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 tum1or 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. The deep learning network is used to train the augmented data and get the corresponding segmentation. 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 & vertical cutting and mirroring augmentation and boundary reconstruction improved by 9.66% and 12.43% compared with no data augmentation. And the DSC obtained by diagonal cutting and mirroring augmentation and boundary reconstruction method is 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.