###### Abstract
(2018) devised a semi-automatic algorithm based on the Image Foresting Transform (IFT). [PERSON] et al. [PERSON] et al. (2019) introduced a super pixel-based labeling approach to create a dataset for deep learning-based segmentation algorithms. The proposed approach utilizes super pixel-based domain similarities for labeling different minerals in rocks. [PERSON] et al. [PERSON] et al. (2018) proposed a deep learning-based approach for sandstone image segmentation. In the segmentation process, VGG16 and ResNet18 were employed in the backbone of DeepLabV3+, with experimental results showing better performance with ResNet18. [PERSON] et al. [PERSON] et al. (2019) compared image processing and deep learning-based methods for segmenting sandstone thin-section images. For this purpose, a Mask-RCNN-based approach was employed on the deep learning side, determining the rock type based on the size of minerals in the segmented image. [PERSON] et al. [PERSON] et al. (2019) introduced a multi-scale edge detection approach to identify rock types based on the size of mineral grains in petrographic thin-section images. The proposed approach was tested on a cross-polarized petrographic image dataset, successfully distinguishing mineral boundaries. A fuzzy rule-based approach is proposed for thin-section rock image segmentation [PERSON] et al. (2019). This approach takes a thin-section image in RGB format as input and produces the segmented image. It is also compared with fuzzy clustering to demonstrate its effectiveness. [PERSON] and [PERSON] [PERSON] and [PERSON] (2019) proposed an auto encoder-based approach for the automatic segmentation of digital rock images. Using a SegNet-based approach for segmentation, rocks were segmented with high accuracy. However, the rock types used are not complex and easy to distinguish. [PERSON] et al. [PERSON] et al. (2019) presented an approach for segmenting thin-section sandstone rocks using a multi-channel attention transformer. To overcome the problem of small sample sizes, data augmentation was performed using the copy-paste method. A new multi-channel attention module was developed to integrate the correlation of image sequences acquired from different lighting angles. [PERSON] et al. [PERSON] et al. (2019)proposed a new method that combines Study-Smoothed Score Class Activation Mapping (SSCAM) and Restricted Similarity Dual-Contrast Learning (RSimDCL) to address the segmentation accuracy issues of sandstone thin-section images, which are affected by high data labeling cost and manual bias. The proposed approach reduces the data annotation workload and avoids manual bias. [PERSON] et al. (2018) studied the effect of using three different spatial filters to address the problem of automatic grain-matrix segmentation in cross-polarized images of sedimentary rock thin sections. The proposed method provides grain segmentation with higher accuracy compared to existing methods, especially when dealing with challenging images with small grains. With the methods available and utilized in the literature, it is not always possible to identify most modified mineral boundaries in thin sections. Existing techniques for mineral segmentation, in thin sections containing partially modified minerals that have not retained their crystal shape or texture, are not highly accurate. Rocks comprise various mineral types, each possessing distinct physical properties. Identifying these minerals and determining their proportions within the rock structure pose significant challenges. Atmospheric conditions, humidity, and diurnal temperature variations, in particular, influence the external appearance and mineral composition of rocks. These external factors impact the strength and characteristics of minerals and rocks, making their quantitative identification more difficult. Thin sections of rocks encompass properties such as mineral composition, color, grain shape, grain size, weathering, structure, and other characteristics ([PERSON] et al., 2018).