Network topological determinants of pathogen spread

How do we best constrain social interactions to decrease transmission of communicable diseases? Indiscriminate suppression is unsustainable long term and presupposes that all interactions carry equal importance. Instead, transmission within a social network has been shown to be determined by its topology. In this paper, we deploy simulations to understand and quantify the impact on disease transmission of a set of topological network features, building a dataset of 9000 interaction graphs using generators of different types of synthetic social networks. Independently of the topology of the network, we maintain constant the total volume of social interactions in our simulations, to show how even with the same social contact some network structures are more or less resilient to the spread. We find a suitable intervention to be specific suppression of unfamiliar and casual interactions that contribute to the network’s global efficiency. This is, pathogen spread is significantly reduced by limiting specific kinds of contact rather than their global number. Our numerical studies might inspire further investigation in connection to public health, as an integrative framework to craft and evaluate social interventions in communicable diseases with different social graphs or as a highlight of network metrics that should be captured in social studies

Content aware multi-focus image fusion for high-magnification blood film microscopy

Automated digital high-magnification optical microscopy is key to accelerating biology research and improving pathology clinical pathways. High magnification objectives with large numerical apertures are usually preferred to resolve the fine structural details of biological samples, but they have a very limited depth-of-field. Depending on the thickness of the sample, analysis of specimens typically requires the acquisition of multiple images at different focal planes for each field-of-view, followed by the fusion of these planes into an extended depth-of-field image. This translates into low scanning speeds, increased storage space, and processing time not suitable for high-throughput clinical use. We introduce a novel content-aware multi-focus image fusion approach based on deep learning which extends the depth-of-field of high magnification objectives effectively. We demonstrate the method with three examples, showing that highly accurate, detailed, extended depth of field images can be obtained at a lower axial sampling rate, using 2-fold fewer focal planes than normally required

Detection of acute promyelocytic leukemia in peripheral blood and bone marrow with annotation-free deep learning

While optical microscopy inspection of blood films and bone marrow aspirates by a hematologist is a crucial step in establishing diagnosis of acute leukemia, especially in low-resource settings where other diagnostic modalities are not available, the task remains time-consuming and prone to human inconsistencies. This has an impact especially in cases of Acute Promyelocytic Leukemia (APL) that require urgent treatment. Integration of automated computational hematopathology into clinical workflows can improve the throughput of these services and reduce cognitive human error. However, a major bottleneck in deploying such systems is a lack of sufficient cell morphological object-labels annotations to train deep learning models. We overcome this by leveraging patient diagnostic labels to train weakly-supervised models that detect different types of acute leukemia. We introduce a deep learning approach, Multiple Instance Learning for Leukocyte Identification (MILLIE), able to perform automated reliable analysis of blood films with minimal supervision. Without being trained to classify individual cells, MILLIE differentiates between acute lymphoblastic and myeloblastic leukemia in blood films. More importantly, MILLIE detects APL in blood films (AUC 0.94 ± 0.04) and in bone marrow aspirates (AUC 0.99 ± 0.01). MILLIE is a viable solution to augment the throughput of clinical pathways that require assessment of blood film microscopy

Optical mesoscopy, machine learning, and computational microscopy enable high information content diagnostic imaging of blood films

Automated image-based assessment of blood films has tremendous potential to support clinical haematology within overstretched healthcare systems. To achieve this, efficient and reliable digital capture of the rich diagnostic information contained within a blood film is a critical first step. However, this is often challenging, and in many cases entirely unfeasible, with the microscopes typically used in haematology due to the fundamental trade-off between magnification and spatial resolution. To address this, we investigated three state-of-the-art approaches to microscopic imaging of blood films which leverage recent advances in optical and computational imaging and analysis to increase the information capture capacity of the optical microscope: optical mesoscopy, which uses a giant microscope objective (Mesolens) to enable high-resolution imaging at low magnification; Fourier ptychographic microscopy, a computational imaging method which relies on oblique illumination with a series of LEDs to capture high-resolution information; and deep neural networks which can be trained to increase the quality of low magnification, low resolution images. We compare and contrast the performance of these techniques for blood film imaging for the exemplar case of Giemsa-stained peripheral blood smears. Using computational image analysis and shape-based object classification, we demonstrate their use for automated analysis of red blood cell morphology and visualization and detection of small blood-borne parasites such as the malarial parasite Plasmodium falciparum. Our results demonstrate that these new methods greatly increase the information capturing capacity of the light microscope, with transformative potential for haematology and more generally across digital pathology

