# Explainable Anatomical Shape Analysis through Deep Hierarchical Generative Models

arXiv: https://arxiv.org/abs/1907.00058 · HTML: https://mlsanity.com/paper/1907.00058/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2019.

## Built on by (most cited first, 18 of 18)

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- [CHeart: A Conditional Spatio-Temporal Generative Model for Cardiac Anatomy](https://mlsanity.com/paper/2301.13098/) (2023)
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- [Interpretability of Machine Learning Methods Applied to Neuroimaging](https://mlsanity.com/paper/2204.07005/) (2022)
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- [Progressive DeepSSM: Training Methodology for Image-To-Shape Deep Models](https://mlsanity.com/paper/2310.01529/) (2023)
- [Shaping History: Advanced Machine Learning Techniques for the Analysis and Dating of Cuneiform Tablets over Three Millennia](https://mlsanity.com/paper/2406.04039/) (2024)
- [Interpretable Representation Learning of Cardiac MRI via Attribute Regularization](https://mlsanity.com/paper/2406.08282/) (2024)
- [3D Cardiac Anatomy Generation Using Mesh Latent Diffusion Models](https://mlsanity.com/paper/2508.14122/) (2025)
- [Reconnoitering the class distinguishing abilities of the features, to know them better](https://mlsanity.com/paper/2211.12771/) (2022)
- [Attribute Regularized Soft Introspective Variational Autoencoder for Interpretable Cardiac Disease Classification](https://mlsanity.com/paper/2312.08915/) (2023)
- [FusionNet: a frame interpolation network for 4D heart models](https://mlsanity.com/paper/2603.10212/) (2026)
- [Deep Spectral Models for Robust Dental Shape Generation](https://mlsanity.com/paper/2606.31293/) (2026)

## Abstract (arXiv)

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.