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Three-dimensional microscopy and image analysis methodology for mapping and quantification of nuclear positions in tissues with approximate cylindrical geometry.

ABSTRACT: Organogenesis involves extensive and dynamic changes of tissue shape during development. It is associated with complex morphogenetic events that require enormous tissue plasticity and generate a large variety of transient three-dimensional geometries that are achieved by global tissue responses. Nevertheless, such global responses are driven by tight spatio-temporal regulation of the behaviours of individual cells composing these tissues. Therefore, the development of image analysis tools that allow for extraction of quantitative data concerning individual cell behaviours is central to study tissue morphogenesis. There are many image analysis tools available that permit extraction of cell parameters. Unfortunately, the majority are developed for tissues with relatively simple geometries such as flat epithelia. Problems arise when the tissue of interest assumes a more complex three-dimensional geometry. Here, we use the endothelium of the developing zebrafish dorsal aorta as an example of a tissue with cylindrical geometry and describe the image analysis routines developed to extract quantitative data on individual cells in such tissues, as well as the image acquisition and sample preparation methodology.This article is part of the Theo Murphy meeting issue 'Mechanics of development'.

SUBMITTER: Campinho P

PROVIDER: S-EPMC6158202 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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Three-dimensional microscopy and image analysis methodology for mapping and quantification of nuclear positions in tissues with approximate cylindrical geometry.

Campinho Pedro P Lamperti Paola P Boselli Francesco F Vermot Julien J

Philosophical transactions of the Royal Society of London. Series B, Biological sciences 20180924 1759

Organogenesis involves extensive and dynamic changes of tissue shape during development. It is associated with complex morphogenetic events that require enormous tissue plasticity and generate a large variety of transient three-dimensional geometries that are achieved by global tissue responses. Nevertheless, such global responses are driven by tight spatio-temporal regulation of the behaviours of individual cells composing these tissues. Therefore, the development of image analysis tools that a ...[more]

PMID: 30249780

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Project description:Accurate quantification of bone, muscle, and their components is still an unmet need in the musculoskeletal field. Current methods to quantify tissue volumes in 3D images are expensive, labor-intensive, and time-consuming; thus, a reliable, valid, and quick application is highly needed. Tissue Compass is a standalone software for semiautomatic segmentation and automatic quantification of musculoskeletal organs. To validate the software, cross-sectional micro-CT scans images of rat femur (n = 19), and CT images of hip and abdomen (n = 100) from the Osteoporotic Fractures in Men (MrOS) Study were used to quantify bone, hematopoietic marrow (HBM), and marrow adipose tissue (MAT) using commercial manual software as a comparator. Also, abdominal CT scans (n = 100) were used to quantify psoas muscle volumes and intermuscular adipose tissue (IMAT) using the same software. We calculated Pearson's correlation coefficients, individual intra-class correlation coefficients (ICC), and Bland-Altman limits of agreement together with Bland-Altman plots to show the inter- and intra-observer agreement between Tissue Compass and commercially available software. In the animal study, the agreement between Tissue Compass and commercial software was r > 0.93 and ICC > 0.93 for rat femur measurements. Bland-Altman limits of agreement was - 720.89 (- 1.5e+04, 13,074.00) for MAT, 4421.11 (- 1.8e+04, 27,149.73) for HBM and - 6073.32 (- 2.9e+04, 16,388.37) for bone. The inter-observer agreement for QCT human study between two observers was r > 0.99 and ICC > 0.99. Bland-Altman limits of agreement was 0.01 (- 0.07, 0.10) for MAT in hip, 0.02 (- 0.08, 0.12) for HBM in hip, 0.05 (- 0.15, 0.25) for bone in hip, 0.02 (- 0.18, 0.22) for MAT in L1, 0.00 (- 0.16, 0.16) for HBM in L1, and 0.02 (- 0.23, 0.27) for bone in L1. The intra-observer agreement for QCT human study between the two applications was r > 0.997 and ICC > 0.99. Bland-Altman limits of agreement was 0.03 (- 0.13, 0.20) for MAT in hip, 0.05 (- 0.08, 0.18) for HBM in hip, 0.05 (- 0.24, 0.34) for bone in hip, - 0.02 (- 0.34, 0.31) for MAT in L1, - 0.14 (- 0.44, 0.17) for HBM in L1, - 0.29 (- 0.62, 0.05) for bone in L1, 0.03 (- 0.08, 0.15) for IMAT in psoas, and 0.02 (- 0.35, 0.38) for muscle in psoas. Compared to a conventional application, Tissue Compass demonstrated high accuracy and non-inferiority while also facilitating easier analyses. Tissue Compass could become the tool of choice to diagnose tissue loss/gain syndromes in the future by requiring a small number of CT sections to detect tissue volumes and fat infiltration.

Project description:Accurate three-dimensional (3D) reconstruction of myocardial infarct geometry is crucial to patient-specific modeling of the heart aimed at providing therapeutic guidance in ischemic cardiomyopathy. However, myocardial infarct imaging is clinically performed using two-dimensional (2D) late-gadolinium enhanced cardiac magnetic resonance (LGE-CMR) techniques, and a method to build accurate 3D infarct reconstructions from the 2D LGE-CMR images has been lacking. The purpose of this study was to address this need.The authors developed a novel methodology to reconstruct 3D infarct geometry from segmented low-resolution (Lo-res) clinical LGE-CMR images. Their methodology employed the so-called logarithm of odds (LogOdds) function to implicitly represent the shape of the infarct in segmented image slices as LogOdds maps. These 2D maps were then interpolated into a 3D image, and the result transformed via the inverse of LogOdds to a binary image representing the 3D infarct geometry. To assess the efficacy of this method, the authors utilized 39 high-resolution (Hi-res) LGE-CMR images, including 36 in vivo acquisitions of human subjects with prior myocardial infarction and 3 ex vivo scans of canine hearts following coronary ligation to induce infarction. The infarct was manually segmented by trained experts in each slice of the Hi-res images, and the segmented data were downsampled to typical clinical resolution. The proposed method was then used to reconstruct 3D infarct geometry from the downsampled images, and the resulting reconstructions were compared with the manually segmented data. The method was extensively evaluated using metrics based on geometry as well as results of electrophysiological simulations of cardiac sinus rhythm and ventricular tachycardia in individual hearts. Several alternative reconstruction techniques were also implemented and compared with the proposed method.The accuracy of the LogOdds method in reconstructing 3D infarct geometry, as measured by the Dice similarity coefficient, was 82.10% ± 6.58%, a significantly higher value than those of the alternative reconstruction methods. Among outcomes of electrophysiological simulations with infarct reconstructions generated by various methods, the simulation results corresponding to the LogOdds method showed the smallest deviation from those corresponding to the manual reconstructions, as measured by metrics based on both activation maps and pseudo-ECGs.The authors have developed a novel method for reconstructing 3D infarct geometry from segmented slices of Lo-res clinical 2D LGE-CMR images. This method outperformed alternative approaches in reproducing expert manual 3D reconstructions and in electrophysiological simulations.

Dataset Information

Three-dimensional microscopy and image analysis methodology for mapping and quantification of nuclear positions in tissues with approximate cylindrical geometry.

Publications

Three-dimensional microscopy and image analysis methodology for mapping and quantification of nuclear positions in tissues with approximate cylindrical geometry.

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