Online learning in motion modeling for intra-interventional image sequences
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866912073425879040 |
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| author | Gunnarsson, Niklas Sjölund, Jens Kimstrand, Peter Schön, Thomas. B |
| author_facet | Gunnarsson, Niklas Sjölund, Jens Kimstrand, Peter Schön, Thomas. B |
| contents | Image monitoring and guidance during medical examinations can aid both diagnosis and treatment. However, the sampling frequency is often too low, which creates a need to estimate the missing images. We present a probabilistic motion model for sequential medical images, with the ability to both estimate motion between acquired images and forecast the motion ahead of time. The core is a low-dimensional temporal process based on a linear Gaussian state-space model with analytically tractable solutions for forecasting, simulation, and imputation of missing samples. The results, from two experiments on publicly available cardiac datasets, show reliable motion estimates and an improved forecasting performance using patient-specific adaptation by online learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_11491 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Online learning in motion modeling for intra-interventional image sequences Gunnarsson, Niklas Sjölund, Jens Kimstrand, Peter Schön, Thomas. B Computer Vision and Pattern Recognition Medical Physics Image monitoring and guidance during medical examinations can aid both diagnosis and treatment. However, the sampling frequency is often too low, which creates a need to estimate the missing images. We present a probabilistic motion model for sequential medical images, with the ability to both estimate motion between acquired images and forecast the motion ahead of time. The core is a low-dimensional temporal process based on a linear Gaussian state-space model with analytically tractable solutions for forecasting, simulation, and imputation of missing samples. The results, from two experiments on publicly available cardiac datasets, show reliable motion estimates and an improved forecasting performance using patient-specific adaptation by online learning. |
| title | Online learning in motion modeling for intra-interventional image sequences |
| topic | Computer Vision and Pattern Recognition Medical Physics |
| url | https://arxiv.org/abs/2410.11491 |