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International Journal of Computer Assisted Radiology and Surgery

Erschienen in:

01.09.2018 | Original Article

Fully automatic segmentation of paraspinal muscles from 3D torso CT images via multi-scale iterative random forest classifications

verfasst von: Naoki Kamiya, Jing Li, Masanori Kume, Hiroshi Fujita, Dinggang Shen, Guoyan Zheng

Erschienen in: International Journal of Computer Assisted Radiology and Surgery | Ausgabe 11/2018

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Abstract

Purpose

To develop and validate a fully automatic method for segmentation of paraspinal muscles from 3D torso CT images.

Methods

We propose a novel learning-based method to address this challenging problem. Multi-scale iterative random forest classifications with multi-source information are employed in this study to speed up the segmentation and to improve the accuracy. Here, multi-source images include the original torso CT images and later also the iteratively estimated and refined probability maps of the paraspinal muscles. We validated our method on 20 torso CT data with associated manual segmentation. We randomly partitioned the 20 CT data into two evenly distributed groups and took one group as the training data and the other group as the test data.

Results

The proposed method achieved a mean Dice coefficient of 93.0%. It took on average 46.5 s to segment a 3D torso CT image with the size ranging from \(512 \times 512 \times 802\) voxels to \(512 \times 512 \times 1031\) voxels.

Conclusions

Our fully automatic, learning-based method can accurately segment paraspinal muscles from 3D torso CT images. It generates segmentation results that are better than those achieved by the state-of-the-art methods.

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Titel: Fully automatic segmentation of paraspinal muscles from 3D torso CT images via multi-scale iterative random forest classifications
verfasst von: Naoki Kamiya
Jing Li
Masanori Kume
Hiroshi Fujita
Dinggang Shen
Guoyan Zheng
Publikationsdatum: 01.09.2018
Verlag: Springer International Publishing
Erschienen in: International Journal of Computer Assisted Radiology and Surgery / Ausgabe 11/2018
Print ISSN: 1861-6410
Elektronische ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-018-1852-1

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Abstract

Purpose

Methods

Results

Conclusions

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