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Background: Computational advances in artificial intelligence have led to the recent emergence of U-Net convolutional neural networks (CNNs) applied to medical imaging. Our objectives were to assess the progression of fibrotic interstitial lung disease (ILD) using routine CT scans processed by a U-Net CNN developed by our research team, and to identify a progression threshold indicative of poor prognosis., Methods: CT scans and clinical history of 32 patients with idiopathic fibrotic ILDs were retrospectively reviewed. Successive CT scans were processed by the U-Net CNN and ILD quantification was obtained. Correlation between ILD and FVC changes was assessed. ROC curve was used to define a threshold of ILD progression rate (PR) to predict poor prognostic (mortality or lung transplantation). The PR threshold was used to compare the cohort survival with Kaplan Mayer curves and log-rank test., Results: The follow-up was 3.8 ± 1.5 years encompassing 105 CT scans, with 3.3 ± 1.1 CT scans per patient. A significant correlation between ILD and FVC changes was obtained (p = 0.004, ρ = -0.30 [95% CI: -0.16 to -0.45]). Sixteen patients (50%) experienced unfavorable outcome including 13 deaths and 3 lung transplantations. ROC curve analysis showed an aera under curve of 0.83 (p < 0.001), with an optimal cut-off PR value of 4%/year. Patients exhibiting a PR ≥ 4%/year during the first two years had a poorer prognosis (p = 0.001)., Conclusions: Applying a U-Net CNN to routine CT scan allowed identifying patients with a rapid progression and unfavorable outcome., Competing Interests: Declaration of Competing Interest XG, SR, CF, MB, DIBI, JFB, DV, JCj, JMN, PYB have no conflicts of interest. MPD received fees (presentations or participation in expert groups) from Boehringer-Ingelheim. HN received fees from Roche/Genentech, Boehringer-Ingelheim, Galapagos., (Copyright © 2023 SPLF and Elsevier Masson SAS. All rights reserved.)

Computerised processing of images from routine noncontrast HRCT could be an efficient, costless and safe tool to investigate the vascular remodelling that occurs in the months after COVID-19 in a large number of patients https://bit.ly/3qAQZDW., Competing Interests: Conflict of interest: The authors have no personal conflicts of interest to disclose., (Copyright ©The authors 2023.)

Interstitial lung diseases, such as idiopathic pulmonary fibrosis (IPF) or post-COVID-19 pulmonary fibrosis, are progressive and severe diseases characterized by an irreversible scarring of interstitial tissues that affects lung function. Despite many efforts, these diseases remain poorly understood and poorly treated. In this paper, we propose an automated method for the estimation of personalized regional lung compliances based on a poromechanical model of the lung. The model is personalized by integrating routine clinical imaging data - namely computed tomography images taken at two breathing levels in order to reproduce the breathing kinematic-notably through an inverse problem with fully personalized boundary conditions that is solved to estimate patient-specific regional lung compliances. A new parametrization of the inverse problem is introduced in this paper, based on the combined estimation of a personalized breathing pressure in addition to material parameters, improving the robustness and consistency of estimation results. The method is applied to three IPF patients and one post-COVID-19 patient. This personalized model could help better understand the role of mechanics in pulmonary remodeling due to fibrosis; moreover, patient-specific regional lung compliances could be used as an objective and quantitative biomarker for improved diagnosis and treatment follow up for various interstitial lung diseases., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Cell segmentation is a key step for a wide variety of biological investigations, especially in the context of muscle science. Currently, automated methods still struggle to perform skeletal muscle fiber quantification on Hematoxylin-Eosin (HE) stained histopathological whole slide images due to low contrast. On the other hand, the Deep Learning algorithm Cellpose offers new perspectives considering its increasing adoption for segmentation of a wide range of cells. Combining two open-source tools, Cellpose and QuPath, we developed MyoSOTHES, an automated Myofibers Segmentation wOrkflow Tuned for HE Staining. MyoSOTHES enables solving segmentation inconsistencies encountered by default Cellpose model in presence of large range size cells and provides information related to muscle Feret's diameter distribution and Centrally Nucleated Fibers, thus depicting muscle health and treatment effects. MyoSOTHES achieves high quality segmentation compared to baseline workflow with a detection F1-score increasing from 0.801 to 0.919 and a Root Mean Square Error (RMSE) on diameter improved by 31%. MyoSOTHES was validated on an animal study featuring gene transfer in [Formula: see text]-Sarcoglycanopathy, for which dose-response effect is visible and conclusions drawn are consistent with those previously published. MyoSOTHES thus paves the way for wide quantification of HE stained muscle sections and retrospective analysis of HE labeled slices used in laboratories for decades., (© 2022. The Author(s).)