Représentation continue des organes déformables basée sur des maillages tétraédriques : application à la dosimétrie et l'imagerie pour l'hadronthérapie

Dans le cadre du projet européen ENVISION (2010-2014) et en collaboration avec l'équipe CAS-PHABIO de l'IPNL, cette thèse constitue une contribution méthodologique et technique dans le domaine de la dosimétrie et de l'imagerie de contrôle par émission des positons (TEP) pour les organes en mouvement. Les méthodes actuelles utilisent le recalage déformable d'images CT pour estimer le mouvement des organes internes. Le recalage déformable permet d'estimer le déplacement de chaque voxel d'une image à une autre. La dose radio-thérapeutique ainsi que l'activité TEP sont accumulées sur des voxels. Ces approches ont des difficultés quand il s'agit de prendre en compte la variation de densité à l'intérieur des organes et l'aspect non-répétitif du mouvement respiratoire. Les travaux antérieurs de l'équipe ont permis de développer un premier modèle biomécanique complet du système respiratoire qui, corrélé avec des signaux externes, pourrait prendre en compte la variabilité du mouvement respiratoire. Cette thèse présente une approche qui permet d'intégrer un tel modèle biomécanique dans un système de planification de traitement pour l'hadronthérapie. Dans cette thèse, nous avons choisi d'investiguer de près l'utilisation des maillages tétraédriques déformables dans la dosimétrie et la reconstruction d'images TEP afin d'estimer les avantages et inconvénients de ce type de géométrie. En conclusion, notre approche peut être utilisée avec n'importe quel modèle de déformation basé sur une géométrie tétraédrique et dont le mouvement est décrit par le déplacement des nœuds des maillages et donc contrairement aux méthodes basés images, notre approche n'est pas nécessairement dépendante de l'existence des images internes à tout moment. Dans le futur, les méthodes développées dans cette thèse pourraient être utilisées avec un modèle biomécanique complet du système respiratoire afin de quantifier, par exemple, les effets de la variabilité de la respiration sur le dépôt de doseRespiratory-induced organ motion is a technical challenge to nuclear imaging and to charged particle therapy dose calculations for lung cancer treatment in particular. Internal organ tissue displacements and deformations induced by breathing need to be taken into account when calculating Monte Carlo dose distributions as well as when performing tomographic reconstructions for PET imaging. Current techniques based on Deformable Image Registration (DIR) cannot fully take into account the density variations of the tissues nor the fact that respiratory motion is not reproducible. As part of the ENVISION (2010-2014) European project, in collaboration with the CAS-PHABIO team from IPNL (the Nuclear Physics Institute from Lyon), this PhD project presents a methodological contribution to physical dose calculations and PET-based treatment verification for hadron therapy in the case of moving tumours. Contrary to DIR-based methods where motion is described by relative voxel displacement, each organ is represented as a deformable grid of tetrahedra where internal motion is described by mesh vertex transformations calculated using continuum mechanics. First, this PhD project proposes a new method to calculate four dimensional dose distribution over tetrahedral meshes, which are deformed using biomechanical modeling based on Finite Element Analysis (FEA). The second part of the PhD is focused on motion compensation for PET image reconstruction using deformable tetrahedral meshe

Représentation continue des organes déformables basée sur des maillages tétraédriques : application à la dosimétrie et l'imagerie pour l'hadronthérapie

