AI-Based Particle Size Distribution Prediction from 3D Packed-Particle Tomographic Volumes: A Comparative Study with classical Watershed Segmentation
Year: 2026
Type: preprint
Abstract: Abstract Determining the Particle Size Distribution (PSD) of granular media and powder compacts is vital across condensed matter physics, geological sciences, and materials engineering. While X-ray computed tomography (µCT) captures granular microstructure non-destructively, traditional quantitative PSD estimation relies on classical morphological segmentation (binarization, distance transform, and watershed flooding), which is computationally intensive and sensitive to contact-neck ambiguities. In this work, we investigate whether a compact 3D Convolutional Neural Network (Compact3DCNN, 300,900 parameters) can predict normalized 20-bin PSD histograms directly from unsegmented 3D grayscale volumes without intermediate particle instance extraction. Using a synthetic dataset of 60 independent non-overlapping polydisperse packed-sphere volumes (483 voxels) with exact analytic PSD ground truth, we benchmark the CNN against a classical 3D Euclidean distance-transform watershed baseline on an untouched, held-out test split (N = 10, 40 train / 10 validation / 10 test). On this low-to-moderate packing regime (15%–25%), the classical watershed baseline achieved superior accuracy, attaining a mean Earth Mover’s Distance (EMD) of 0.0974 ± 0.0261 voxels compared to 0.3428 ± 0.0949 voxels for the CNN (paired 95% bootstrap CI: [+0.1837, +0.3156] voxels). Measured runtime was 1.19 ± 0.27 ms per volume for the CNN on an NVIDIA RTX 3050 Laptop GPU versus 33.71 ± 7.50 ms for the watershed pipeline on an Intel CPU; however, we emphasize that this reflects disparate computing architectures rather than a controlled hardware comparison. Furthermore, comparison against an empirical training-set-mean baseline (0.6206 ± 0.2440 vx EMD on validation) confirms that the CNN learns volume-specific morphological features (0.3970 ± 0.1335 vx, a 36.0% error reduction). A controlled validation experiment evaluating direct 1D Wasserstein-1 loss training yielded substantially higher error (1.1382 ± 0.4907 vx EMD) than the primary Kullback-Leibler (KL) divergence configuration, illustrating optimization challenges in cumulative L1 histogram penalties. Our findings characterize the accuracy-throughput profile in direct volumetric PSD regression and delineate key open challenges for dense granular systems.
Source: Research Square
Authors: Ruhi Gupta, Mayank Goswami
Institution: Indian Institute of Technology Roorkee
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225379094