Pulmonary function is tightly linked to the lung mechanical behavior, especially large deformation during breathing. Interstitial lung diseases, such as idiopathic pulmonary fibrosis (IPF), have an impact on the pulmonary mechanics and consequently alter lung function. However, IPF remains poorly understood, poorly diagnosed, and poorly treated. Currently, the mechanical impact of such diseases is assessed by pressure-volume curves, giving only global information. We developed a poromechanical model of the lung that can be personalized to a patient based on routine clinical data. The personalization pipeline uses clinical data, mainly computed tomography (CT) images at two time steps and involves the formulation of an inverse problem to estimate regional compliances. The estimation problem can be formulated both in terms of "effective", i.e., without considering the mixture porosity, or "rescaled," i.e., where the first-order effect of the porosity has been taken into account, compliances. Regional compliances are estimated for one control subject and three IPF patients, allowing to quantify the IPF-induced tissue stiffening. This personalized model could be used in the clinic as an objective and quantitative tool for IPF diagnosis., (Copyright © 2022 by ASME.)

Objectives: To determine the characteristics and outcomes of patients prone-positioned during extracorporeal membrane oxygenation for severe acute respiratory distress syndrome and lung CT pattern associated with improved respiratory system static compliance after that intervention., Design: Retrospective, single-center study over 8 years., Settings: Twenty-six bed ICU in a tertiary center., Measurements and Main Results: A propensity score-matched analysis compared patients with prone-positioning during extracorporeal membrane oxygenation and those without. An increase of the static compliance greater than or equal to 3 mL/cm H2O after 16 hours of prone-positioning defined prone-positioning responders. The primary outcome was the time to successful extracorporeal membrane oxygenation weaning within 90 days of postextracorporeal membrane oxygenation start, with death as a competing risk. Among 298 venovenous extracorporeal membrane oxygenation-treated adults with severe acute respiratory distress syndrome, 64 were prone-positioning extracorporeal membrane oxygenation. Although both propensity score-matched groups had similar extracorporeal membrane oxygenation durations, prone-positioning extracorporeal membrane oxygenation patients' 90-day probability of being weaned-off extracorporeal membrane oxygenation and alive was higher (0.75 vs 0.54, p = 0.03; subdistribution hazard ratio [95% CI], 1.54 [1.05-2.58]) and 90-day mortality was lower (20% vs 42%, p < 0.01) than that for no prone-positioning extracorporeal membrane oxygenation patients. Extracorporeal membrane oxygenation-related complications were comparable for the two groups. Patients without improved static compliance had higher percentages of nonaerated or poorly aerated ventral and medial-ventral lung regions (p = 0.047)., Conclusions: Prone-positioning during venovenous extracorporeal membrane oxygenation was safe and effective and was associated with a higher probability of surviving and being weaned-off extracorporeal membrane oxygenation at 90 days. Patients with greater normally aerated lung tissue in the ventral and medial-ventral regions on quantitative lung CT-scan performed before prone-positioning are more likely to improve their static compliance after that procedure during extracorporeal membrane oxygenation., Competing Interests: Dr. Combes received grants from Getinge and personal fees from Getinge, Baxter, and Xenios outside the submitted work. Dr. Schmidt received personal fees from Getinge, Drager, 3M, and Xenios, outside the submitted work. The remaining authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2021 by the Society of Critical Care Medicine and Wolters Kluwer Health, Inc. All Rights Reserved.)

The analysis of high-resolution computed tomography (CT) images of the lung is dependent on inter-subject differences in airway geometry. The application of computational models in understanding the significance of these differences has previously been shown to be a useful tool in biomedical research. Studies using image-based geometries alone are limited to the analysis of the central airways, down to generation 6-10, as other airways are not visible on high-resolution CT. However, airways distal to this, often termed the small airways, are known to play a crucial role in common airway diseases such as asthma and chronic obstructive pulmonary disease (COPD). Other studies have incorporated an algorithmic approach to extrapolate CT segmented airways in order to obtain a complete conducting airway tree down to the level of the acinus. These models have typically been used for mechanistic studies, but also have the potential to be used in a patient-specific setting. In the current study, an image analysis and modelling pipeline was developed and applied to a number of healthy (n = 11) and asthmatic (n = 24) CT patient scans to produce complete patient-based airway models to the acinar level (mean terminal generation 15.8 ± 0.47). The resulting models are analysed in terms of morphometric properties and seen to be consistent with previous work. A number of global clinical lung function measures are compared to resistance predictions in the models to assess their suitability for use in a patient-specific setting. We show a significant difference (p < 0.01) in airways resistance at all tested flow rates in complete airway trees built using CT data from severe asthmatics (GINA 3-5) versus healthy subjects. Further, model predictions of airways resistance at all flow rates are shown to correlate with patient forced expiratory volume in one second (FEV1) (Spearman ρ = -0.65, p < 0.001) and, at low flow rates (0.00017 L/s), FEV1 over forced vital capacity (FEV1/FVC) (ρ = -0.58, p < 0.001). We conclude that the pipeline and anatomical models can be used directly in mechanistic modelling studies and can form the basis for future patient-based modelling studies.

