Stratified decision forests for accurate anatomical landmark localization in cardiac images

Accurate localization of anatomical landmarks is an important step in medical imaging, as it provides useful prior information for subsequent image analysis and acquisition methods. It is particularly useful for initialization of automatic image analysis tools (e.g. segmentation and registration) and detection of scan planes for automated image acquisition. Landmark localization has been commonly performed using learning based approaches, such as classifier and/or regressor models. However, trained models may not generalize well in heterogeneous datasets when the images contain large differences due to size, pose and shape variations of organs. To learn more data-adaptive and patient specific models, we propose a novel stratification based training model, and demonstrate its use in a decision forest. The proposed approach does not require any additional training information compared to the standard model training procedure and can be easily integrated into any decision tree framework. The proposed method is evaluated on 1080 3D highresolution and 90 multi-stack 2D cardiac cine MR images. The experiments show that the proposed method achieves state-of-theart landmark localization accuracy and outperforms standard regression and classification based approaches. Additionally, the proposed method is used in a multi-atlas segmentation to create a fully automatic segmentation pipeline, and the results show that it achieves state-of-the-art segmentation accuracy

3D High-Resolution Cardiac Segmentation Reconstruction From 2D Views Using Conditional Variational Autoencoders

Accurate segmentation of heart structures imaged by cardiac MR is key for the quantitative analysis of pathology. High-resolution 3D MR sequences enable whole-heart structural imaging but are time-consuming, expensive to acquire and they often require long breath holds that are not suitable for patients. Consequently, multiplanar breath-hold 2D cines sequences are standard practice but are disadvantaged by lack of whole-heart coverage and low through-plane resolution. To address this, we propose a conditional variational autoencoder architecture able to learn a generative model of 3D high-resolution left ventricular (LV) segmentations which is conditioned on three 2D LV segmentations of one short-axis and two long-axis images. By only employing these three 2D segmentations, our model can efficiently reconstruct the 3D high-resolution LV segmentation of a subject. When evaluated on 400 unseen healthy volunteers, our model yielded an average Dice score of 87.92 ± 0.15 and outperformed competing architectures (TL-net, Dice score = 82.60 ± 0.23, p = 2.2 · 10 -16 )

Fractal analysis of left ventricular trabeculations is associated with impaired myocardial deformation in healthy Chinese

Background: Left ventricular (LV) non-compaction (LVNC) is defined by extreme LV trabeculation, but is measured variably. Here we examined the relationship between quantitative measurement in LV trabeculation and myocardial deformation in health and disease and determined the clinical utility of semi-automated assessment of LV trabeculations. Methods: Cardiovascular magnetic resonance (CMR) was performed in 180 healthy Singaporean Chinese (age 20–69 years; males, n = 91), using balanced steady state free precession cine imaging at 3T. The degree of LV trabeculation was assessed by fractal dimension (FD) as a robust measure of trabeculation complexity using a semi-automated technique. FD measures were determined in healthy men and women to derive normal reference ranges. Myocardial deformation was evaluated using feature tracking. We tested the utility of this algorithm and the normal ranges in 10 individuals with confirmed LVNC (non-compacted/compacted; NC/C ratio > 2.3 and ≥1 risk factor for LVNC) and 13 individuals with suspected disease (NC/C ratio > 2.3). Results: Fractal analysis is a reproducible means of assessing LV trabeculation extent (intra-class correlation coefficient: intra-observer, 0.924, 95% CI [0.761–0.973]; inter-observer, 0.925, 95% CI [0.821–0.970]). The overall extent of LV trabeculation (global FD: 1.205 ± 0.031) was independently associated with increased indexed LV end-diastolic volume and mass (sβ = 0.35; p  2.3. Conclusion: This study defines the normal range of LV trabeculation in healthy Chinese that can be used to make or refute a diagnosis of LVNC using the fractal analysis tool, which we make freely available. We also show that increased myocardial trabeculation is associated with higher LV volumes, mass and reduced myocardial strain

Deep learning cardiac motion analysis for human survival prediction

Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimising the interpretation of dynamic biological systems requires accurate and precise motion tracking as well as efficient representations of high-dimensional motion trajectories so that these can be used for prediction tasks. Here we use image sequences of the heart, acquired using cardiac magnetic resonance imaging, to create time-resolved three-dimensional segmentations using a fully convolutional network trained on anatomical shape priors. This dense motion model formed the input to a supervised denoising autoencoder (4Dsurvival), which is a hybrid network consisting of an autoencoder that learns a task-specific latent code representation trained on observed outcome data, yielding a latent representation optimised for survival prediction. To handle right-censored survival outcomes, our network used a Cox partial likelihood loss function. In a study of 302 patients the predictive accuracy (quantified by Harrell's C-index) was significantly higher (p < .0001) for our model C=0.73 (95$\%$ CI: 0.68 - 0.78) than the human benchmark of C=0.59 (95$\%$ CI: 0.53 - 0.65). This work demonstrates how a complex computer vision task using high-dimensional medical image data can efficiently predict human survival

