Multi-Planar Deep Segmentation Networks for Cardiac Substructures from MRI and CT

Non-invasive detection of cardiovascular disorders from radiology scans requires quantitative image analysis of the heart and its substructures. There are well-established measurements that radiologists use for diseases assessment such as ejection fraction, volume of four chambers, and myocardium mass. These measurements are derived as outcomes of precise segmentation of the heart and its substructures. The aim of this paper is to provide such measurements through an accurate image segmentation algorithm that automatically delineates seven substructures of the heart from MRI and/or CT scans. Our proposed method is based on multi-planar deep convolutional neural networks (CNN) with an adaptive fusion strategy where we automatically utilize complementary information from different planes of the 3D scans for improved delineations. For CT and MRI, we have separately designed three CNNs (the same architectural configuration) for three planes, and have trained the networks from scratch for voxel-wise labeling for the following cardiac structures: myocardium of left ventricle (Myo), left atrium (LA), left ventricle (LV), right atrium (RA), right ventricle (RV), ascending aorta (Ao), and main pulmonary artery (PA). We have evaluated the proposed method with 4-fold-cross validation on the multi-modality whole heart segmentation challenge (MM-WHS 2017) dataset. The precision and dice index of 0.93 and 0.90, and 0.87 and 0.85 were achieved for CT and MR images, respectively. While a CT volume was segmented about 50 seconds, an MRI scan was segmented around 17 seconds with the GPUs/CUDA implementation.Comment: The paper is accepted to STACOM 201

CardiacNET: Segmentation of Left Atrium and Proximal Pulmonary Veins from MRI Using Multi-View CNN

Anatomical and biophysical modeling of left atrium (LA) and proximal pulmonary veins (PPVs) is important for clinical management of several cardiac diseases. Magnetic resonance imaging (MRI) allows qualitative assessment of LA and PPVs through visualization. However, there is a strong need for an advanced image segmentation method to be applied to cardiac MRI for quantitative analysis of LA and PPVs. In this study, we address this unmet clinical need by exploring a new deep learning-based segmentation strategy for quantification of LA and PPVs with high accuracy and heightened efficiency. Our approach is based on a multi-view convolutional neural network (CNN) with an adaptive fusion strategy and a new loss function that allows fast and more accurate convergence of the backpropagation based optimization. After training our network from scratch by using more than 60K 2D MRI images (slices), we have evaluated our segmentation strategy to the STACOM 2013 cardiac segmentation challenge benchmark. Qualitative and quantitative evaluations, obtained from the segmentation challenge, indicate that the proposed method achieved the state-of-the-art sensitivity (90%), specificity (99%), precision (94%), and efficiency levels (10 seconds in GPU, and 7.5 minutes in CPU).Comment: The paper is accepted by MICCAI 2017 for publicatio

Aortic Valve Thrombosis in Antiphospholipid Syndrome Causing Coronary Artery Embolic Disease

Antiphospholipid syndrome (APS) is a disorder characterized by the presence of anti-phospholipid antibodies which can include Lupus anticoagulant and Anticardiolipin antibody [1]. These antibodies bind to cardiolipin and have been shown in some cases to require β2- glycoprotein I in order to bind to cardiolipin [2]. Symptoms of this disorder include vascular thrombosis without inflammation in the vessel wall, premature birth, spontaneous abortion, and death in a morphologically normal fetus at or beyond the 10th week of gestation [1]. APS has a strong correlation to systemic lupus erythematosus (SLE) with studies showing that 30% of patients with SLE will develop APS; however, APS can still be found in patients without SLE at a low frequency [3]

Ethical Considerations concerning Contacts by Counsel or Investigators with Present and Former Employees of an Opposing Party The Sixth Annual Symposium on Legal Malpractice and Professional Responsibility.

Present and former employees of an opposing party are often the best source of information concerning that party’s activities. Contact with these employees or their representatives may be considered ethical or unethical depending on the circumstances. Unfortunately, the rules governing such contacts are neither clear nor consistent. The first issue lawyers must resolve is whether the present and former employees are considered “represented” under Rule 4.2 of the Model Rules of Professional Conduct. If they are “unrepresented” for purposes of Rule 4.2, the issue then becomes whether Rule 4.3 applies. Of grave importance are the potential sanctions, which include the exclusion of unlawfully or unethically obtained evidence and lawyer disqualification. As a matter of law, the acceptable limits of contact with present and former employees of an opposing party vary according to jurisdiction. In both multi-state and federal litigation, some jurisdictions use the Model Rules of Professional Conduct, while others rely on the ABA Model Code. Federal litigants must also be aware of so-called national ethical standards. These “national standards” can involve the Model Code, Model Rules, the applicable state’s disciplinary rules, and even “the ethical rules announced in the national profession in the light of public interest.” Meaning, lawyers must consult several sets of laws and rules before choosing a course of action. Even though the knowledge held by present and former employees can be a goldmine, attempting to obtain that knowledge outside the formal discovery process can be dangerous. Thus, the result of such risky behavior can potentially be more of a minefield than a goldmine. Attorneys must keep the above-mentioned rules and court decisions in mind when participating in such a treacherous game. And although the payoff may be great, the consequences could be disastrous

