Deep Part-Based Generative Shape Model with Latent Variables

International audienceThe Shape Boltzmann Machine (SBM) [6] and its multilabel version MSBM [5] have been recently introduced as deep generative models that capture the variations of an object shape. While being more flexible MSBM requires datasets with labeled parts of the objects for training. In the paper we present an algorithm for training MSBM using binary masks of objects and the seeds which approximately correspond to the locations of objects parts. The latter can be obtained from part-based detectors in an unsupervised manner. We derive a latent variable model and an EM-like training procedure for adjusting the weights of MSBM using a deep learning framework. We show that the model trained by our method outperforms SBM in the tasks related to binary shapes and is very close to the original MSBM in terms of quality of multilabel shapes

Machine learning approaches to model cardiac shape in large-scale imaging studies

Recent improvements in non-invasive imaging, together with the introduction of fully-automated segmentation algorithms and big data analytics, has paved the way for large-scale population-based imaging studies. These studies promise to increase our understanding of a large number of medical conditions, including cardiovascular diseases. However, analysis of cardiac shape in such studies is often limited to simple morphometric indices, ignoring large part of the information available in medical images. Discovery of new biomarkers by machine learning has recently gained traction, but often lacks interpretability. The research presented in this thesis aimed at developing novel explainable machine learning and computational methods capable of better summarizing shape variability, to better inform association and predictive clinical models in large-scale imaging studies. A powerful and flexible framework to model the relationship between three-dimensional (3D) cardiac atlases, encoding multiple phenotypic traits, and genetic variables is first presented. The proposed approach enables the detection of regional phenotype-genotype associations that would be otherwise neglected by conventional association analysis. Three learning-based systems based on deep generative models are then proposed. In the first model, I propose a classifier of cardiac shapes which exploits task-specific generative shape features, and it is designed to enable the visualisation of the anatomical effect these features encode in 3D, making the classification task transparent. The second approach models a database of anatomical shapes via a hierarchy of conditional latent variables and it is capable of detecting, quantifying and visualising onto a template shape the most discriminative anatomical features that characterize distinct clinical conditions. Finally, a preliminary analysis of a deep learning system capable of reconstructing 3D high-resolution cardiac segmentations from a sparse set of 2D views segmentations is reported. This thesis demonstrates that machine learning approaches can facilitate high-throughput analysis of normal and pathological anatomy and of its determinants without losing clinical interpretability.Open Acces

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

Variational Autoencoders for Deforming 3D Mesh Models

3D geometric contents are becoming increasingly popular. In this paper, we study the problem of analyzing deforming 3D meshes using deep neural networks. Deforming 3D meshes are flexible to represent 3D animation sequences as well as collections of objects of the same category, allowing diverse shapes with large-scale non-linear deformations. We propose a novel framework which we call mesh variational autoencoders (mesh VAE), to explore the probabilistic latent space of 3D surfaces. The framework is easy to train, and requires very few training examples. We also propose an extended model which allows flexibly adjusting the significance of different latent variables by altering the prior distribution. Extensive experiments demonstrate that our general framework is able to learn a reasonable representation for a collection of deformable shapes, and produce competitive results for a variety of applications, including shape generation, shape interpolation, shape space embedding and shape exploration, outperforming state-of-the-art methods.Comment: CVPR 201