A Comprehensive Approach for Learning-Based Fully-Automated Inter-slice Motion Correction for Short-Axis Cine Cardiac MR Image Stacks

In the clinical routine, short axis (SA) cine cardiac MR (CMR) image stacks are acquired during multiple subsequent breath-holds. If the patient cannot consistently hold the breath at the same position, the acquired image stack will be affected by inter-slice respiratory motion and will not correctly represent the cardiac volume, introducing potential errors in the following analyses and visualisations. We propose an approach to automatically correct inter-slice respiratory motion in SA CMR image stacks. Our approach makes use of probabilistic segmentation maps (PSMs) of the left ventricular (LV) cavity generated with decision forests. PSMs are generated for each slice of the SA stack and rigidly registered in-plane to a target PSM. If long axis (LA) images are available, PSMs are generated for them and combined to create the target PSM; if not, the target PSM is produced from the same stack using a 3D model trained from motion-free stacks. The proposed approach was tested on a dataset of SA stacks acquired from 24 healthy subjects (for which anatomical 3D cardiac images were also available as reference) and compared to two techniques which use LA intensity images and LA segmentations as targets, respectively. The results show the accuracy and robustness of the proposed approach in motion compensation

Explainable Anatomical Shape Analysis through Deep Hierarchical Generative Models

Quantification of anatomical shape changes currently relies on scalar global indexes which are largely insensitive to regional or asymmetric modifications. Accurate assessment of pathology-driven anatomical remodeling is a crucial step for the diagnosis and treatment of many conditions. Deep learning approaches have recently achieved wide success in the analysis of medical images, but they lack interpretability in the feature extraction and decision processes. In this work, we propose a new interpretable deep learning model for shape analysis. In particular, we exploit deep generative networks to model a population of anatomical segmentations through a hierarchy of conditional latent variables. At the highest level of this hierarchy, a two-dimensional latent space is simultaneously optimised to discriminate distinct clinical conditions, enabling the direct visualisation of the classification space. Moreover, the anatomical variability encoded by this discriminative latent space can be visualised in the segmentation space thanks to the generative properties of the model, making the classification task transparent. This approach yielded high accuracy in the categorisation of healthy and remodelled left ventricles when tested on unseen segmentations from our own multi-centre dataset as well as in an external validation set, and on hippocampi from healthy controls and patients with Alzheimer's disease when tested on ADNI data. More importantly, it enabled the visualisation in three-dimensions of both global and regional anatomical features which better discriminate between the conditions under exam. The proposed approach scales effectively to large populations, facilitating high-throughput analysis of normal anatomy and pathology in large-scale studies of volumetric imaging

Peripartum cardiomyopathy: a review

Peripartum cardiomyopathy is a rare type of heart failure manifesting towards the end of pregnancy or in the months following delivery, in the absence of any other cause of heart failure. There is a wide range of incidence across countries reflecting different population demographics, uncertainty over definitions and under-reporting. Race, ethnicity, multiparity and advanced maternal age are considered important risk factors for the disease. Its etiopathogenesis is incompletely understood and is likely multifactorial, including hemodynamic stresses of pregnancy, vasculo-hormonal factors, inflammation, immunology and genetics. Affected women present with heart failure secondary to reduced left ventricular systolic function (LVEF <45%) and often with associated phenotypes such as LV dilatation, biatrial dilatation, reduced systolic function, impaired diastolic function, and increased pulmonary pressure. Electrocardiography, echocardiography, magnetic resonance imaging, endomyocardial biopsy, and certain blood biomarkers aid in diagnosis and management. Treatment for peripartum cardiomyopathy depends on the stage of pregnancy or postpartum, disease severity and whether the woman is breastfeeding. It includes standard pharmacological therapies for heart failure, within the safety restrictions for pregnancy and lactation. Targeted therapies such as bromocriptine have shown promise in early, small studies, with large definitive trials currently underway. Failure of medical interventions may require mechanical support and transplantation in severe cases. Peripartum cardiomyopathy carries a high mortality rate of up to 10% and a high risk of relapse in subsequent pregnancies, but over half of women present normalization of LV function within a year of diagnosis