A Deep Learning Image Data Augmentation Method for Single Tumor Segmentation - Zhang, Chunling; Bao, Nan; Sun, Hang; Li, Hong; Li, Jing; Qian, Wei; Zhou, Shi (Frontiers in Oncology, 2022)
Purpose Medical imaging examination is the primary method of diagnosis, treatment, and prevention of cancer. However, the amount of medical image data is often not enough to meet deep learning needs. This article aims to expand the small data set in tumor segmentation based on the deep learning method. Methods This method includes three main parts: image cutting and mirroring augmentation, segmentation of augmented images, and boundary reconstruction. Firstly, the image is divided into four parts horizontally & vertically, and diagonally along the tumor’s approximate center. Then each part is mirrored to get a new image and hence a four times data set. Next, the deep learning network trains the augmented data and gets the corresponding segmentation model. Finally, the segmentation boundary of the original tumor is obtained by boundary compensation and reconstruction. Results Combined with Mask-RCNN and U-Net, this study carried out experiments on a public breast ultrasound data set. The results show that the dice similarity coefficient (DSC) value obtained by horizontal and vertical cutting and mirroring augmentation and boundary reconstruction improved by 9.66% and 12.43% compared with no data augmentation. Moreover, the DSC obtained by diagonal cutting and mirroring augmentation and boundary reconstruction method improved by 9.46% and 13.74% compared with no data augmentation. Compared with data augmentation methods (cropping, rotating, and mirroring), this method’s DSC improved by 4.92% and 12.23% on Mask-RCNN and U-Net. Conclusion Compared with the traditional methods, the proposed data augmentation method has better performance in single tumor segmentation.