Zero-Shot Digital Rock Image Segmentation with a Fine-Tuned Segment
  Anything Model
Zhaoyang Ma Xupeng He Shuyu Sun Bicheng Yan Hyung Kwak Jun Gao
Accurate image segmentation is crucial in reservoir modelling and material
characterization, enhancing oil and gas extraction efficiency through detailed
reservoir models. This precision offers insights into rock properties,
advancing digital rock physics understanding. However, creating pixel-level
annotations for complex CT and SEM rock images is challenging due to their size
and low contrast, lengthening analysis time. This has spurred interest in
advanced semi-supervised and unsupervised segmentation techniques in digital
rock image analysis, promising more efficient, accurate, and less
labour-intensive methods. Meta AI's Segment Anything Model (SAM) revolutionized
image segmentation in 2023, offering interactive and automated segmentation
with zero-shot capabilities, essential for digital rock physics with limited
training data and complex image features. Despite its advanced features, SAM
struggles with rock CT/SEM images due to their absence in its training set and
the low-contrast nature of grayscale images. Our research fine-tunes SAM for
rock CT/SEM image segmentation, optimizing parameters and handling large-scale
images to improve accuracy. Experiments on rock CT and SEM images show that
fine-tuning significantly enhances SAM's performance, enabling high-quality
mask generation in digital rock image analysis. Our results demonstrate the
feasibility and effectiveness of the fine-tuned SAM model (RockSAM) for rock
images, offering segmentation without extensive training or complex labelling.