Automatic 3D bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach

Deep learning approaches have achieved state-of-the-art performance in cardiac magnetic resonance (CMR) image segmentation. However, most approaches have focused on learning image intensity features for segmentation, whereas the incorporation of anatomical shape priors has received less attention. In this paper, we combine a multi-task deep learning approach with atlas propagation to develop a shape-constrained bi-ventricular segmentation pipeline for short-axis CMR volumetric images. The pipeline first employs a fully convolutional network (FCN) that learns segmentation and landmark localisation tasks simultaneously. The architecture of the proposed FCN uses a 2.5D representation, thus combining the computational advantage of 2D FCNs networks and the capability of addressing 3D spatial consistency without compromising segmentation accuracy. Moreover, the refinement step is designed to explicitly enforce a shape constraint and improve segmentation quality. This step is effective for overcoming image artefacts (e.g. due to different breath-hold positions and large slice thickness), which preclude the creation of anatomically meaningful 3D cardiac shapes. The proposed pipeline is fully automated, due to network's ability to infer landmarks, which are then used downstream in the pipeline to initialise atlas propagation. We validate the pipeline on 1831 healthy subjects and 649 subjects with pulmonary hypertension. Extensive numerical experiments on the two datasets demonstrate that our proposed method is robust and capable of producing accurate, high-resolution and anatomically smooth bi-ventricular 3D models, despite the artefacts in input CMR volumes

Collective impact : operationalizing a framework to coordinate community services

The Maternal, Infant, and Early Childhood Home Visiting (MIECHV) program provides comprehensive early childhood services. Federal agencies emphasize coordination of stakeholders for systems-building. Designing a well-coordinated system is complex. We reviewed MIECHV’s literature and program documents to identify community-coordination infrastructure elements. We designed visual frameworks for each model to display infrastructure, components, and connections. In the independent point of entry model, families access services directly. In the coordinated point of entry model, a centralized intake and referral structure supports system coordination. In the collective impact model, relevant community stakeholders actively and collaboratively participate in service coordination. Visual frameworks allow stakeholders to align on process and infrastructure of their programs to facilitate planning activities, use these frameworks to identify whether the model under which they operate is ideal, and then evolve their infrastructure.Includes bibliographical references (page 8-9)

Using Process Mining to Assess the Fidelity of a Home Visiting Program

Background: The Maternal, Infant, and Early Childhood Home Visiting (MIECHV) program is a federal public health initiative which supports at-risk families through evidence-based programs and promising approaches for pregnant women, and childhood development for children aged 0 to 5. These public health program funding mechanisms commonly include process evaluation mandates. Purpose: The use of process mining was explored as a methodology to assess the fidelity of the MIECHV programs’ actual workflow to that of their intended models. Methods: Research Electronic Data Capture (REDCap) data files that were populated with program process data elements from the local implementing agencies were mined. The focus was on three main variables: participant identification, activity labels, and timestamps. These variables were imported into the Disco process-mining software. Disco was used to develop process maps to track process pathways and compare the actual workflow against the intended model. Results: Using process mining as a diagnostic tool, fidelity to the MIECHV process model was assessed, identifying a total of 262 different process variations. The 15 most frequent variations represent 60.7% of the total pool of process variations, 13 of which were deemed to have fidelity to the intended model. Analysis of the variations indicated that many activities in the intended process were skipped or implemented out of sequence. Implications: Process mining is a useful tool for organizations to visually display, track, understand, compare, and improve their workflow processes. This method should be considered by programs as complex as MIECHV to improve the data reporting and the identification of opportunities to strengthen programs

Visualizing Complex Adaptive Systems: A Case Study of the Missouri Maternal, Infant, and Early Childhood Home Visiting Program

Background: The Maternal, Infant, and Early Childhood Home Visiting (MIECHV) program was created by the 2010 Patient Protection and Affordable Care Act. MIECHV provides comprehensive services to at-risk families through evidence-based home visiting programs. Purpose: The following question is addressed: Does the Missouri MIECHV system meet the definition of a complex adaptive system (CAS)? Methods: A systematic review was conducted of documents related to MIECHV programs (federal, state, and local levels), and to affiliated programs with a home visiting and early childhood (aged birth to 5 years) scope. The organizations’ fit was identified for the scope of early childhood home visiting programs, and then its relationship extracted to MIECHV and its affiliates. Results: MIECHV meets the definition of a CAS, being dynamic, massively entangled, scale independent, transformative, and emergent. Over 250 organizations were identified; 19 federal and 79 state organizations; 24 nonprofits at the federal level, 31 at the state; over 150 community-level agencies; and 13 home visiting models implemented in Missouri. Implications: A considerable amount of organizational complexity exists within the MIECHV system and among its affiliates with a home visiting and early childhood scope. The complexity of the system challenges its potential for effective and efficient implementation, coordination, sustainability, and evaluation, and increases the potential for redundancy, overlap, and fragmentation. Evaluating a CAS requires acknowledgement of its complexity, beyond traditional approaches to evaluation. Creating visualization tools of federal, state, and local stakeholders and their relationships is a practical approach for aligning, organizing, and communicating the work flow