Ethical Considerations concerning Contacts by Counsel or Investigators with Present and Former Employees of an Opposing Party The Sixth Annual Symposium on Legal Malpractice and Professional Responsibility.

Present and former employees of an opposing party are often the best source of information concerning that party’s activities. Contact with these employees or their representatives may be considered ethical or unethical depending on the circumstances. Unfortunately, the rules governing such contacts are neither clear nor consistent. The first issue lawyers must resolve is whether the present and former employees are considered “represented” under Rule 4.2 of the Model Rules of Professional Conduct. If they are “unrepresented” for purposes of Rule 4.2, the issue then becomes whether Rule 4.3 applies. Of grave importance are the potential sanctions, which include the exclusion of unlawfully or unethically obtained evidence and lawyer disqualification. As a matter of law, the acceptable limits of contact with present and former employees of an opposing party vary according to jurisdiction. In both multi-state and federal litigation, some jurisdictions use the Model Rules of Professional Conduct, while others rely on the ABA Model Code. Federal litigants must also be aware of so-called national ethical standards. These “national standards” can involve the Model Code, Model Rules, the applicable state’s disciplinary rules, and even “the ethical rules announced in the national profession in the light of public interest.” Meaning, lawyers must consult several sets of laws and rules before choosing a course of action. Even though the knowledge held by present and former employees can be a goldmine, attempting to obtain that knowledge outside the formal discovery process can be dangerous. Thus, the result of such risky behavior can potentially be more of a minefield than a goldmine. Attorneys must keep the above-mentioned rules and court decisions in mind when participating in such a treacherous game. And although the payoff may be great, the consequences could be disastrous

Automatic coronary calcium scoring in chest CT using a deep neural network in direct comparison with non-contrast cardiac CT:A validation study

Purpose: To evaluate deep-learning based calcium quantification on Chest CT scans compared with manual evaluation, and to enable interpretation in terms of the traditional Agatston score on dedicated Cardiac CT. Methods: Automated calcium quantification was performed using a combination of deep-learning convolution neural networks with a ResNet-architecture for image features and a fully connected neural network for spatial coordinate features. Calcifications were identified automatically, after which the algorithm automatically excluded all non-coronary calcifications using coronary probability maps and aortic segmentation. The algorithm was first trained on cardiac-CTs and refined on non-triggered chest-CTs. This study used on 95 patients (cohort 1), who underwent both dedicated calcium scoring and chest-CT acquisitions using the Agatston score as reference standard and 168 patients (cohort 2) who underwent chest-CT only using qualitative expert assessment for external validation. Results from the deep-learning model were compared to Agatston-scores(cardiac-CTs) and manually determined calcium volumes(chest-CTs) and risk classifications. Results: In cohort 1, the Agatston score and AI determined calcium volume shows high correlation with a correlation coefficient of 0.921(p < 0.001) and R-2 of 0.91. According to the Agatston categories, a total of 67(70 %) were correctly classified with a sensitivity of 91 % and specificity of 92 % in detecting presence of coronary calcifications. Manual determined calcium volume on chest-CT showed excellent correlation with the AI volumes with a correlation coefficient of 0.923(p < 0.001) and R-2 of 0.96, no significant difference was found (p = 0.247). According to qualitative risk classifications in cohort 2, 138(82 %) cases were correctly classified with a k-coefficient of 0.74, representing good agreement. All wrongly classified scans (30(18 %)) were attributed to an adjacent category. Conclusion: Artificial intelligence based calcium quantification on chest-CTs shows good correlation compared to reference standards. Fully automating this process may reduce evaluation time and potentially optimize clinical calcium scoring without additional acquisitions

Ubu Roi

Program for the nineth annual RISD Cabaret held in the Waterman Building commemorating the 100th anniversary of the premier of Alfred Jarry\u27s 1896 play, Ubu Roi . Graphic design: Matt DeSmith and Vivian Law.https://digitalcommons.risd.edu/liberalarts_cabaret_programs/1008/thumbnail.jp

