Reproducible Industrial CT–to–Porosity Metrics with nnU-Net—A Weak Versus Strong Inference Benchmark on Cementitious Slices

Porosity-related quantities from industrial X-ray CT depend on segmentation and inference choices. When inference defaults are omitted from the report, void or phase fractions can shift by amounts comparable to slice-to-slice variability. The contribution is metrological rather than architectural: we document a reproducible nnU-Net 2D workflow on Dataset601 CTVoid from semantic labels to slice-wise void fraction, optional two-dimensional connected-component pore summaries, isotropic three-dimensional stacking at 0.058 mm spacing, and spatial axis diagnostics, with region of interest and voxel spacing stated explicitly. The main results pair a weak export policy, defined as a single forward pass per slice without multi-scale fusion or test-time augmentation, with a strong policy that enables multi-scale fusion and flip-based augmentation on the same slice exports and identical weights, on one hundred consecutive slices from one cementitious industrial stack of 1028 × 1028 pixels. In parallel we report trainer validation on eight named Dataset601 validation cases and mirroring-based test-time augmentation off versus on re-inference on those same cases; case identifiers and the cross-validation split appear in the main text. These quantities answer different questions and must not be substituted for one another or for independent full-stack ground truth. Porosity-related scalars from industrial X-ray CT depend on how segmentation and inference are configured; when defaults are omitted, void fractions can shift by amounts comparable to slice-to-slice variability. For fixed nnU-Net weights on one cementitious industrial slice stack (1028 × 1028 pixels), we benchmark weak inference (single forward pass, no multi-scale fusion or test-time augmentation) against a strong export policy (multi-scale fusion and flip-based augmentation) on 100 paired slices, and report parallel trainer validation and TTA-off versus TTA-on re-inference on eight Dataset601 hold-out cases. For the industrial dataset, mean void-class IoU between modes is 0.716 (SD 0.043), while strong inference is ~2.6× slower and predicts lower mean void area (2.37% vs. 3.04%). The full weak export gives a 3D void ratio of 2.44% and integrated void volume of 5175 mm3. On validation patches, mean void Dice/IoU against the reference are 0.835/0.728, while weak–strong void IoU reaches 0.924 under the nnU-Net-native TTA contrast—quantities that must not be interchanged across domains or definitions. The present benchmark does not include a systematic polymer dosage series, and the study does not equate semantic void with open porosity but provides a reproducible disclosure template relevant to porous and polymer-modified cementitious CT reporting.