3D Intervertebral Disc Localization and Segmentation from MR Images by Data-Driven Regression and Classification

Chu, Chengwen; Belavy, D.; Zheng, Guoyan (2014). 3D Intervertebral Disc Localization and Segmentation from MR Images by Data-Driven Regression and Classification. In: Wu, Guorong; Zhang, Daoqiang; Zhou, Luping (eds.) MLMI 2014, LNCS 8679. Lecture Notes in Computer Science: Vol. 8679 (pp. 50-58). Cham: Springer 10.1007/978-3-319-10581-9_7

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In this paper we propose a new fully-automatic method for
localizing and segmenting 3D intervertebral discs from MR images, where
the two problems are solved in a unified data-driven regression and classification
framework. We estimate the output (image displacements for
localization, or fg/bg labels for segmentation) of image points by exploiting
both training data and geometric constraints simultaneously.
The problem is formulated in a unified objective function which is then
solved globally and efficiently. We validate our method on MR images
of 25 patients. Taking manually labeled data as the ground truth, our
method achieves a mean localization error of 1.3 mm, a mean Dice metric
of 87%, and a mean surface distance of 1.3 mm. Our method can be
applied to other localization and segmentation tasks.

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