Coarse to Fine Vertebrae Localization and Segmentation with SpatialConfiguration-Net and U-Net.

Christian Payer, Darko Stern, Horst Bischof, Martin Urschler

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

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).

Original languageEnglish
Title of host publicationVISAPP
EditorsGiovanni Maria Farinella, Petia Radeva, Jose Braz
Pages124-133
Number of pages10
ISBN (Electronic)9789897584022
DOIs
Publication statusPublished - 1 Jan 2020
Event16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications: VISIGRAPP 2021 - Virtuell, Austria
Duration: 8 Feb 202110 Feb 2021

Conference

Conference16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
CountryAustria
CityVirtuell
Period8/02/2110/02/21

Keywords

  • SpatialConfiguration-Net
  • U-Net
  • VerSe 2019 Challenge
  • Vertebrae Localization
  • Vertebrae Segmentation

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

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