Dans le cadre du projet européen ENVISION (2010-2014) et en collaboration avec l'équipe CAS-PHABIO de l'IPNL, cette thèse constitue une contribution méthodologique et technique dans le domaine de la dosimétrie et de l'imagerie de contrôle par émission des positons (TEP) pour les organes en mouvement. Les méthodes actuelles utilisent le recalage déformable d'images CT pour estimer le mouvement des organes internes. Le recalage déformable permet d'estimer le déplacement de chaque voxel d'une image à une autre. La dose radio-thérapeutique ainsi que l'activité TEP sont accumulées sur des voxels. Ces approches ont des difficultés quand il s'agit de prendre en compte la variation de densité à l'intérieur des organes et l'aspect non-répétitif du mouvement respiratoire. Les travaux antérieurs de l'équipe ont permis de développer un premier modèle biomécanique complet du système respiratoire qui, corrélé avec des signaux externes, pourrait prendre en compte la variabilité du mouvement respiratoire. Cette thèse présente une approche qui permet d'intégrer un tel modèle biomécanique dans un système de planification de traitement pour l'hadronthérapie. Dans cette thèse, nous avons choisi d'investiguer de près l'utilisation des maillages tétraédriques déformables dans la dosimétrie et la reconstruction d'images TEP afin d'estimer les avantages et inconvénients de ce type de géométrie. En conclusion, notre approche peut être utilisée avec n'importe quel modèle de déformation basé sur une géométrie tétraédrique et dont le mouvement est décrit par le déplacement des nœuds des maillages et donc contrairement aux méthodes basés images, notre approche n'est pas nécessairement dépendante de l'existence des images internes à tout moment. Dans le futur, les méthodes développées dans cette thèse pourraient être utilisées avec un modèle biomécanique complet du système respiratoire afin de quantifier, par exemple, les effets de la variabilité de la respiration sur le dépôt de doseRespiratory-induced organ motion is a technical challenge to nuclear imaging and to charged particle therapy dose calculations for lung cancer treatment in particular. Internal organ tissue displacements and deformations induced by breathing need to be taken into account when calculating Monte Carlo dose distributions as well as when performing tomographic reconstructions for PET imaging. Current techniques based on Deformable Image Registration (DIR) cannot fully take into account the density variations of the tissues nor the fact that respiratory motion is not reproducible. As part of the ENVISION (2010-2014) European project, in collaboration with the CAS-PHABIO team from IPNL (the Nuclear Physics Institute from Lyon), this PhD project presents a methodological contribution to physical dose calculations and PET-based treatment verification for hadron therapy in the case of moving tumours. Contrary to DIR-based methods where motion is described by relative voxel displacement, each organ is represented as a deformable grid of tetrahedra where internal motion is described by mesh vertex transformations calculated using continuum mechanics. First, this PhD project proposes a new method to calculate four dimensional dose distribution over tetrahedral meshes, which are deformed using biomechanical modeling based on Finite Element Analysis (FEA). The second part of the PhD is focused on motion compensation for PET image reconstruction using deformable tetrahedral meshe

Motion compensation for PET image reconstruction using deformable tetrahedral meshes

International audienceRespiratory-induced organ motion is a technical challenge to PET imaging.This motion induces displacements and deformation of the organs tissues, which need to be taken into account when reconstructing the spatial radiation activity. Classical image-based methods that describe motion using Deformable Image Registration (DIR) algorithms cannot fully take into account the non-reproducibility of the respiratory internal organ motion nor the tissue volume variations that occur during breathing. In order to overcome these limitations, various biomechanical models of the respiratory system have been developed in the past decade as an alternative to DIRapproaches. In this paper, we describe a new method of correcting motion artefacts in PET imagereconstruction adapted to motion estimation models such as those based onthe finite element method (FEM). In contrast with the DIR-based approaches, the radiation activity was reconstructed on deforming tetrahedral meshes. For this, we have re- formulated the tomographic reconstruction problem by introducing atime-dependent system matrix based calculated using tetrahedral meshes instead of voxelized images.The MLEM algorithm was chosen as the reconstruction method. The simulationsperformed in this study show that the motion compensated reconstruction based on tetrahedral deformable meshes has the capability to correct motion artefacts. Results demonstrate that, in the case of complex deformations, when large volume variations occur, the developed tetrahedral based method is more appropriate than the classical DIR-based one. This method can be used, together with biomechanical models controlled by external surrogates, to correct motion artefacts in PET images and thus reducing the need for additional internal imaging during the acquisition

4D POSITRON EMISSION TOMOGRAPHY IMAGE RECONSTRUCTION BASED ON BIOMECHANICAL RESPIRATORY MOTION

International audienceRespiratory-induced organ motion is a technical challenge tonuclear imaging and to particle therapy dose calculations forlung cancer treatment in particular. Internal organ tissue dis-placements and deformations induced by breathing need to betaken into account when calculating Monte Carlo dose distri-butions or when performing tomographic reconstructions forPET imaging. This paper proposes a method to reconstructPET activities over tetrahedral meshes which are deformedbased on biomechanical patient specific model of the respi-ratory system to tackle the non reproductibility of the breath-ing. We also describe the adaptation of the popular List-ModeMaximum Likelihood Estimation (LM-MLEM) reconstruc-tion algorithm to motion estimation model using the finite el-ement method (FEM). Our simulations demonstrate the accu-racy of the proposed 4D LM-MLEM reconstruction algorithmbased on biomechanical model and its capability to correctmotion artifacts due to the breathing