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Robust and Realtime Large Deformation Ultrasound Registration Using End-to-End Differentiable Displacement Optimisation
Image registration for temporal ultrasound sequences can be very beneficial for image-guided diagnostics and interventions. Cooperative human–machine systems that enable seamless assistance for both inexperienced and expert users during ultrasound examinations rely on robust, realtime motion estimat...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10056872/ https://www.ncbi.nlm.nih.gov/pubmed/36991588 http://dx.doi.org/10.3390/s23062876 |
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author | Heinrich, Mattias P. Siebert, Hanna Graf, Laura Mischkewitz, Sven Hansen, Lasse |
author_facet | Heinrich, Mattias P. Siebert, Hanna Graf, Laura Mischkewitz, Sven Hansen, Lasse |
author_sort | Heinrich, Mattias P. |
collection | PubMed |
description | Image registration for temporal ultrasound sequences can be very beneficial for image-guided diagnostics and interventions. Cooperative human–machine systems that enable seamless assistance for both inexperienced and expert users during ultrasound examinations rely on robust, realtime motion estimation. Yet rapid and irregular motion patterns, varying image contrast and domain shifts in imaging devices pose a severe challenge to conventional realtime registration approaches. While learning-based registration networks have the promise of abstracting relevant features and delivering very fast inference times, they come at the potential risk of limited generalisation and robustness for unseen data; in particular, when trained with limited supervision. In this work, we demonstrate that these issues can be overcome by using end-to-end differentiable displacement optimisation. Our method involves a trainable feature backbone, a correlation layer that evaluates a large range of displacement options simultaneously and a differentiable regularisation module that ensures smooth and plausible deformation. In extensive experiments on public and private ultrasound datasets with very sparse ground truth annotation the method showed better generalisation abilities and overall accuracy than a VoxelMorph network with the same feature backbone, while being two times faster at inference. |
format | Online Article Text |
id | pubmed-10056872 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-100568722023-03-30 Robust and Realtime Large Deformation Ultrasound Registration Using End-to-End Differentiable Displacement Optimisation Heinrich, Mattias P. Siebert, Hanna Graf, Laura Mischkewitz, Sven Hansen, Lasse Sensors (Basel) Article Image registration for temporal ultrasound sequences can be very beneficial for image-guided diagnostics and interventions. Cooperative human–machine systems that enable seamless assistance for both inexperienced and expert users during ultrasound examinations rely on robust, realtime motion estimation. Yet rapid and irregular motion patterns, varying image contrast and domain shifts in imaging devices pose a severe challenge to conventional realtime registration approaches. While learning-based registration networks have the promise of abstracting relevant features and delivering very fast inference times, they come at the potential risk of limited generalisation and robustness for unseen data; in particular, when trained with limited supervision. In this work, we demonstrate that these issues can be overcome by using end-to-end differentiable displacement optimisation. Our method involves a trainable feature backbone, a correlation layer that evaluates a large range of displacement options simultaneously and a differentiable regularisation module that ensures smooth and plausible deformation. In extensive experiments on public and private ultrasound datasets with very sparse ground truth annotation the method showed better generalisation abilities and overall accuracy than a VoxelMorph network with the same feature backbone, while being two times faster at inference. MDPI 2023-03-07 /pmc/articles/PMC10056872/ /pubmed/36991588 http://dx.doi.org/10.3390/s23062876 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Heinrich, Mattias P. Siebert, Hanna Graf, Laura Mischkewitz, Sven Hansen, Lasse Robust and Realtime Large Deformation Ultrasound Registration Using End-to-End Differentiable Displacement Optimisation |
title | Robust and Realtime Large Deformation Ultrasound Registration Using End-to-End Differentiable Displacement Optimisation |
title_full | Robust and Realtime Large Deformation Ultrasound Registration Using End-to-End Differentiable Displacement Optimisation |
title_fullStr | Robust and Realtime Large Deformation Ultrasound Registration Using End-to-End Differentiable Displacement Optimisation |
title_full_unstemmed | Robust and Realtime Large Deformation Ultrasound Registration Using End-to-End Differentiable Displacement Optimisation |
title_short | Robust and Realtime Large Deformation Ultrasound Registration Using End-to-End Differentiable Displacement Optimisation |
title_sort | robust and realtime large deformation ultrasound registration using end-to-end differentiable displacement optimisation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10056872/ https://www.ncbi.nlm.nih.gov/pubmed/36991588 http://dx.doi.org/10.3390/s23062876 |
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