Rationale and Objectives: To analyze airway dimensions throughout the bronchial tree in severe asthmatic patients using multidetector row computed tomography (MDCT) focusing on airway narrowing., Materials and Methods: Thirty-two patients with severe asthma underwent automated (BronCare software) analysis of their right lung bronchi, with counts of airways >3 mm long arising from the main bronchi (airway count) and bronchial dimension quantification at segmental and subsegmental levels (lumen area [LA], wall area [WA], and WA%). Focal bronchial stenosis was defined as >50% narrowing of maximal LA on contiguous cross-sectional slices. Severe asthmatics were compared to 13 nonsevere asthmatic patients and nonasthmatic (pooled) subjects (Wilcoxon rank tests, then stepwise logistic regression). Finally, cluster analysis of severe asthmatic patients and stepwise logistic regression identified specific imaging subgroups., Results: The most significant differences between severe asthmatic patients and the pooled subjects were bronchial stenosis (subsegmental and all bronchi: P < .002) and WA% (P < .0003). Stepwise logistic regression retained WA% as the only explanatory covariable (P = .002). Two identified clusters of severe asthmatic patients differed for parameters characterizing airway narrowing (airway count: P = .0002; focal bronchial stenosis: P = .009). Airway count was as discriminant as forced expiratory volume in 1 second/forced vital capacity (P = .01) to identify patients in each cluster, with both variables being correlated (r = 0.59, P = .005)., Conclusions: Severe asthma-associated morphologic changes were characterized by focal bronchial stenoses and diffuse airway narrowing; the latter was associated with airflow obstruction. WA%, dependent on airway caliber, is the best parameter to identify severe asthmatic patients from pooled subjects., (Copyright © 2015 AUR. Published by Elsevier Inc. All rights reserved.)

The VESSEL12 (VESsel SEgmentation in the Lung) challenge objectively compares the performance of different algorithms to identify vessels in thoracic computed tomography (CT) scans. Vessel segmentation is fundamental in computer aided processing of data generated by 3D imaging modalities. As manual vessel segmentation is prohibitively time consuming, any real world application requires some form of automation. Several approaches exist for automated vessel segmentation, but judging their relative merits is difficult due to a lack of standardized evaluation. We present an annotated reference dataset containing 20 CT scans and propose nine categories to perform a comprehensive evaluation of vessel segmentation algorithms from both academia and industry. Twenty algorithms participated in the VESSEL12 challenge, held at International Symposium on Biomedical Imaging (ISBI) 2012. All results have been published at the VESSEL12 website http://vessel12.grand-challenge.org. The challenge remains ongoing and open to new participants. Our three contributions are: (1) an annotated reference dataset available online for evaluation of new algorithms; (2) a quantitative scoring system for objective comparison of algorithms; and (3) performance analysis of the strengths and weaknesses of the various vessel segmentation methods in the presence of various lung diseases., (Copyright © 2014 Elsevier B.V. All rights reserved.)

Emerging idea in asthma phenotyping, incorporating local morphometric information on the airway wall thickness would be able to better account for the process of airway remodeling as indicator of pathology or therapeutic impact. It is thus important that such information be provided uniformly along the airway tree, not on a sparse (cross-section) sampling basis. The volumetric segmentation of the airway wall from CT data is the issue addressed in this paper by exploiting a patient-specific surface active model. An original aspect taken into account in the proposed deformable model is the management of auto-collisions for this complex morphology. The analysis of several solutions ended up with the design of a motion vector field specific to the patient geometry to guide the deformation. The segmentation result, presented as two embedded inner/outer surfaces of the wall, allows the quantification of the tissue thickness based on a locally-defined measure sensitive to even small surface irregularities. The method is validated with respect to several ground truth simulations of pulmonary CT data with different airway geometries and acquisition protocols showing accuracy within the CT resolution range. Results from an ongoing clinical study on moderate and severe asthma are presented and discussed.

