arXiv：cs.AI· Farouk Ganiyu Adewumi, Timothy Oladunni·· 6 小时前AI 评分29
三维线粒体形态测量中的表示偏差、校正迁移与分辨率敏感性
Representation Bias, Correction Transfer, and Resolution Sensitivity in Three-Dimensional Mitochondrial Morphometry
AI 导读
基于3D Mitochondria Shape Library for Optical Microscopy的2,720个对象，研究发现占用体积比参考网格体积平均高3.665%，尽管ICC达0.994；在受控标签流水线重实现中，去除深度偏移使55个对象的体积误差平均降低1.57个百分点。
正文
Abstract:Quantitative imaging pipelines can produce precise but systematically different measurements of the same object. We present an empirical reliability assessment of three-dimensional mitochondrial morphometry that connects representation bias, a controlled processing intervention, correction transfer, and resolution sensitivity. Using 2,720 development objects from the 3D Mitochondria Shape Library for Optical Microscopy, we find that occupancy-derived volumes exceed reference mesh volumes by 3.665% on average despite an intraclass correlation coefficient of 0.994. Boundary analysis identifies an outward label displacement of 0.00304 normalized units. In a controlled label-pipeline reimplementation, removing the depth offset reduces volume error in all 55 analyzed objects by a mean of 1.57 percentage points, approximately 45% of mean reproduced inflation; the source of the remainder is not isolated. A frozen regression using occupancy-derived features reduces median absolute percentage error from 3.481% to 0.664% in 2,728 previously unused objects from the same resource. However, its calibrated error bound covers only 92.1% overall and 49.2% in a low-occupancy subgroup, demonstrating that accuracy and uncertainty transfer must be evaluated separately. In 550 rat-cortex objects from the MitoEM resource, coarsening in-plane spacing from 8 to 24 nanometers changes median surface area by minus 10.60% and sphericity by plus 11.76%, despite a rank correlation of 0.994. These results provide quantitative checks for distinguishing processing-induced descriptor changes from candidate biological differences, without establishing biological invariance or cross-source correction transfer.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.07582 [cs.AI] |
| (or arXiv:2610.07582v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.07582 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Farouk Ganiyu Adewumi [view email]
[v1]
Tue, 6 Oct 2026 01:18:00 UTC (985 KB)
来源：arXiv：cs.AI · arxiv.org