KAIST / 김선호, 박현욱*
Abstract
Purpose: Medical image analysis using deep neural networks has been actively studied. For accurate training of deep neural networks, the learning data should be sufficient and have good quality and generalized characteristics. However, in medical images, it is difficult to acquire sufficient patient data because of the difficulty of patient recruitment, the burden of annotation of lesions by experts, and the invasion of patients' privacy. In comparison, the medical images of healthy volunteers can be easily acquired. To resolve this data bias problem, the proposed method synthesizes brain tumor images from normal brain images.
Methods: Our method can synthesize a huge number of brain tumor multicontrast MR images from numerous healthy brain multicontrast MR images and various concentric circles. Because tumors have complex characteristics, the proposed method simplifies them into concentric circles that are easily controllable. Then, it converts the concentric circles into various realistic tumor masks through deep neural networks. The tumor masks are used to synthesize realistic brain tumor images from normal brain images.
Results: We performed a qualitative and quantitative analysis to assess the usefulness of augmented data from the proposed method. Data augmentation by the proposed method provided significant improvements to tumor segmentation compared with other GAN-based methods. Intuitive experimental results are available online at https://github.com/KSH0660/BrainTumor.
Conclusions: The proposed method can control the grade tumor masks by the concentric circles, and synthesize realistic brain tumor multicontrast MR images. In terms of data augmentation, the proposed method can successfully synthesize brain tumor images that can be used to train tumor segmentation networks or other deep neural networks.
Affiliation
Sunho Kim 1 , Byungjai Kim 1 , HyunWook Park 1
1 School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea.
편집위원
딥러닝을 위한 훈련영상을 마련하기가 힘들 때 특히 의료영상으로 훈련을 할 때 다양한 많은 훈련영상을 확보하기 힘든 경우를 대비하여 딥러닝을 이용하여 종양이 있는 의료영상을 만드는 기술을 다루고 있다. 딥러닝을 위한 훈련영상을 딥러닝으로 만드는 세상이 오는 걸까...
2021-06-30 16:11:28