Schobs, L., Zhou, S., Cogliano, M. et al. (2 more authors) (2021) Confidence-quantifying landmark localisation for cardiac MRI. In: Proceedings of the IEEE International Symposium on Biomedical Imaging (ISBI 2021). IEEE International Symposium on Biomedical Imaging (ISBI 2021), 13-16 Apr 2021, Virtual conference. IEEE , pp. 985-988. ISBN 9781665429474
Abstract
Landmark localisation in medical imaging has achieved great success using deep encoder-decoder style networks to regress heatmap images centered around the target landmarks. However, these networks are large and computationally expensive. Moreover, their clinical use often requires human interaction, opening the door for manual correction of low confidence predictions. We propose PHD-Net: a lightweight, multi-task Patch-based network combining Heatmap and Displacement regression. We design a simple Candidate Smoothing strategy to fuse its two-task outputs, generating the final prediction with quantified confidence. We evaluate PHD-Net on hundreds of Short Axis and Four Chamber cardiac MRIs, showing promising results.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Landmark localisation; confidence; cardiac MRI; patch-based method; multi-task learning |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Funding Information: | Funder Grant number Engineering and Physical Science Research Council 2274702 The Wellcome Trust 215799/Z/19/Z and 205188/Z/16/Z |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 12 Feb 2021 09:49 |
Last Modified: | 25 May 2022 00:38 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/ISBI48211.2021.9433895 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:170857 |