Iterative convolutional neural networks for automatic vertebra identification and segmentation in CT images

Nikolas Lessmann; Bram van Ginneken; Ivana Išgum

doi:10.1117/12.2292731

2 March 2018 Iterative convolutional neural networks for automatic vertebra identification and segmentation in CT images

Nikolas Lessmann, Bram van Ginneken, Ivana Išgum

Author Affiliations +

Proceedings Volume 10574, Medical Imaging 2018: Image Processing; 1057408 (2018) https://doi.org/10.1117/12.2292731
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

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.

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Nikolas Lessmann, Bram van Ginneken, and Ivana Išgum "Iterative convolutional neural networks for automatic vertebra identification and segmentation in CT images", Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 1057408 (2 March 2018); https://doi.org/10.1117/12.2292731

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