Diffusion Models for Memory-efficient Processing of 3D Medical Images

Denoising diffusion models have recently achieved state-of-the-art performance in many image-generation tasks. They do, however, require a large amount of computational resources. This limits their application to medical tasks, where we often deal with large 3D volumes, like high-resolution three-dimensional data. In this work, we present a number of different ways to reduce the resource consumption for 3D diffusion models and apply them to a dataset of 3D images. The main contribution of this paper is the memory-efficient patch-based diffusion model \textit{PatchDDM}, which can be applied to the total volume during inference while the training is performed only on patches. While the proposed diffusion model can be applied to any image generation tasks, we evaluate the method on the tumor segmentation task of the BraTS2020 dataset and demonstrate that we can generate meaningful three-dimensional segmentations.Comment: Accepted at MIDL 202

Point Cloud Diffusion Models for Automatic Implant Generation

Advances in 3D printing of biocompatible materials make patient-specific implants increasingly popular. The design of these implants is, however, still a tedious and largely manual process. Existing approaches to automate implant generation are mainly based on 3D U-Net architectures on downsampled or patch-wise data, which can result in a loss of detail or contextual information. Following the recent success of Diffusion Probabilistic Models, we propose a novel approach for implant generation based on a combination of 3D point cloud diffusion models and voxelization networks. Due to the stochastic sampling process in our diffusion model, we can propose an ensemble of different implants per defect, from which the physicians can choose the most suitable one. We evaluate our method on the SkullBreak and SkullFix datasets, generating high-quality implants and achieving competitive evaluation scores

What is it like for a middle manager to take safety into account? Practices and challenges

Aviation today is seen as a very safe industry, yet recent accidents have shown that vulnerabilities still exist. The literature has often drawn attention to the role played by top managers/CEO in running their businesses profitably, and at the same time keeping them safe from threats. Research has also investigated the way people at the sharp-end of organisations are ‘mindful’ of the possible threats that can occur in their day-to-day activities, and how they can anticipate (most of) them. But what about the role played by middle managers in ensuring safety in every organisational operation? Even if researchers now agree that middle managers’ actions are a valuable asset for organisations and central to pursuing key organisational outcomes, very little is known about how middle managers take safety into account in their daily operations, and the challenges they face. This paper reports on the safety-related practices and challenges of middle managers of the civil aviation industry. Within the Future Sky Safety project, over a two-year research activity, 48 middle managers from a range of aviation organisations agreed to talk about the strategies and actions they put in place on a routine basis, to embed safety in the daily operations. Methodologically, semi-structured interviews were conducted and the qualitative content analysis (QCA) method was used to make sense of the raw material, through a data-driven coding frame. The findings of this research suggest that the practices middle managers identify as central in relation to their role in the management of safety can be grouped into three high-level categories: (1) making decisions, (2) influencing key stakeholders to get the job done, and (3) managing information. This research adds knowledge in relation to the middle managers’ role in the management of safety, in particular shedding light on the competency that middle managers from the civil aviation industry rely on to get the job done when it comes to contributing to safety

Learn to Ignore: Domain Adaptation for Multi-Site MRI Analysis

The limited availability of large image datasets, mainly due to data privacy and differences in acquisition protocols or hardware, is a significant issue in the development of accurate and generalizable machine learning methods in medicine. This is especially the case for Magnetic Resonance (MR) images, where different MR scanners introduce a bias that limits the performance of a machine learning model. We present a novel method that learns to ignore the scanner-related features present in MR images, by introducing specific additional constraints on the latent space. We focus on a real-world classification scenario, where only a small dataset provides images of all classes. Our method \textit{Learn to Ignore (L2I)} outperforms state-of-the-art domain adaptation methods on a multi-site MR dataset for a classification task between multiple sclerosis patients and healthy controls

