Automatic segmentation of thoracic aorta segments in low-dose chest CT

Julia M.H. Noothout, Bob D. De Vos, Jelmer M. Wolterink, Ivana Išgum

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

9 Citations (Scopus)

Abstract

Morphological analysis and identification of pathologies in the aorta are important for cardiovascular diagnosis and risk assessment in patients. Manual annotation is time-consuming and cumbersome in CT scans acquired without contrast enhancement and with low radiation dose. Hence, we propose an automatic method to segment the ascending aorta, the aortic arch and the thoracic descending aorta in low-dose chest CT without contrast enhancement. Segmentation was performed using a dilated convolutional neural network (CNN), with a receptive field of 131 × 131 voxels, that classified voxels in axial, coronal and sagittal image slices. To obtain a final segmentation, the obtained probabilities of the three planes were averaged per class, and voxels were subsequently assigned to the class with the highest class probability. Two-fold cross-validation experiments were performed where ten scans were used to train the network and another ten to evaluate the performance. Dice coefficients of 0.83 ± 0.07, 0.86 ± 0.06 and 0.88 ± 0.05, and Average Symmetrical Surface Distances (ASSDs) of 2.44 ± 1.28, 1.56 ± 0.68 and 1.87 ± 1.30 mm were obtained for the ascending aorta, the aortic arch and the descending aorta, respectively. The results indicate that the proposed method could be used in large-scale studies analyzing the anatomical location of pathology and morphology of the thoracic aorta.

Original languageEnglish
Title of host publicationMedical Imaging 2018
Subtitle of host publicationImage Processing
EditorsElsa D. Angelini, Elsa D. Angelini, Bennett A. Landman
PublisherSPIE Press
ISBN (Electronic)9781510616370
DOIs
Publication statusPublished - 1 Jan 2018
Externally publishedYes
EventSPIE Medical Imaging 2018: Ultrasonic Imaging and Tomography - Marriott Marquis Houston, Houston, United States
Duration: 10 Feb 201815 Feb 2018

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume10574
ISSN (Print)1605-7422

Conference

ConferenceSPIE Medical Imaging 2018
CountryUnited States
CityHouston
Period10/02/1815/02/18

Keywords

  • Aorta segmentation
  • Aortic arch
  • Ascending aorta
  • Descending aorta
  • Dilated convolutional neural network
  • Low-dose chest CT

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