Abstract
In this paper, we address the problems of fully automatic localization and segmentation of 3D vertebral bodies from CT/MR images. We propose a learning-based, unified random forest regression and classification framework to tackle these two problems. More specifically, in the first stage, the localization of 3D vertebral bodies is solved with random forest regression where we aggregate the votes from a set of randomly sampled image patches to get a probability map of the center of a target vertebral body in a given image. The resultant probability map is then further regularized by Hidden Markov Model (HMM) to eliminate potential ambiguity caused by the neighboring vertebral bodies. The output from the first stage allows us to define a region of interest (ROI) for the segmentation step, where we use random forest classification to estimate the likelihood of a voxel in the ROI being foreground or background. The estimated likelihood is combined with the prior probability, which is learned from a set of training data, to get the posterior probability of the voxel. The segmentation of the target vertebral body is then done by a binary thresholding of the estimated probability. We evaluated the present approach on two openly available datasets: 1) 3D T2-weighted spine MR images from 23 patients and 2) 3D spine CT images from 10 patients. Taking manual segmentation as the ground truth (each MR image contains at least 7 vertebral bodies from T11 to L5 and each CT image contains 5 vertebral bodies from L1 to L5), we evaluated the present approach with leave-one-out experiments. Specifically, for the T2-weighted MR images, we achieved for localization a mean error of 1.6 mm, and for segmentation a mean Dice metric of 88.7% and a mean surface distance of 1.5 mm, respectively. For the CT images we achieved for localization a mean error of 1.9 mm, and for segmentation a mean Dice metric of 91.0% and a mean surface distance of 0.9 mm, respectively.
Highlights
In clinical routine, lower back pain (LBP) caused by spinal disorders is reported as a common reason for clinical visits [1, 2]
In this paper, inspired by the work presented in [8, 23, 29, 30], we propose a learning-based, unified random forest regression and classification framework to tackle the problems of fully automatic localization and segmentation of vertebral bodies (VBs) from a 3D computed tomography (CT) image or a 3D T2-weighted Turbo Spin Echo (TSE) magnetic resonance (MR) image
2) The second dataset contains 10 3D spine CT images and the associated ground truth segmentation [21]. They are freely available from “http://spineweb.digitalimaginggroup.ca/spineweb/index.php?n=Main.Datasets”. The sizes of these CT images are varying from 512 × 512 × 200 to 1024 × 1024 × 323 voxels with intra-slice resolutions between 0.28245 mm and 0.79082 mm and inter-slice distances between 0.725 mm and 1.5284 mm
Summary
Lower back pain (LBP) caused by spinal disorders is reported as a common reason for clinical visits [1, 2]. To identify and label VBs from 3D CT data, Glocker et al [8] proposed a supervised, random forest (RF) regressionbased method [22, 23] Another regression based framework was introduced in [24], where a data-driven regression algorithm was proposed to tackle the problem of localizing IVD centers from 3D T2 weighted MRI data. In this paper, inspired by the work presented in [8, 23, 29, 30], we propose a learning-based, unified random forest regression and classification framework to tackle the problems of fully automatic localization and segmentation of VBs from a 3D CT image or a 3D T2-weighted TSE MR image. The segmentation of the target VB is done by a binary thresholding of the estimated probability
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