This paper describes a framework for establishing a reference airway tree segmentation, which was used to quantitatively evaluate fifteen different airway tree extraction algorithms in a standardized manner. Because of the sheer difficulty involved in manually constructing a complete reference standard from scratch, we propose to construct the reference using results from all algorithms that are to be evaluated. We start by subdividing each segmented airway tree into its individual branch segments. Each branch segment is then visually scored by trained observers to determine whether or not it is a correctly segmented part of the airway tree. Finally, the reference airway trees are constructed by taking the union of all correctly extracted branch segments. Fifteen airway tree extraction algorithms from different research groups are evaluated on a diverse set of twenty chest computed tomography (CT) scans of subjects ranging from healthy volunteers to patients with severe pathologies, scanned at different sites, with different CT scanner brands, models, and scanning protocols. Three performance measures covering different aspects of segmentation quality were computed for all participating algorithms. Results from the evaluation showed that no single algorithm could extract more than an average of 74% of the total length of all branches in the reference standard, indicating substantial differences between the algorithms. A fusion scheme that obtained superior results is presented, demonstrating that there is complementary information provided by the different algorithms and there is still room for further improvements in airway segmentation algorithms.

Computed tomography is important for the diagnosis and follow-up of chronic diffuse interstitial lung diseases. Image quality has improved from each generation of scanner to the next and this continues to allow a better characterization of extent of pathology, or even the nature of the pathological process (potentially reversible inflammatory lesions compared to fibrotic lesions). The diagnostic imaging approach has evolved at the same time as technological developments. We initially thought in terms of the predominant lesions (nodular, alveolar consolidation, ground-glass opacity), and then moved to reasoning based on patterns, which are a combination of several elementary lesions (typically for the diagnosis of idiopathic pulmonary fibrosis). Nowadays, studies are focused on building models characterizing a specific disease and which combine several distinct patterns (typically for ground-glass opacity analysis). CT also allows a quantification of the extent of lung disease, which is linked to the prognosis of the disease and helps to monitor its progression. This quantification is usually based on visual criteria, the principles of which are summarized here. The development of automated quantification software could in the near future, be a support for the radiologist., (Copyright © 2011 SPLF. Published by Elsevier Masson SAS. All rights reserved.)

Characterization and quantification of diffuse parenchymal lung disease (DPLD) severity using MDCT, mainly in interstitial lung diseases and emphysema, is an important issue in clinical research for the evaluation of new therapies. This paper develops a 3D automated approach for detection and diagnosis of DPLDs (emphysema, fibrosis, honeycombing, ground glass). The proposed methodology combines multi-resolution image decomposition based on 3D morphological filtering, and graph-based classification for a full characterization of the parenchymal tissue. The very promising results obtained on a small patient database are good premises for a near implementation and validation of the proposed approach in clinical routine.

This paper develops a volumetric quantification approach of the airway wall in multi-detector computed tomography (MDCT), exploiting a 3D segmentation methodology based on patient-specific deformable mesh model. A comparative study carried out with respect to a reference 2D/3D surface quantification technique underlines the clinical interest of the proposed approach in assessing airway remodeling in asthmatics and in evaluating the efficiency of therapeutic protocols.

Background: Computed tomography DICOM images analysis allows a quantitative measurement of organ weight, volume and specific gravity in humans., Methods: The brain weight, volume and specific gravity of 15 traumatic brain-injury patients (3+/-2 days after trauma) were computed using a specially designed software (BrainView). Data were compared with those obtained from 15 healthy subjects paired for age and overall intracranial volume., Results: Hemisphere weight were 91 g higher in patients than in controls (1167+/-101 vs 1076+/-112 g; p<0.05). Specific gravity of hemispheres (1.0367+/-0.0017 vs 1.0335+/-0.0012 g/ml; p<0.001), brainstem (1.0302+/-0.0016 vs 1.0277+/-0.0015 g/ml; p<0.001) and cerebellum (1.0396+/-0.0020 vs 1.0375+/-0.0015 g/ml; p<0.05) was significantly higher in traumatic brain injury (TBI) patients than in controls (all p<0.0001 without interaction). This increase in specific gravity was evenly distributed between the hemispheres, the brainstem and the cerebellum, and the grey and white matter. It was more pronounced in the rostral than in the caudal areas of the hemispheres. It was independent of the volume of brain contusion, of the mechanism of head injury, of natremia and of initial Glasgow coma score., Conclusion: Human TBI patients present a diffuse increase in specific gravity. This observation is in sharp opposition with the data derived from the experimental literature.

