Highly accurate deep registration networks for large deformation estimation in compression ultrasound

Laura F. Graf; Hanna Siebert; Sven Mischkewitz; Ron Keuth; Mattias P. Heinrich

doi:10.1117/12.2653870

3 April 2023 Highly accurate deep registration networks for large deformation estimation in compression ultrasound

Laura F. Graf, Hanna Siebert, Sven Mischkewitz, Ron Keuth, Mattias P. Heinrich

Proceedings Volume 12464, Medical Imaging 2023: Image Processing; 1246426 (2023) https://doi.org/10.1117/12.2653870
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

AI guidance on compression ultrasound is a problem for 2D segmentation networks, which produce inconsistent labels. This is aided by registration but classical untrained approaches cannot handle the large deformations and the noisy background movement and convolutional models like VoxelMorph do not reach the robust accuracy required. Meanwhile, large deformations are typically estimated with multi-warp networks that comprise "correlation layers", but they are resource-intensive and not easily applicable on end-devices in clinical context. We propose to replace the "correlation layer" with a differentiable convex optimisation block and perform end-to-end training of the convolutional feature backbone for improved performance.

Citation Download Citation

Laura F. Graf, Hanna Siebert, Sven Mischkewitz, Ron Keuth, and Mattias P. Heinrich "Highly accurate deep registration networks for large deformation estimation in compression ultrasound", Proc. SPIE 12464, Medical Imaging 2023: Image Processing, 1246426 (3 April 2023); https://doi.org/10.1117/12.2653870

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