Improving the performance of V-net architecture for volumetric medical image segmentation by implementing a gradient pre-processor
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AbstractApplications of deep learning models and Convolutional Neural Network (CNN) have been achieving good performance in 3D medical image analysis. In this study, we present a fast and efficient 3D femur segmentation method based on V-net with a gradient pre-processor. Instead of feeding CT data into a standard V-net model, our proposed model is fed with gradient data based on CT scans using a gradient pre-processor, which forces the network to learn from the gradient field. Adopting an objective function based on dice similarity coefficient, the imbalance between the numbers of femur voxels against that of background could be addressed. A dataset of lower limb CT data with 60 samples was trained and tested on a pure V-net model and the proposed V-net model separately. Experimental results show that our proposed method could achieve better segmentation results (1.4% improvement in dice similarity coefficient) and a higher robustness as compared with the pure V-net model, which allows faster training speed and higher segmentation accuracy.
Acceptance Date06/12/2021
All Author(s) ListElvis Chun-Sing Chui, Lawrence Chun-Man Lau, Ka-Bon Kwok, Xin Ye, Lik-Hang Hung, Kwoon-Ho Chow, Ronald Man-Yeung Wong, Edmond Wing-Fung Yau, Sheung-Wai Law, Wing-Hoi Cheung, Patrick Shu-Hang Yung
Journal nameClinics In Surgery
Year2021
Month12
Day14
Volume Number6
Issue Number1
PublisherRemedy Publications
Place of PublicationUSA
Article number3383
ISSN2474-1647
LanguagesEnglish-United States
KeywordsDeep Learning, Convolutional Neural Network, Volumetric Segmentation, V-net, Gradient pre-processor, Femur
