Abstract
PurposeMedical 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.MethodsThis 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.ResultsCombined 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.ConclusionCompared with the traditional methods, the proposed data augmentation method has better performance in single tumor segmentation.
Highlights
As we all know, cancer accounts for a large proportion of the diseases causing premature human death if noncommunicable conditions are not taken into account, and this trend will continue or increase in the future [1]
The flow chart of the method studied in this paper is shown in Figure 1, which mainly includes image cutting and mirroring augmentation, deep learning segmentation, and boundary reconstruction
In addition to the data augmentation method proposed in this paper, the traditional data augmentation methods were used as the reference group
Summary
Cancer accounts for a large proportion of the diseases causing premature human death if noncommunicable conditions are not taken into account, and this trend will continue or increase in the future [1]. The prevention and treatment of cancer through early detection can effectively reduce mortality [2, 3]. Tumor Image Augmentation Method essential role in tumor screening, diagnosis, and treatment [4]. With the explosive development of deep learning in various application directions, medical imaging and medical image analysis have been widely concerned [6]. The weighted model through deep learning has been constructed to explore the deep features of the tumor image and further help for tumor target detection, classification, and prognosis prediction of unknown medical images, which can more effectively assist doctors in clinical diagnosis [7,8,9,10]
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