{"paper":{"title":"Exploring the role of sample thickness for hyperspectral microscopy tissue discrimination through Monte Carlo simulations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["physics.bio-ph"],"primary_cat":"physics.optics","authors_text":"1), Amsterdam, Aquaculture Research (Nofima), Behdad Dashtbozorg (2), Department of Surgery, Fisheries, Gustavo M. Callico (1) ((1) Institute For Applied Microelectronics (Iuma), Henricus J.c.m. Sterenborg (2), Laura Quintana-quintana (1), Mark Witteveen (2), Netherlands (3) Norwegian Institute Of Food, Norway), Samuel Ortega (3, Spain (2) Image-guided Surgery, The Netherlands Cancer Institute, Theo J.m. Ruers (2), Troms{\\o}, University Of Las Palmas De Gran Canaria (Ulpgc)","submitted_at":"2025-07-29T10:37:48Z","abstract_excerpt":"Recent advancements in multispectral (MS) and hyperspectral (HS) microscopy have focused on sensor and system improvements, yet sample processing remains overlooked. We conducted an analysis of the literature, revealing that 40 percent of studies do not report sample thickness. Among those that did report it, the vast majority, 98 percent, used 2 to 10 micrometer samples. This study investigates the impact of unstained sample thickness on MS/HS image quality through light transport simulations. Monte Carlo simulations were conducted on various tissue types (i.e., breast, colorectal, liver, and"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.21675","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2507.21675/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}