Spatio-temporal neural distance fields for conditional generative modeling of the heart
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866929421287424000 |
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| author | Sørensen, Kristine Diez, Paula Margeta, Jan Youssef, Yasmin El Pham, Michael Pedersen, Jonas Jalili Kühl, Tobias de Backer, Ole Kofoed, Klaus Camara, Oscar Paulsen, Rasmus |
| author_facet | Sørensen, Kristine Diez, Paula Margeta, Jan Youssef, Yasmin El Pham, Michael Pedersen, Jonas Jalili Kühl, Tobias de Backer, Ole Kofoed, Klaus Camara, Oscar Paulsen, Rasmus |
| contents | The rhythmic pumping motion of the heart stands as a cornerstone in life, as it circulates blood to the entire human body through a series of carefully timed contractions of the individual chambers. Changes in the size, shape and movement of the chambers can be important markers for cardiac disease and modeling this in relation to clinical demography or disease is therefore of interest. Existing methods for spatio-temporal modeling of the human heart require shape correspondence over time or suffer from large memory requirements, making it difficult to use for complex anatomies. We introduce a novel conditional generative model, where the shape and movement is modeled implicitly in the form of a spatio-temporal neural distance field and conditioned on clinical demography. The model is based on an auto-decoder architecture and aims to disentangle the individual variations from that related to the clinical demography. It is tested on the left atrium (including the left atrial appendage), where it outperforms current state-of-the-art methods for anatomical sequence completion and generates synthetic sequences that realistically mimics the shape and motion of the real left atrium. In practice, this means we can infer functional measurements from a static image, generate synthetic populations with specified demography or disease and investigate how non-imaging clinical data effect the shape and motion of cardiac anatomies. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2407_10663 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Spatio-temporal neural distance fields for conditional generative modeling of the heart Sørensen, Kristine Diez, Paula Margeta, Jan Youssef, Yasmin El Pham, Michael Pedersen, Jonas Jalili Kühl, Tobias de Backer, Ole Kofoed, Klaus Camara, Oscar Paulsen, Rasmus Computer Vision and Pattern Recognition Artificial Intelligence The rhythmic pumping motion of the heart stands as a cornerstone in life, as it circulates blood to the entire human body through a series of carefully timed contractions of the individual chambers. Changes in the size, shape and movement of the chambers can be important markers for cardiac disease and modeling this in relation to clinical demography or disease is therefore of interest. Existing methods for spatio-temporal modeling of the human heart require shape correspondence over time or suffer from large memory requirements, making it difficult to use for complex anatomies. We introduce a novel conditional generative model, where the shape and movement is modeled implicitly in the form of a spatio-temporal neural distance field and conditioned on clinical demography. The model is based on an auto-decoder architecture and aims to disentangle the individual variations from that related to the clinical demography. It is tested on the left atrium (including the left atrial appendage), where it outperforms current state-of-the-art methods for anatomical sequence completion and generates synthetic sequences that realistically mimics the shape and motion of the real left atrium. In practice, this means we can infer functional measurements from a static image, generate synthetic populations with specified demography or disease and investigate how non-imaging clinical data effect the shape and motion of cardiac anatomies. |
| title | Spatio-temporal neural distance fields for conditional generative modeling of the heart |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2407.10663 |