in the style of multidimensional scaling. A case study is made on a dataset of airway trees in relation to Chronic Obstructive Pulmonary Disease.

Statistical shape modeling (SSM) was used to quantify 3D variation and morphologic differences between femurs with and without cam femoroacetabular impingement (FAI). 3D surfaces were generated from CT scans of femurs from 41 controls and 30 cam FAI patients. SSM correspondence particles were optimally positioned on each surface using a gradient descent energy function. Mean shapes for groups were defined. Morphological differences between group mean shapes and between the control mean and individual patients were calculated. Principal component analysis described anatomical variation. Among all femurs, the first six modes (or principal components) captured significant variations, which comprised 84% of cumulative variation. The first two modes, which described trochanteric height and femoral neck width, were significantly different between groups. The mean cam femur shape protruded above the control mean by a maximum of 3.3 mm with sustained protrusions of 2.5–3.0 mm along the anterolateral head-neck junction/distal anterior neck. SSM described variations in femoral morphology that corresponded well with areas prone to damage. Shape variation described by the first two modes may facilitate objective characterization of cam FAI deformities; variation beyond may be inherent population variance. SSM could characterize disease severity and guide surgical resection of bone.

Description of purpose: The NA-MIC SPHARM-PDM Toolbox represents an automated set of tools for the computation of 3D structural statistical shape analysis. SPHARM-PDM solves the correspondence problem by defining a first order ellipsoid aligned, uniform spherical parameterization for each object with correspondence established at equivalently parameterized points. However, SPHARM correspondence has shown to be inadequate for some biological shapes that are not well described by a uniform spherical parameterization. Entropy-based particle systems compute correspondence by representing surfaces as discrete point sets that does not rely on any inherent parameterization. However, they are sensitive to initialization and have little ability to recover from initial errors. By combining both methodologies we compute reliable correspondences in topologically challenging biological shapes. Data: Diverse subcortical structures cohorts were used, obtained from MR brain images. Method(s): The SPHARM-PDM shape analysis toolbox was used to compute point based correspondent models that were then used as initializing particles for the entropy-based particle systems. The combined framework was implemented as a stand-alone Slicer3 module, which works as an end-to-end shape analysis module. Results: The combined SPHARM-PDM-Particle framework has demonstrated to improve correspondence in the example dataset over the conventional SPHARM-PDM toolbox. Conclusions: The work presented in this paper demonstrates a two-sided improvement for the scientific community, being able to 1) find good correspondences among spherically topological shapes, that can be used in many morphometry studies 2) offer an end-to-end solution that will facilitate the access to shape analysis framework to users without computer expertise.

In this paper, we propose a new method for longitudinal shape analysis that fits a linear mixed-effects model, while simultaneously optimizing correspondences on a set of anatomical shapes. Shape changes are modeled in a hierarchical fashion, with the global population trend as a fixed effect and individual trends as random effects. The statistical significance of the estimated trends are evaluated using specifically designed permutation tests. We also develop a permutation test based on the Hotelling T2 statistic to compare the average shapes trends between two populations. We demonstrate the benefits of our method on a synthetic example of longitudinal tori and data from a developmental neuroimaging study.

An ensemble of biological shapes can be represented and analyzed with a dense set of point correspondences. In previous work, optimal point placement was determined by optimizing an information theoretic criterion that depends on relative spatial locations on different shapes combined with pairwise Euclidean distances between nearby points on the same shape. These choices have prevented such methods from effectively characterizing shapes with complex geometry such as thin or highly curved features. This paper extends previous methods for automatic shape correspondence by taking into account the underlying geometry of individual shapes. This is done by replacing the Euclidean distance for intrashape pairwise particle interactions by the geodesic distance. A novel set of numerical techniques for fast distance computations on curved surfaces is used to extract these distances. In addition, we introduce an intershape penalty term that incorporates surface normal information to achieve better particle correspondences near sharp features. Finally, we demonstrate this new method on synthetic and biological datasets.

Shape regression promises to be an important tool to study the relationship between anatomy and underlying clinical or biological parameters, such as age. In this paper we propose a new method to building shape models that incorporates regression analysis in the process of optimizing correspondences on a set of open surfaces. The statistical significance of the dependence is evaluated using permutation tests designed to estimate the likelihood of achieving the observed statistics under numerous rearrangements of the shape parameters with respect to the explanatory variable. We demonstrate the method on synthetic data and provide a new results on clinical MRI data related to early development of the human head.

Intra-subject deformable registration applications, such as longitudinal analysis and multi-modal imaging, use a high degree freedom deformation to accurately align soft tissue. However, smoothness constraints applied to the deformation and insufficient degrees of freedom in the deformation may distort the more rigid tissue types such as bone. In this paper, we present a technique that aligns rigid structures using rigid constraints while aligning soft tissue with a high degree of freedom deformation.

Prostate cancer is the most common cancer among men, excluding skin cancer. It is diagnosed by histopathology interpretation of Hematoxylin and Eosin (H&E)-stained tissue sections. Gland and nuclei distributions vary with the disease grade, and the morphological features vary with the advance of cancer. A tissue microarray with known disease stages can be used to enable efficient pathology slide image analysis. We focus on an intuitive approach for segmenting such images, using the Hierarchical Self-Organizing Map (HSOM). Our approach introduces the use of unsupervised clustering using both color and texture features, and the use of unsupervised color merging outside of the HSOM framework. The HSOM was applied to segment 109 tissues composed of four tissue clusters: glands, epithelia, stroma, and nuclei. These segmentations were compared with the results of an EM Gaussian clustering algorithm. The proposed method confirms that the self-learning ability and adaptability of the HSOM, coupled with the information fusion mechanism of the hierarchical network, leads to superior segmentation results for tissue images.

With an increase in full-body scans and longitudinal acquisitions to track disease progression, it becomes significant to find correspondence between multiple images. One example would be the monitoring size/location of tumors using PET images during chemotherapy to determine treatment progression. While there is a need to go beyond a single parametric transform to recover misalignments, pure deformable solutions become complex, time-consuming and unnecessary at times. Simple anatomically guided approach for whole body image registration offers enhanced alignment of large coverage inter-scan studies. In this experiment, we provide anatomy specific transformations to capture their independent motions. This solution is characterized by an automatic segmentation of regions in the image, followed by a custom registration and volume stitching. We have tested this algorithm on phantom images as well as clinical longitudinal datasets. We were successful in proving that decoupling transformations improves the overall registration quality.

This paper presents a novel approach to natural scene segmentation. It uses both color and texture features in cooperation to provide comprehensive knowledge about every pixel in the image. A novel scheme for the collection of training samples, based on homogeneity, is proposed. Natural scene segmentation is carried out using a two-stage hierarchical self-organizing map (HSOM). The proposed method confirms that the sample selection based on homogeneity and the selflearning ability and adaptability of the HSOM, coupled with the information fusion mechanism, can lead to good segmentation result, which is validated by experiments on a variety of natural scene images.