@inproceedings{
 title = {Coarse to fine vertebrae localization and segmentation with spatialconfiguration-Net and U-Net},
 type = {inproceedings},
 year = {2020},
 keywords = {SpatialConfiguration-Net,U-Net,VerSe 2019 Challenge,Vertebrae Localization,Vertebrae Segmentation},
 id = {cddfbc84-8eb0-3c03-b1bc-1c5b63a8944c},
 created = {2020-06-20T01:37:12.778Z},
 file_attached = {false},
 profile_id = {53d1e3c7-2f16-3c81-9a84-dccd45be4841},
 last_modified = {2020-06-20T01:37:12.778Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Payer2020},
 private_publication = {false},
 abstract = {Localization and segmentation of vertebral bodies from spine CT volumes are crucial for pathological diagnosis, surgical planning, and postoperative assessment. However, fully automatic analysis of spine CT volumes is difficult due to the anatomical variation of pathologies, noise caused by screws and implants, and the large range of different field-of-views. We propose a fully automatic coarse to fine approach for vertebrae localization and segmentation based on fully convolutional CNNs. In a three-step approach, at first, a U-Net localizes the rough position of the spine. Then, the SpatialConfiguration-Net performs vertebrae localization and identification using heatmap regression. Finally, a U-Net performs binary segmentation of each identified vertebrae in a high resolution, before merging the individual predictions into the resulting multi-label vertebrae segmentation. The evaluation shows top performance of our approach, ranking first place and winning the MICCAI 2019 Large Scale Vertebrae Segmentation Challenge (VerSe 2019).},
 bibtype = {inproceedings},
 author = {Payer, Christian and Štern, Darko and Bischof, Horst and Urschler, Martin},
 doi = {10.5220/0008975201240133},
 booktitle = {VISIGRAPP 2020 - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications}
}

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