2020/05/26 by Liset Vázquez Romaguera, Romaguera, Liset Vázquez, Rosalie Plantefève +3
Computer Science · Engineering · Medicine · #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Medical Image Segmentation Techniques #Medical Imaging and Analysis #Radiomics and Machine Learning in Medical Imaging #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2005.13071
openalex publication_date 2020/05/26 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
In this work we propose a multi-scale recurrent encoder-decoder architecture\nto predict the breathing induced organ deformation in future frames. The model\nwas trained end-to-end from input images to predict a sequence of motion\nlabels. Targets were created by quantizing the displacement fields obtained\nfrom deformable image registration. We report results using MRI free-breathing\nacquisitions from 12 volunteers. Experiments were aimed at investigating the\nproposed multi-scale design and the effect of increasing the number of\npredicted frames on the overall accuracy of the model. The proposed model was\nable to predict vessel positions in the next temporal image with a mean\naccuracy of 2.07 (2.95) mm showing increased performance in comparison with\nstate-of-the-art approaches.\n