ABINIT: Overview and focus on selected capabilities

Paper published as part of the special topic on Electronic Structure SoftwareABINIT is probably the first electronic-structure package to have been released under an open-source license about 20 years ago. It implements density functional theory, density-functional perturbation theory (DFPT), many-body perturbation theory (GW approximation and Bethe–Salpeter equation), and more specific or advanced formalisms, such as dynamical mean-field theory (DMFT) and the “temperaturedependent effective potential” approach for anharmonic effects. Relying on planewaves for the representation of wavefunctions, density, and other space-dependent quantities, with pseudopotentials or projector-augmented waves (PAWs), it is well suited for the study of periodic materials, although nanostructures and molecules can be treated with the supercell technique. The present article starts with a brief description of the project, a summary of the theories upon which ABINIT relies, and a list of the associated capabilities. It then focuses on selected capabilities that might not be present in the majority of electronic structure packages either among planewave codes or, in general, treatment of strongly correlated materials using DMFT; materials under finite electric fields; properties at nuclei (electric field gradient, Mössbauer shifts, and orbital magnetization); positron annihilation; Raman intensities and electro-optic effect; and DFPT calculations of response to strain perturbation (elastic constants and piezoelectricity), spatial dispersion (flexoelectricity), electronic mobility, temperature dependence of the gap, and spin-magnetic-field perturbation. The ABINIT DFPT implementation is very general, including systems with van der Waals interaction or with noncollinear magnetism. Community projects are also described: generation of pseudopotential and PAW datasets, high-throughput calculations (databases of phonon band structure, second-harmonic generation, and GW computations of bandgaps), and the library LIBPAW. ABINIT has strong links with many other software projects that are briefly mentioned.This work (A.H.R.) was supported by the DMREF-NSF Grant No. 1434897, National Science Foundation OAC-1740111, and U.S. Department of Energy DE-SC0016176 and DE-SC0019491 projects. N.A.P. and M.J.V. gratefully acknowledge funding from the Belgian Fonds National de la Recherche Scientifique (FNRS) under Grant No. PDR T.1077.15-1/7. M.J.V. also acknowledges a sabbatical “OUT” grant at ICN2 Barcelona as well as ULiège and the Communauté Française de Belgique (Grant No. ARC AIMED G.A. 15/19-09). X.G. and M.J.V. acknowledge funding from the FNRS under Grant No. T.0103.19-ALPS. X.G. and G.-M. R. acknowledge support from the Communauté française de Belgique through the SURFASCOPE Project (No. ARC 19/24-057). X.G. acknowledges the hospitality of the CEA DAM-DIF during the year 2017. G.H. acknowledges support from the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Materials Sciences and Engineering Division under Contract No. DE-AC02-05-CH11231 (Materials Project Program No. KC23MP). The Belgian authors acknowledge computational resources from supercomputing facilities of the University of Liège, the Consortium des Equipements de Calcul Intensif (Grant No. FRS-FNRS G.A. 2.5020.11), and Zenobe/CENAERO funded by the Walloon Region under Grant No. G.A. 1117545. M.C. and O.G. acknowledge support from the Fonds de Recherche du Québec Nature et Technologie (FRQ-NT), Canada, and the Natural Sciences and Engineering Research Council of Canada (NSERC) under Grant No. RGPIN-2016-06666. The implementation of the libpaw library (M.T., T.R., and D.C.) was supported by the ANR NEWCASTLE project (Grant No. ANR-2010-COSI-005-01) of the French National Research Agency. M.R. and M.S. acknowledge funding from Ministerio de Economia, Industria y Competitividad (MINECO-Spain) (Grants Nos. MAT2016-77100-C2-2-P and SEV-2015-0496) and Generalitat de Catalunya (Grant No. 2017 SGR1506). This work has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation program (Grant Agreement No. 724529). P.G. acknowledges support from FNRS Belgium through PDR (Grant No. HiT4FiT), ULiège and the Communauté française de Belgique through the ARC project AIMED, the EU and FNRS through M.ERA.NET project SIOX, and the European Funds for Regional Developments (FEDER) and the Walloon Region in the framework of the operational program “Wallonie-2020.EU” through the project Multifunctional thin films/LoCoTED. The Flatiron Institute is a division of the Simons Foundation. A large part of the data presented in this paper is available directly from the Abinit Web page www.abinit.org. Any other data not appearing in this web page can be provided by the corresponding author upon reasonable request.Peer reviewe

Individual and environmental dimensions influencing the middle managers’ contribution to safety: the emergence of a ‘safety-related universe’

Even if enhancing safety remains a key challenge in civil aviation, safety research has mainly focussed on front line operators, senior managers and safety managers. This paper aims to shed light on the middle managers, more specifically on the overall context that influences their contribution to safety in their daily operations, and the challenges they face. Over a two-year period, extensive field research was undertaken involving six sector organisations, and overall forty-three middle managers. Interviews were conducted to capture the participants’ views and experiences in embedding safety-related aspects in their daily activities and actions. A data-driven approach was used to support the emergence of recurring codes/themes that could describe the conditions the middle managers face in their organisations, and explain how the specific factors interplay and impact on their action. NVivo, with its tools, supported the entire research process (data storage, codification, both qualitatively and quantitatively descriptive analysis at code level, and explanatory analysis at codes-relationship level). Our results suggest a number of conditions/dimensions (internal and external to the organisation) that interplay to either support or hinder the middle managers’ contribution to safety. This contribution is translated in practices (i.e. strategies and actions that the middle managers apply to support safety-related outcomes) modulated by a certain ‘mindset’ that each middle manager possesses as a result of past experiences, background education and view on the role of a manager. These aspects are interrelated not only with the middle managers’ safety-related practices directly, but also with one another. To understand management contribution to safety, and what may promote or hinder it, one should adopt a systemic view combining individual, organisational, external aspects and their interrelations