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Proceedings Paper

Iterative convolutional neural networks for automatic vertebra identification and segmentation in CT images
Author(s): Nikolas Lessmann; Bram van Ginneken; Ivana Išgum
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Paper Abstract

Segmentation and identification of the vertebrae in CT images are important steps for automatic analysis of the spine. This paper presents an automatic method based on iterative convolutional neural networks. These utilize the inherent order of the vertebral column to simplify the detection problem, so that the network can be trained with as little as ten manual reference segmentations. Vertebrae are segmented and identified one- by-one in sequential order, using an iterative procedure. Vertebrae are first roughly localized and identified in low-resolution images that enable the analysis of context information, and afterwards reanalyzed in the original high-resolution images to obtain a fine segmentation. The method was trained and evaluated with 15 spine CT scans from the MICCAI CSI 2014 workshop challenge. These scans cover the whole thoracic and lumbar part of the spine of healthy young adults. In contrast to a non-iterative convolutional neural network, which made labeling mistakes, the proposed iterative method correctly identified all vertebrae. Our method achieved a mean Dice coefficient of 0.948 and a mean surface distance of 0.29 mm and thus outperforms the best method that participated in the original challenge.

Paper Details

Date Published: 2 March 2018
PDF: 6 pages
Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 1057408 (2 March 2018); doi: 10.1117/12.2292731
Show Author Affiliations
Nikolas Lessmann, Univ. Medical Ctr. Utrecht (Netherlands)
Bram van Ginneken, Radboud Univ. Medical Ctr. (Netherlands)
Ivana Išgum, Univ. Medical Ctr. Utrecht (Netherlands)


Published in SPIE Proceedings Vol. 10574:
Medical Imaging 2018: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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