A new data augmentation method based on local image warping for medical image segmentation.

Abstract

The segmentation accuracy of medical images was improved by increasing the number of training samples using a local image warping technique. The performance of the proposed method was evaluated in the segmentation of breast masses, prostate and brain tumors, and lung nodules. We propose a simple data augmentation method which is called stochastic evolution (SE). Specifically, the idea of SE stems from our thinking about the deterioration of the diseased tissue and the healing process. In order to simulate this natural process, we implement it according to the local distortion algorithm in image warping. In other words, the irregular deterioration and healing processes of the diseased tissue is simulated according to the direction of the local distortion, thereby producing a natural sample that is indistinguishable by humans. The proposed method is evaluated on four segmentation tasks of breast masses, prostate, brain tumors, and lung nodules. Comparing the experimental results of four segmentation methods based on the UNet segmentation architecture without adding any expanded data during training, the accuracy and the Hausdorff distance obtained in our approach remain almost the same as other methods. However, the dice similarity coefficient (DSC) and sensitivity (SEN) have both improved to some extent. Among them, DSC is increased by 5.2%, 2.8%, 1.0%, and 3.2%, respectively; SEN is increased by 6.9%, 4.3%, 1.2%, and 4.5%, respectively. Experimental results show that the proposed SE data augmentation method could improve the segmentation accuracy of breast masses, prostate, brain tumors, and lung nodules. The method also shows the robustness with different image datasets and imaging modalities.