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Proceedings Paper

Vertebrae segmentation via stacked sparse autoencoder from computed tomography images
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Paper Abstract

An accurate vertebrae segmentation in the spine is an essential pre-requisite in many applications of image-based spine assessment, surgical planning, clinical diagnostic treatment, and biomechanical modeling. In this paper, we present the stacked sparse autoencoder (SSAE) model for the segmentation of vertebrae from CT images. After the preprocessing step, we extracted overlapped patches from the vertebrae CT images as the inputs of our proposed model. The SSAE model was trained in an unsupervised way to learn high-level features from the input pixels of the unlabeled images patch. To improve the discriminability of the learned features, we further refined the feature representation in a supervised fashion and fine-tuned the whole model by using the feedforward neural network parameters for classifying the overlapped patches. We then validated our model on a publicly available MICCAI CSI2014 dataset and found that our model outperforms the other state-of-the-art methods.

Paper Details

Date Published: 14 August 2019
PDF: 6 pages
Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 111794K (14 August 2019); doi: 10.1117/12.2540176
Show Author Affiliations
Syed Furqan Qadri, Beijing Institute of Technology (China)
Zhiqi Zhao, Beijing Institute of Technology (China)
Danni Ai, Beijing Institute of Technology (China)
Mubashir Ahmad, Beijing Institute of Technology (China)
Yongtian Wang, Beijing Institute of Technology (China)

Published in SPIE Proceedings Vol. 11179:
Eleventh International Conference on Digital Image Processing (ICDIP 2019)
Jenq-Neng Hwang; Xudong Jiang, Editor(s)

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