A computational model of an oscillatory laminar flow of an incompressible Newtonian fluid has been carried out in the proximal part of human tracheobronchial trees, either normal or with a strongly stenosed right main bronchus. After acquisition with a multislice spiral CT, the thoracic images are processed to reconstruct the geometry of the trachea and the first six bronchus generations and to virtually travel inside this duct network. The facetisation associated with the 3D reconstruction of the tracheobronchial tree is improved to get a computation-adapted surface triangulation, which leads to a volumic mesh composed of tetrahedra. The Navier-Stokes equations associated with the classical boundary conditions and different values of the flow dimensionless parameters are solved using the finite element method. The airways are supposed to be rigid during rest breathing. The flow distribution among the set of bronchi is determined during the respiratory cycle. Cycle reproducibility and mesh size effects on the numerical results are examined. Helpful qualitative data are provided rather than accurate quantitative results in the context of multimodelling, from image processing to numerical simulations.

Detailed description of the flow field in human airways is highly important to better understand human breathing and provide a patient's customized diagnosis. An integrated numerical simulation platform is presently proposed in order to incorporate medical images into a numerical software to calculate flow field and to analyze it in terms of fluid dynamics. The platform was set up to compute steady inspiratory airflow in realistic human airways reconstructed from tomodensitometric medical images at resting breathing conditions. This morpho-functional simulation platform has been tested retrospectively with two CT-scanned patient airway morphological models: (i) a normal airway model (subject A) with no evidence of morphological alteration and (ii) a highly altered airway model (subject B) exhibiting a severe stenosis in the right main bronchus. First, various morphological aspects proper to each airway model are provided to show the performance and interest of the reconstruction method. Second, we describe the three-dimensional flow patterns associated to the global morphological features, which are mainly shared by the present realistic models and previous idealistic airway models. Finally, the flow characteristics associated to local morphological features specific to realistic airway models are discussed. The results demonstrate that the morpho-functional simulation platform is able to capture the main features of airway velocity patterns but also more specific airflow patterns which are related to customized patient morphological features such as laminar vortex formation. The present results suggest that the proposed airway functional imaging platform is adequate to provide most of functional information related to airflow and enable a patient to patient diagnosis.

Preventing complications during hepatic surgery in living-donor transplantation or in oncologic resections requires a careful preoperative analysis of the hepatic venous anatomy. Such an analysis relies on CT hepatic venography data, which enhances the vascular structure due to contrast medium injection. However, a 3D investigation of the enhanced vascular anatomy based on typical computer vision tools is ineffective because of the large amount of occlusive opacities to be removed. This paper proposes an automated 3D approach for the segmentation of the vascular structure in CT hepatic venography, providing the appropriate tools for such an investigation. The developed methodology relies on advanced topological and morphological operators applied in mono- and multiresolution filtering schemes. It allows to discriminate the opacified vessels from the bone structures and liver parenchyma regardless of noise presence or inter-patient variability in contrast medium dispersion. The proposed approach was demonstrated at different phases of hepatic perfusion and is currently under extensive validation in clinical routine.

Objective: In patients with acute lung injury, alveolar recruitment resulting from positive end-expiratory pressure (PEEP) may be associated with overinflation of previously aerated lung regions. The aim of this study was to assess the incidence and regional distribution of lung overinflation resulting from mechanical ventilation with PEEP., Design: Reanalysis with a specific software including a color-coding system of quantitative lung computed tomography data obtained in four previous prospective studies., Setting: A 20-bed surgical intensive care unit of a Parisian university hospital., Patients: Thirty-two patients with acute lung injury in whom computed tomography of the whole lung was obtained at zero end-expiratory pressure (ZEEP) and PEEP 15 cm H2O., Interventions: None., Measurements and Main Results: Total lung recruitment was measured as the reaeration of poorly aerated (computed tomography attenuations ranging between -500 and -100 Hounsfield units) and nonaerated (computed tomography attenuations > or = -100 Hounsfield units) lung areas, and overinflation was measured as the lung volume characterized by computed tomography attenuations < or = -900 Hounsfield units. PEEP was associated with a significant alveolar recruitment (423 +/- 178 mL). Concomitantly, a lung overinflation of 123 +/- 138 mL was found in 14 patients (44%). In eight patients without chronic obstructive pulmonary disease, lung overinflation was predominantly found in nondependent lung regions located beneath the dome of diaphragm. In six patients with a past history of chronic obstructive pulmonary disease, PEEP increased the volume of emphysematous areas present in apical lung regions and produced an overinflation of nondependent lung regions located beneath the dome of diaphragm., Conclusion: Lung overinflation resulting from mechanical ventilation with PEEP is observed in more than one third of patients with acute lung injury lying supine and predominates in caudal and nondependent lung regions. Furthermore, in patients with a history of chronic obstructive pulmonary disease, PEEP markedly increases the volume of emphysematous lung regions.