ImageGrains 2.0: Improved precision and generalization for grain segmentation

Abstract. Recent advances in deep-learning–based image segmentation have enabled the development of automated approaches to detect individual grains and measure them for geoscientific applications. These methods facilitate the creation of much larger and more precise datasets than traditional manual grain measurements. However, they typically perform best as specialized models trained on homogeneous, task-specific datasets, and often show reduced accuracy when used to generalize to different data types.

Here, we present an updated framework, ImageGrains 2.0 that leverages Cellpose-SAM, a recently published next-generation deep-learning model originally developed for cell segmentation in biomedical research. It currently represents the state of the art for dense segmentation in 2D and 3D biomedical datasets, and yields robust, and is capable to generalize across distinctly different image datasets. These properties allow us to re-train the model with geoscientific dataset comprising annotated images of fluvial gravel, coarse pro-glacial deposits, and X-ray computer tomography scans of glacial till and marine sand. We benchmark the segmentation performance of the method against ground-truth annotations, compare it to the performance of other segmentation methods, and we evaluate measurement accuracy. Our results indicate that this approach outperforms existing methods and confirm that the outstanding performance of Cellpose-SAM is transferable to segment sediment grains. We analyze the size and shape of these segmented grains and find that an increase in grain segmentation accuracy leads to more precise and realistic morphometric results, e.g., more accurate grain size distributions. Additionally, we introduce an interactive graphical user interface for image annotation and correction of model predictions, facilitating the use of the framework in a broader range of image settings. Furthermore, this study underscores the importance of curating of more publicly available datasets, which could pave the way towards the generation of a foundation model for segmenting granular particles in geoscientific imagery.