Atlas-based anatomical modeling and analysis of heart disease

Medrano-Gracia, Pau; Cowan, Brett R.; Suinesiaputra, Avan; Young, Alistair A.

doi:10.1016/j.ddmod.2014.05.002

Cited by 7 publications

(4 citation statements)

References 37 publications

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“…Given the high dimensionality of medical images, a popular approach in the literature is to analyze them by constructing statistical shape models of the heart using finite elements models or segmentation and co-registration algorithms to derive subject-specific meshes [8,9]. Similar to brain image analysis, principal component analysis (PCA) is then subsequently performed on these point distribution models to learn their main modes of deformation.…”

Section: Related Workmentioning

confidence: 99%

Learning Interpretable Anatomical Features Through Deep Generative Models: Application to Cardiac Remodeling

Biffi

Oktay

Tarroni

et al. 2018

Lecture Notes in Computer Science

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Alterations in the geometry and function of the heart define well-established causes of cardiovascular disease. However, current approaches to the diagnosis of cardiovascular diseases often rely on subjective human assessment as well as manual analysis of medical images. Both factors limit the sensitivity in quantifying complex structural and functional phenotypes. Deep learning approaches have recently achieved success for tasks such as classification or segmentation of medical images, but lack interpretability in the feature extraction and decision processes, limiting their value in clinical diagnosis. In this work, we propose a 3D convolutional generative model for automatic classification of images from patients with cardiac diseases associated with structural remodeling. The model leverages interpretable task-specific anatomic patterns learned from 3D segmentations. It further allows to visualise and quantify the learned pathology-specific remodeling patterns in the original input space of the images. This approach yields high accuracy in the categorization of healthy and hypertrophic cardiomyopathy subjects when tested on unseen MR images from our own multi-centre dataset (100%) as well on the ACDC MICCAI 2017 dataset (90%). We believe that the proposed deep learning approach is a promising step towards the development of interpretable classifiers for the medical imaging domain, which may help clinicians to improve diagnostic accuracy and enhance patient risk-stratification.

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Section: Related Workmentioning

confidence: 99%

Learning Interpretable Anatomical Features Through Deep Generative Models: Application to Cardiac Remodeling

Biffi

Oktay

Tarroni

et al. 2018

Lecture Notes in Computer Science

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“…A variety of atlas-based label fusion approaches [12][13][14] have also been proposed for the same task.…”

Section: Related Workmentioning

confidence: 99%

“…Traditional approaches to cardiac MRI segmentation were model-based, or hybrid techniques, which employed deformable [ 9 ], active contour [ 10 ], or statistical shape models [ 11 ], in combination with thresholding and morphological operations. A variety of atlas-based label fusion approaches [ 12 , 13 , 14 ] have also been proposed for the same task.…”

Section: Related Workmentioning

confidence: 99%

Fully Automated 3D Cardiac MRI Localisation and Segmentation Using Deep Neural Networks

2020

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Cardiac magnetic resonance (CMR) imaging is used widely for morphological assessment and diagnosis of various cardiovascular diseases. Deep learning approaches based on 3D fully convolutional networks (FCNs), have improved state-of-the-art segmentation performance in CMR images. However, previous methods have employed several pre-processing steps and have focused primarily on segmenting low-resolutions images. A crucial step in any automatic segmentation approach is to first localize the cardiac structure of interest within the MRI volume, to reduce false positives and computational complexity. In this paper, we propose two strategies for localizing and segmenting the heart ventricles and myocardium, termed multi-stage and end-to-end, using a 3D convolutional neural network. Our method consists of an encoder–decoder network that is first trained to predict a coarse localized density map of the target structure at a low resolution. Subsequently, a second similar network employs this coarse density map to crop the image at a higher resolution, and consequently, segment the target structure. For the latter, the same two-stage architecture is trained end-to-end. The 3D U-Net with some architectural changes (referred to as 3D DR-UNet) was used as the base architecture in this framework for both the multi-stage and end-to-end strategies. Moreover, we investigate whether the incorporation of coarse features improves the segmentation. We evaluate the two proposed segmentation strategies on two cardiac MRI datasets, namely, the Automatic Cardiac Segmentation Challenge (ACDC) STACOM 2017, and Left Atrium Segmentation Challenge (LASC) STACOM 2018. Extensive experiments and comparisons with other state-of-the-art methods indicate that the proposed multi-stage framework consistently outperforms the rest in terms of several segmentation metrics. The experimental results highlight the robustness of the proposed approach, and its ability to generate accurate high-resolution segmentations, despite the presence of varying degrees of pathology-induced changes to cardiac morphology and image appearance, low contrast, and noise in the CMR volumes.

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“…More recent techniques include statistical shape modelling [3] and [4], and atlas-based regional wall motion analysis [5]. A common constraint used in state-of-the-art methods to retain physiological motion dynamics is to enforce the deformations (which describe the motion from one image to another) to be diffeomorphic (smooth transformations that preserve the structure of the material to prevent non-physiological transformations such as folding).…”

Section: A Related Workmentioning

confidence: 99%

Cardiac Motion Evolution Model for Analysis of Functional Changes Using Tensor Decomposition and Cross-Sectional Data

McLeod

Tøndel

Calvet³

et al. 2018

IEEE Trans. Biomed. Eng.

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Abstract-Cardiac disease can reduce the ability of the ventricles to function well enough to sustain long-term pumping efficiency. Recent advances in cardiac motion tracking have led to improvements in the analysis of cardiac function. We propose a method to study cohort effects related to age with respect to cardiac function. The proposed approach makes use of a recent method for describing cardiac motion of a given subject using a Polyaffine model, which gives a compact parameterisation that reliably and accurately describes the cardiac motion across populations. Using this method, a data tensor of motion parameters is extracted for a given population. The partial least squares method for higher-order arrays is used to build a model to describe the motion parameters with respect to age, from which a model of motion given age is derived. Based on cross-sectional statistical analysis with the data tensor of each subject treated as an observation along time, the left ventricular motion over time of Tetralogy of Fallot patients is analysed to understand the temporal evolution of functional abnormalities in this population compared to healthy motion dynamics.

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Atlas-based anatomical modeling and analysis of heart disease

Cited by 7 publications

References 37 publications

Learning Interpretable Anatomical Features Through Deep Generative Models: Application to Cardiac Remodeling

Learning Interpretable Anatomical Features Through Deep Generative Models: Application to Cardiac Remodeling

Fully Automated 3D Cardiac MRI Localisation and Segmentation Using Deep Neural Networks

Cardiac Motion Evolution Model for Analysis of Functional Changes Using Tensor Decomposition and Cross-Sectional Data

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