Pushing the Limits of an FCN and A CRF Towards Near-Ideal Vertebrae Labelling.
Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/Gutachten › Beitrag in Konferenzband › Beigetragen › Begutachtung
Beitragende
Abstract
In this work, we propose a simple pipeline for labelling vertebrae in a spine CT image composed of a fully convolutional neural network (FCN) and a conditional random field (CRF). Firstly, we adapt the high-resolution network to work on three-dimensional spine CT images and train them with recent advances in deep learning to regress spatial likelihood maps of the vertebral locations. This sets a strong baseline performance for fully automated identification, resulting in a performance comparable to prior state-of-art. Secondly, we employ a prior-informed CRF conditioned on the predicted likelihood maps of the HRNet, thus refining the location predictions. Our custom FCN-CRF solution produces state-of-the-art results in automated labelling tasks for three benchmark datasets achieving identification rates higher than 97%. Finally, we design an interaction module to perform drag-and-drop correction on the CRF output graph. This semi-automated solution achieves near-100% identification with minimal interaction (measured in actions per scan). Code for this work is published at https://github.com/JannikIrmai/interactive-fcn-crf.
Details
Originalsprache | Englisch |
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Titel | 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), Cartagena, Colombia |
Seiten | 1-5 |
Seitenumfang | 5 |
ISBN (elektronisch) | 978-1-6654-7358-3 |
Publikationsstatus | Veröffentlicht - 2023 |
Peer-Review-Status | Ja |
Publikationsreihe
Reihe | IEEE International Symposium on Biomedical Imaging (ISBI) |
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ISSN | 1945-7928 |
Externe IDs
Scopus | 85172096418 |
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ORCID | /0000-0001-5036-9162/work/143781903 |
Mendeley | 35e2ce74-db66-351b-a02d-e14f78f4c3c7 |
Schlagworte
ASJC Scopus Sachgebiete
Schlagwörter
- conditional random fields, fully convolutional neural network, landmark detection, spine, vertebrae