# Review of Image Augmentation Used in Deep Learning-Based Material Microscopic Image Segmentation
## 1 Introduction  
The quantitative analysis of microstructures through the microscopic image segmentation method is essential for the control of the material properties and performances of metals or alloys in various industrial applications [1, 2]. In recent years, deep learning-based methods [3], which can automatically learn powerful and beneficial features from large amounts of microscopic image data, have gradually become mainstream in the field of image processing [4, 5, 6]. [PERSON] [7, 8] used the DeepLab module [9] to extract numerous Al-La phase tissues in material microscopic images. [PERSON] [10] used a modified U-Net [11] to extractgrain boundaries in serial sections. These studies tried to provide substantial information for investigating the intrinsic relationships between material composition, preparation processes, and macroscopic properties [12], and ultimately facilitate the optimal design of materials.  
Unfortunately, the accuracy and generalization performance of deep learning models have been hindered by the lack of large training data due to the time-consuming labeling of material microscopic images, which is so-called the insufficient data (or small sample learning) problem [13]. It is still a huge challenge in deep learning, especially for microscopic image segmentation, where it needs to generate pixel-wise semantic labels for each image, which requires a large amount of human effort and expertise to trace the object boundaries accurately. Generally, in cases where large amounts of real data cannot be generated in a short period of time, researchers often use image augmentation methods to expand the data set that is used to train deep learning models.  
Data augmentation, also known as Data enhancement, is a technique for increasing the quantity and diversity of limited data. Unlike other common methods to prevent overfitting, such as pretraining [14], dropout [15], batch normalization [16], and transfer learning [17], data augmentation methods start from the root cause of the overfitting problem, i.e., insufficient training samples, and prevent overfitting by simulating the possible changes in real data through mathematical simulation. As shown in Figure 1, it can extract more generalized information and features from small data sets, increase the size of the data set used to train machine learning models, and finally enhance the precision and generalizability of those models [18].  
With the development of deep learning technology, many different kinds of image augmentation methods have been proposed and are widely used in various image analysis applications. However, as of yet, there is little work that has evaluated the performance of different image augmentation methods in material microscopic image segmentation tasks. In particular, the study focuses on the use of augmentation methods for addressing the issue of few-shot learning in microscopic image analysis [13].  
In this work, we first review commonly used image augmentation methods and divide more than 60 basic image augmentation methods into eleven categories based on different implementation strategies. Secondly, we conduct experiments to verify the effectiveness of various basic image augmentation for the image segmentation task of two classical material microscopic images using evaluation metrics with different applicabilities. Then, we discuss the advantages and applicability of various image augmentation methods in the material microscopic image segmentation task. The evaluation experiments and conclusions in this work can serve as a guide for the creation of intelligent modeling frameworks in the materials industry.  
Figure 1: Image data augmentation used in deep learning-based material microscopic image segmentation.