Deep nested level sets: Fully automated segmentation of cardiac MR images in patients with pulmonary hypertension

In this paper we introduce a novel and accurate optimisation method for segmentation of cardiac MR (CMR) images in patients with pulmonary hypertension (PH). The proposed method explicitly takes into account the image features learned from a deep neural network. To this end, we estimate simultaneous probability maps over region and edge locations in CMR images using a fully convolutional network. Due to the distinct morphology of the heart in patients with PH, these probability maps can then be incorporated in a single nested level set optimisation framework to achieve multi-region segmentation with high efficiency. The proposed method uses an automatic way for level set initialisation and thus the whole optimisation is fully automated. We demonstrate that the proposed deep nested level set (DNLS) method outperforms existing state-of-the-art methods for CMR segmentation in PH patients

Oxygen impurities in NiAl: Relaxation effects

We have used a full-potential linear muffin-tin orbital method to calculate the effects of oxygen impurities on the electronic structure of NiAl. Using the supercell method with a 16-atom supercell we have investigated the cases where an oxygen atom is substitutionally placed at either a nickel or an aluminum site. Full relaxation of the atoms within the supercell was allowed. We found that oxygen prefers to occupy a nickel site over an aluminum site with a site selection energy of 138 mRy (21,370 K). An oxygen atom placed at an aluminum site is found to cause a substantial relaxation of its nickel neighbors away from it. In contrast, this steric repulsion is hardly present when the oxygen atom occupies the nickel site and is surrounded by aluminum neighbors. We comment on the possible relation of this effect to the pesting degradation phenomenon (essentially spontaneous disintegration in air) in nickel aluminides.Comment: To appear in Phys. Rev. B (Aug. 15, 2001

Bis­(μ-pyridine-2,3-dicarboxyl­ato)bis­[aqua­(3-carb­oxy­pyridine-2-carboxyl­ato)indium(III)] tetra­hydrate

In the binuclear centrosymmetric title compound, [In2(C7H3NO4)2(C7H4NO4)2(H2O)2]·4H2O, which contains both pyridine-2,3-dicarboxyl­ate and 3-carb­oxy­pyridine-2-carboxyl­ate ligands, the InIII atom is six-coordinated in a distorted octa­hedral geometry. One pyridine ligand is N,O-chelated while the other is N,O-chelated and at the same time bridging to the other via the second carboxyl group. In the crystal, an extensive O—H⋯O hydrogen-bonding network, involving the coordinated and lattice water mol­ecules and the carboxyl groups of the ligands, together with C—H⋯O and π–π inter­actions [centroid–centroid distance = 3.793 (1) Å], leads to the formation of a three-dimensional structure

LIRA: Lifelong Image Restoration from Unknown Blended Distortions

Most existing image restoration networks are designed in a disposable way and catastrophically forget previously learned distortions when trained on a new distortion removal task. To alleviate this problem, we raise the novel lifelong image restoration problem for blended distortions. We first design a base fork-join model in which multiple pre-trained expert models specializing in individual distortion removal task work cooperatively and adaptively to handle blended distortions. When the input is degraded by a new distortion, inspired by adult neurogenesis in human memory system, we develop a neural growing strategy where the previously trained model can incorporate a new expert branch and continually accumulate new knowledge without interfering with learned knowledge. Experimental results show that the proposed approach can not only achieve state-of-the-art performance on blended distortions removal tasks in both PSNR/SSIM metrics, but also maintain old expertise while learning new restoration tasks.Comment: ECCV2020 accepte

Onset of magnetism in B2 transition metals aluminides

Ab initio calculation results for the electronic structure of disordered bcc Fe(x)Al(1-x) (0.4<x<0.75), Co(x)Al(1-x) and Ni(x)Al(1-x) (x=0.4; 0.5; 0.6) alloys near the 1:1 stoichiometry, as well as of the ordered B2 (FeAl, CoAl, NiAl) phases with point defects are presented. The calculations were performed using the coherent potential approximation within the Korringa-Kohn-Rostoker method (KKR-CPA) for the disordered case and the tight-binding linear muffin-tin orbital (TB-LMTO) method for the intermetallic compounds. We studied in particular the onset of magnetism in Fe-Al and Co-Al systems as a function of the defect structure. We found the appearance of large local magnetic moments associated with the transition metal (TM) antisite defect in FeAl and CoAl compounds, in agreement with the experimental findings. Moreover, we found that any vacancies on both sublattices enhance the magnetic moments via reducing the charge transfer to a TM atom. Disordered Fe-Al alloys are ferromagnetically ordered for the whole range of composition studied, whereas Co-Al becomes magnetic only for Co concentration >0.5.Comment: 11 pages with 9 embedded postscript figures, to be published in Phys.Rev.