Progression of coronary calcification in healthy postmenopausal women

BACKGROUND: Coronary artery calcium score incrementally improves coronary risk prediction beyond that provided by conventional risk factors. Limited information is available regarding rates of progression of coronary calcification in women, particularly those with baseline scores above zero. Further, determinants of progression of coronary artery calcification in women are not well understood. This study prospectively evaluated rates and determinants of progression of coronary artery calcium score in a group of healthy postmenopausal women. METHODS: We determined coronary calcium score by computed tomography and recorded demographic, lifestyle and health characteristics of 914 postmenopausal women, a subset of those enrolled in the Women's Health Initiative Observational Study. The 305 women with calcium score ≥10 Agatston units at baseline were invited for repeat scan. This analysis includes the 94 women who underwent second scans. RESULTS: Mean age of study participants was 65 ± 9 years (mean ± SD), body mass index was 26.1 ± 6.1 kg/m(2), and baseline calcium score was 162 ± 220 Agatston units. Mean interval between scans was 3.3 ± 0.7 years. A wide range of changes in coronary calcium score was observed, from -53 to +452 Agatston units/year. Women with lower scores at baseline had smaller annual increases in absolute calcium score. Coronary calcium scores increased 11, 31 and 79 Agatston units/year among women with baseline calcium score in the lowest, middle and highest tertiles. In multivariate analysis, age was not an independent predictor of absolute change in coronary calcium score. Hydroxymethylglutaryl coenzyme A reductase inhibitor (statin) use at baseline was a negative predictor (p = 0.015), whereas baseline calcium score was a strong, positive predictor (p < 0.0001) of progression of coronary calcification. CONCLUSION: Among postmenopausal women with coronary calcium score ≥ 10 Agatston units, rates of change of coronary calcium score varied widely. In multivariate analysis, statin use was a negative independent determinant, whereas baseline calcium score was a strong positive predictor of annual change in coronary calcium score

Genome-wide association study of non-alcoholic fatty liver and steatohepatitis in a histologically-characterised cohort

Correction: Volume: 74 Issue: 5 Pages: 1274-1275 DOI: 10.1016/j.jhep.2021.02.003 Correction: Volume78, Issue5 Page: 1085-1086 DOI: 10.1016/j.jhep.2023.02.028 Published MAY 2023Background and Aims Genetic factors associated with non-alcoholic fatty liver disease (NAFLD) remain incompletely understood. To date, most GWAS studies have adopted radiologically assessed hepatic triglyceride content as reference phenotype and so cannot address steatohepatitis or fibrosis. We describe a genome-wide association study (GWAS) encompassing the full spectrum of histologically characterized NAFLD. Methods The GWAS involved 1483 European NAFLD cases and 17781 genetically-matched population controls. A replication cohort of 559 NAFLD cases and 945 controls was genotyped to confirm signals showing genome-wide or close to genome-wide significance. Results Case-control analysis identified signals showing p-values ≤ 5 x 10-8 at four locations (chromosome (chr) 2 GCKR/C2ORF16; chr4 HSD17B13; chr19 TM6SF2; chr22 PNPLA3) together with two other signals with pPeer reviewe

The European NAFLD Registry: A real-world longitudinal cohort study of nonalcoholic fatty liver disease.

Non-Alcoholic Fatty Liver Disease (NAFLD), a progressive liver disease that is closely associated with obesity, type 2 diabetes, hypertension and dyslipidaemia, represents an increasing global public health challenge. There is significant variability in the disease course: the majority exhibit only fat accumulation in the liver but a significant minority develop a necroinflammatory form of the disease (non-alcoholic steatohepatitis, NASH) that may progress to cirrhosis and hepatocellular carcinoma. At present our understanding of pathogenesis, disease natural history and long-term outcomes remain incomplete. There is a need for large, well characterised patient cohorts that may be used to address these knowledge gaps and to support the development of better biomarkers and novel therapies. The European NAFLD Registry is an international, prospectively recruited observational cohort study that aims to establish a large, highly-phenotyped patient cohort and linked bioresource. Here we describe the infrastructure, data management and monitoring plans, and the standard operating procedures implemented to ensure the timely and systematic collection of high-quality data and samples. Already recruiting subjects at secondary/tertiary care centres across Europe, the Registry is supporting the European Union IMI2-funded LITMUS 'Liver Investigation: Testing Marker Utility in Steatohepatitis' consortium, which is a major international effort to robustly validate biomarkers that diagnose, risk stratify and/or monitor NAFLD progression and liver fibrosis stage. The European NAFLD Registry has the demonstrable capacity to support research and biomarker development at scale and pace