Alapfogalmak
The author presents a novel unsupervised geometric deep-learning model to establish refinable shape correspondences in a latent space, construct a population-derived atlas, and generate realistic synthetic shapes. The approach is extended to a joint shape generative-clustering multi-atlas framework to enhance variability and preserve details in generated shapes.
Kivonat
The content introduces a deep learning generative framework for refining shape matching and generation. It addresses challenges in generating anatomically plausible shapes by utilizing graph representations and unsupervised learning. Experimental results demonstrate the model's applicability to computational medicine.
The study focuses on generative modeling for shapes, emphasizing the importance of establishing dense correspondences between shapes with variable mesh typologies. It discusses the challenges of generating representative populations of anatomical shapes suitable for in-silico experiments.
Furthermore, the content evaluates the performance of different models in terms of shape matching quality, generalization, specificity, and clinical relevance. Results show that the proposed Atlas-R-ASMG model outperforms PCA-based models and improves anatomical plausibility in virtual cohorts.
Additionally, an extension model mAtlas-R-ASMG is introduced to handle shape populations with diverse structures through joint clustering and multi-atlas construction. The evaluation demonstrates improved accuracy, reliability, and robustness in shape matching by considering multiple reference atlases.
Statisztikák
HD: 6.43 ± 1.44
CD: 12.04 ± 27.63
HD: 5.91 ± 1.38
CD: 9.85 ± 0.94
HD: 20.21 ± 6.35
CD: 58.14 ± 21.96
Idézetek
"The increasing availability of large-scale medical imaging datasets has enabled these underlying shape variations in the population to be modeled more accurately."
"Our method’s superiority arises from its spatial-based geometric deep learning and fully-differentiable shape matching."
"The refinement improves the quality of the normalized shapes over the sGCN-ATT and hGCN-ATT settings."