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Three-dimensional reconstruction of internal fascicles of human peripheral nerve
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Paper Abstract

Three-dimensional reconstruction of nerve fascicle is important in the analysis of biological characteristics in the arm. The topology of fascicle has been used by doctors to investigate the nerve direction and the relationship between the individual nerve fascicle. However, there still does not exist an ideal internal fascicle and 3D model in the human peripheral nerve. Accurate segmentation of fascicle from CT images is a crucial step to obtain reliable 3D nerve fascicle model. Traditional method in the fascicle segmentation is not efficient due to time consuming, manual work and poor generalization capacity. In this study, we proposed an efficient deep segmentation network and then reconstruct 3D nerve fascicle model. The proposed network explores the intra-slice contextual features with convolutional long short-term memory for accurate fascicle segmentation, and model long-range semantic information among image slices. Transfer learning technique is integrated with ResNet34, and the discriminative capability of intermediate features are further improved. The proposed network architecture is efficient, flexible and suitable for separating the adhesive fascicle. Our approach is the first deep learning method for nerves segmentation. The proposed approach achieves state-of-the-art performance on our dataset, where the mean Dice of our method is 95.4% and at least 5% more than other methods.

Paper Details

Date Published: 8 March 2019
PDF: 6 pages
Proc. SPIE 10951, Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling, 109512G (8 March 2019); doi: 10.1117/12.2512380
Show Author Affiliations
Shouliang Liu, Sun Yat-Sen Univ. (China)
Yanhui Guo, Univ. of Illinois at Springfield (United States)
Liwei Yan, The First Affiliated Hospital of Sun Yat-Sen Univ. (China)
Yao Lu, Sun Yat-Sen Univ. (China)


Published in SPIE Proceedings Vol. 10951:
Medical Imaging 2019: Image-Guided Procedures, Robotic Interventions, and Modeling
Baowei Fei; Cristian A. Linte, Editor(s)

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