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J Digit Imaging. 2019 Oct 10. doi: 10.1007/s10278-019-00273-5. [Epub ahead of print]

Automatic Lumbar Spine Tracking Based on Siamese Convolutional Network.

Author information

1
School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China.
2
School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China. sxbhandsome@163.com.
3
Department of Orthopaedics and Traumatology, The University of Hong Kong, Pokfulam, Hong Kong, SAR, China.

Abstract

Deep learning has demonstrated great success in various computer vision tasks. However, tracking the lumbar spine by digitalized video fluoroscopic imaging (DVFI), which can quantitatively analyze the motion mode of the spine to diagnose lumbar instability, has not yet been well developed due to the lack of steady and robust tracking method. The aim of this work is to automatically track lumbar vertebras with rotated bounding boxes in DVFI sequences. Instead of distinguishing vertebras using annotated lumbar images or sequences, we train a full-convolutional siamese neural network offline to learn generic image features with transfer learning. The siamese network is trained to learn a similarity function that compares the labeled target from the initial frame with the candidate patches from 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. Our tracker is performed by evaluating the candidate rotated patches sampled around the previous target's position and presents rotated bounding boxes to locate the lumbar spine from L1 to L4. Results indicate that the proposed tracking method can track the lumbar vertebra steadily and robustly. The study demonstrates that the lumbar tracker based on siamese convolutional network can be trained successfully without annotated lumbar sequences.

KEYWORDS:

Lumbar tracking; Rotation angle estimation; Siamese convolutional network; Similarity learning; Spine motion

PMID:
31602548
DOI:
10.1007/s10278-019-00273-5

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