# A Deep Learning Approach to Semantic Segmentation of Steel Microstructures
## 1 Introduction
The study utilizes a correlative microscopy technique, combining a light optical microscope (LOM), a scanning electron microscope (SEM), and electron backscatter diffraction (EBSD). The detection of PAGs is accomplished through semantic segmentation using advanced deep learning (DL) methods, specifically U-NET in conjunction with DenseNet, applied to LOM images. To ensure effective model evaluation, the authors emphasize the critical importance of accurately measuring grain sizes in the metallurgical structure of the material. Their experiments reveal an IoU of around 70%, indicating potential discrepancies between metric values and visual perception of model quality. Recognizing the limitations of traditional metrics like IoU and pixel accuracy, particularly in the context of grain size measurement within segmentation tasks, they propose a novel approach. To address this, they introduce a method for quantifying grain size distribution from segmentation maps, calculating the mean, median, and standard deviation. By binning detected grains into intervals of a specific width (500 um\({}^{2}\)) and calculating probability density, they accurately assess segmentation quality compared with values of the ground truth and identify potential errors in grain size determination. The results show a mean error of 6.1% in average grain size, underscoring the high quality of the DL model. [PERSON] et al. [12] introduced a segmentation method (CES) based on the extraction of center-environment features tailored for small material image samples. The proposed method is applied to several datasets that include carbon steels, titanium alloy, wood, and cross-sectional morphology of Pt-Al and WC-Co coating image data. Expert annotators are engaged in the process, drawing region-specific curves based on their domain knowledge. Additionally, the method takes advantage of several machine learning algorithms to achieve highly accurate segmentation. Notably, the results of the study indicate that the Gradient Boosting Decision Tree (GBDT) outperforms other methods in this context. Additionally, a comparison is made with segmentation methods based on deep learning networks such as SegNet, PSPNet, and UNet++, which are found to be 10% higher in IoU and mean IoU metrics compared to the methodology used by the authors. This difference is attributed to the significantly fewer pixels annotated to create the masks using CES compared to deep learning methods. While the proposed method is commendable for its innovative approach and reduced annotation cost, it falls short in achieving comparable segmentation accuracy to deep learning algorithms. The observed 10% disparity in results highlights the limitations of this method, suggesting that a balance between annotation efficiency and segmentation performance has yet to be fully realized. [PERSON] et al. [13] displayed the segmentation of a low-carbon steel microstructure without the need for labeled images, employing a deep learning approach. Specifically, a convolutional neural network combined with the Simple Linear Iterative Clustering (SLIC) superpixel algorithm. By leveraging a diverse range of microstructure optical images containing ferrite, pearlite, bainite, and martensite, the model effectively distinguished and delineated regions corresponding to each constituent phase. [PERSON] et al. [14] trained a U-Net model to perform the segmentation of bainite, ferrite and martensite on EBSD maps using the kernel average misorientation and the pattern quality index as input. The model can differentiate the three constituents with a 92% mean accuracy in the test results. [PERSON] et al. [15] proposed a versatile approach for classifying multiphase steels. It involves generating 3D polycrystalline microstructure templates using the Johnson-Mehl-Avrami-Kolmogorov (JMAK) kinetic model, creating realistic single-phase microstructures through nucleation and growth concepts.