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http://dx.doi.org/10.15701/kcgs.2019.25.3.123

Data Augmentation Method for Deep Learning based Medical Image Segmentation Model  

Choi, Gyujin (Department of Digital Media, Ajou University)
Shin, Jooyeon (Department of Digital Media, Ajou University)
Kyung, Joohyun (Department of Digital Media, Ajou University)
Kyung, Minho (Department of Digital Media, Ajou University)
Lee, Yunjin (Department of Digital Media, Ajou University)
Publication Information
Journal of the Korea Computer Graphics Society / v.25, no.3, 2019 , pp. 123-131 More about this Journal
Abstract
In this study, we modified CT images of femoral head in consideration of anatomically meaningful structure, proposing the method to augment the training data of convolution Neural network for segmentation of femur mesh model. First, the femur mesh model is obtained from the CT image. Then divide the mesh model into meaningful parts by using cluster analysis on geometric characteristic of mesh surface. Finally, transform the segments by using an appropriate mesh deformation algorithm, then create new CT images by warping CT images accordingly. Deep learning models using the data enhancement methods of this study show better image division performance compared to data augmentation methods which have been commonly used, such as geometric conversion or color conversion.
Keywords
Data augmentation; Deep learning; Medical image; Image segmentation;
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