arXiv AI By David Corral Fontecha, Juan Miranda Bautista, Pablo Menendez Fern\'andez-Miranda, Andrea Trapote Fernandez, Lara Lloret Iglesias, Jose A. Vega

A Voxel-Spacing-Aware Extension of PyRadiomics for Anisotropic Texture Analysis

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arXiv AI
Sep 15

Physically Aware Radiomics Without Interpolation: Disentangling Voxel Geometry and Signal Modification in CT and MRI

The study introduces a voxel-spacing-aware radiomic framework that incorporates physical geometry into texture computation without resampling, modifying PyRadiomics to preserve native image signals. Four extraction configurations—native non-resampled (NR), isotropic resampling (RS), voxel-spacing-aware (VS), and fake-isotropic preprocessing (FK)—were compared across 685 CT pulmonary nodules and 209 MRI breast cases, evaluating 196 radiomic descriptors. Results show that VS closely matches NR (median ICC(A,1) ≈0.998) while RS and FK exhibit lower agreement, indicating that spacing metadata alone can significantly influence radiomic features.

By David Corral Fontecha, Juan Miranda Bautista, Pablo Menendez Fern\'andez-Miranda, Sergio Rubio-Mart\'in, Lara Lloret Iglesias, Jose A. Vega
arXiv Computer Vision
Sep 3

RAFT-DVC: Resolution-Aware Machine Learning-Based Digital Volume Correlation

RAFT-DVC is a resolution‑aware family of recurrent all‑pairs field transform (RAFT) based digital volume correlation (DVC) solvers that use encoder downsampling factors of 2, 4, and 8. The solvers localize displacement to about 0.017 feature‑grid voxels, with raw‑volume error scaling roughly as 0.017 s voxels, and exhibit complementary operating regimes determined by displacement reach and volumetric‑texture compatibility. Synthetic benchmarks show comparable performance to tuned classical DVC for fine‑texture, small‑to‑moderate displacements, while outperforming it for coarse‑texture, large‑displacement scenarios; additional tests on confocal and micro‑CT images confirm the importance of matching solver regimes to deformation magnitude and texture, and demonstrate cross‑texture transfer and improved accuracy after correcting sampler geometry.

By Zixiang Tong, Lehu Bu, Jin Yang
arXiv Machine Learning
Jul 31

Negative controls reveal volume-driven confounding in radiomics and imaging foundation model features

arXiv:2607. 28423v1 Announce Type: cross Abstract: Radiomics and imaging foundation models promise non-invasive biomarkers of tumour biology, yet predictive signatures may reflect tumour volume or acquisition artifacts rather than meaningful image structure.

By Katy L. Scott, Sejin Kim, Joshua Siraj, Caryn Geady, Matthew Boccalon, Mattea Welch, Mogtaba Alim, Andrew J. Hope, Benjamin Haibe-Kains