Universal and Expressive Statistical Shape Models for Anatomical Structures
- Form and function of anatomical structures are intimately linked. Pathological changes in form can be associated with the loss of function. For example, diseases often cause characteristic shape changes, making shape a sensitive structural biomarker for medical diagnosis. If the link between form and function is causal, correcting a pathological shape can even restore the healthy function of an organ. Accurate shape reconstruction is then crucial for effective, patient-specific treatment planning. This demonstrates the importance of shape in clinical interventions and its potential to improve overall patient outcomes. Statistical shape models are computational methods that capture shape variations in a given population and enable precise shape analysis and generation. We focus on two key properties of a good statistical shape model. First, it should be easy to construct, and second, it should accurately represent the underlying shape distribution. Established existing approaches can only be constructed from surfaces with pre-defined dense correspondence. Such correspondence is tedious to obtain, can introduce undesired biases, and prevents training on partial or sparse observations. While correspondence-free methods exist, they struggle to accurately capture shape distributions with intricate details and large variations. In this thesis, we develop shape models that simplify training and improve accuracy over state-of-the-art. To achieve these goals, we build on approximately diffeomorphic neural deformations and implicit neural representations. First, our proposed methods are trainable on correspondence-free surfaces and even partial segmentations with large slice distances. This makes them universal since they can be trained on heterogeneous data, enabling scalability to large datasets and avoiding potential biases of pre-defined correspondence. Second, our methods are highly expressive, accurately capturing intricate shape details in complex distributions. We evaluate effectiveness of our models on multiple anatomical structures, outperforming established baselines in both generative and discriminative settings.
| Author: | Tamaz AmiranashviliORCiD |
|---|---|
| Document Type: | Doctoral Thesis |
| Granting Institution: | Technische Universität München |
| Advisor: | Stefan Zachow, Björn Menze |
| Date of final exam: | 2025/07/17 |
| Publishing Institution: | Technische Universität München |
| Year of first publication: | 2025 |
| Page Number: | 86 |
| URL: | https://mediatum.ub.tum.de/?id=1776778 |
| URL: | https://nbn-resolving.org/urn:nbn:de:bvb:91-diss-20250717-1776778-0-4 |

