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      Estimation of Regional Pulmonary Compliance in Idiopathic Pulmonary Fibrosis Based on Personalized Lung Poromechanical Modeling

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          Abstract

          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.

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          User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability.

          Active contour segmentation and its robust implementation using level set methods are well-established theoretical approaches that have been studied thoroughly in the image analysis literature. Despite the existence of these powerful segmentation methods, the needs of clinical research continue to be fulfilled, to a large extent, using slice-by-slice manual tracing. To bridge the gap between methodological advances and clinical routine, we developed an open source application called ITK-SNAP, which is intended to make level set segmentation easily accessible to a wide range of users, including those with little or no mathematical expertise. This paper describes the methods and software engineering philosophy behind this new tool and provides the results of validation experiments performed in the context of an ongoing child autism neuroimaging study. The validation establishes SNAP intrarater and interrater reliability and overlap error statistics for the caudate nucleus and finds that SNAP is a highly reliable and efficient alternative to manual tracing. Analogous results for lateral ventricle segmentation are provided.
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            Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool

            Background Medical Image segmentation is an important image processing step. Comparing images to evaluate the quality of segmentation is an essential part of measuring progress in this research area. Some of the challenges in evaluating medical segmentation are: metric selection, the use in the literature of multiple definitions for certain metrics, inefficiency of the metric calculation implementations leading to difficulties with large volumes, and lack of support for fuzzy segmentation by existing metrics. Result First we present an overview of 20 evaluation metrics selected based on a comprehensive literature review. For fuzzy segmentation, which shows the level of membership of each voxel to multiple classes, fuzzy definitions of all metrics are provided. We present a discussion about metric properties to provide a guide for selecting evaluation metrics. Finally, we propose an efficient evaluation tool implementing the 20 selected metrics. The tool is optimized to perform efficiently in terms of speed and required memory, also if the image size is extremely large as in the case of whole body MRI or CT volume segmentation. An implementation of this tool is available as an open source project. Conclusion We propose an efficient evaluation tool for 3D medical image segmentation using 20 evaluation metrics and provide guidelines for selecting a subset of these metrics that is suitable for the data and the segmentation task.
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              Gmsh: A 3-D finite element mesh generator with built-in pre- and post-processing facilities

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                Author and article information

                Journal
                Journal of Biomechanical Engineering
                ASME International
                0148-0731
                1528-8951
                September 01 2022
                September 01 2022
                March 30 2022
                : 144
                : 9
                Affiliations
                [1 ]Inria, Palaiseau 91120, France; Laboratoire de Mécanique des Solides, École Polytechnique/CNRS/IPP, Palaiseau 91120, France
                [2 ]Hypoxie et Poumon, Université Sorbonne Paris Nord/INSERM, Bobigny 93022, France; Hôpital Avicenne, APHP, Bobigny 93022, France
                [3 ]SAMOVAR, Telecom SudParis/Institut Mines-Télécom/IPP, Évry 91042, France
                [4 ]Hypoxie et Poumon, Université Sorbonne Paris Nord/INSERM, Bobigny 93022, France
                [5 ]Laboratoire de Mecanique des Solides, École Polytechnique/CNRS/IPP, Palaiseau 91120, France; Inria, Palaiseau 91120, France
                Article
                10.1115/1.4054106
                35292805
                35e96e3b-2fee-4f86-899f-99914dc32728
                © 2022

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