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Proceedings Paper

Siamese convolutional networks for tracking the spine motion
Author(s): Yuan Liu; Xiubao Sui; Yicheng Sun; Chengwei Liu; Yong Hu
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Paper Abstract

Deep learning models have demonstrated great success in various computer vision tasks such as image classification and object tracking. However, tracking the lumbar spine by digitalized video fluoroscopic imaging (DVFI), which can quantitatively analyze the motion mode of spine to diagnose lumbar instability, has not yet been well developed due to the lack of steady and robust tracking method. In this paper, we propose a novel visual tracking algorithm of the lumbar vertebra motion based on a Siamese convolutional neural network (CNN) model. We train a full-convolutional neural network offline to learn generic image features. The network is trained to learn a similarity function that compares the labeled target in the first frame with the candidate patches in the current frame. The similarity function returns a high score if the two images depict the same object. Once learned, the similarity function is used to track a previously unseen object without any adapting online. In the current frame, our tracker is performed by evaluating the candidate rotated patches sampled around the previous frame target position and presents a rotated bounding box to locate the predicted target precisely. Results indicate that the proposed tracking method can detect the lumbar vertebra steadily and robustly. Especially for images with low contrast and cluttered background, the presented tracker can still achieve good tracking performance. Further, the proposed algorithm operates at high speed for real time tracking.

Paper Details

Date Published: 19 September 2017
PDF: 7 pages
Proc. SPIE 10396, Applications of Digital Image Processing XL, 103961Y (19 September 2017); doi: 10.1117/12.2272168
Show Author Affiliations
Yuan Liu, Nanjing Univ. of Science and Technology (China)
Xiubao Sui, Nanjing Univ. of Science and Technology (China)
Yicheng Sun, Nanjing Univ. of Science and Technology (China)
Chengwei Liu, Nanjing Univ. of Science and Technology (China)
Yong Hu, The Univ. of Hong Kong (China)

Published in SPIE Proceedings Vol. 10396:
Applications of Digital Image Processing XL
Andrew G. Tescher, Editor(s)

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