Iron ore is found in nature as an aggregate of minerals. Among the main minerals in its composition are hematite, magnetite, goethite, and quartz. Given the importance of iron ore for the industry, there is a growing interest in its characterization to assess the material s quality. With the advancement of image analysis and microscopy research, characterization routines were developed using Digital Microscopy and Digital Image Processing and Analysis tools capable of automating a large part of the process. However, it encountered some difficulties, such as identifying and classifying the different textures of hematite particles, the different shapes of its crystals, or discriminating between quartz and resin in optical microscopy images of reflected light. Therefore, from the need to build systems capable of learning and adapting to possible variations of the images of this material, the possibility of studying the use of Deep Learning tools for this function arose. This work proposes developing a new mineral characterization methodology based on Deep Learning using the Mask R-CNN algorithm. Through this, it is possible to perform instance segmentation, that is, to develop systems capable of identifying, classifying, and segmenting objects in images. In this work, two models were developed: Model 1 performs segmentation of instances for the compact, porous, martite, and goethite classes in images obtained in Bright Field, and Model 2 uses images acquired in Circularly Polarized Light to segment the classes monocrystalline, polycrystalline and martite. For Model 1, F1-score was obtained around 80 percent, and for Model 2, around 90 percent. From the class segmentation, it was possible to extract important attributes of each particle, such as quantity distribution, shape measurements, size, and area fraction. The obtained results were very promising and indicated that the developed methodology could be viable for such characterization.