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Automated strategy for tissue analysis in anatomic pathology: fiducial marker integration and multisurface tissue comparison
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  • Lorenzo Vannozzi ,
  • Lorena de Los Angeles Guachi Guachi ,
  • Jacopo Ruspi ,
  • Sabrina Ciancia ,
  • Gabriele Baldi ,
  • Dario Lunni ,
  • Paola Scarlino ,
  • Aliria Poliziani ,
  • Alessandra Zucca ,
  • Marco Bellini ,
  • Gian Andrea Pedrazzini ,
  • Andrea Cavazzana ,
  • Leonardo Ricotti
Lorenzo Vannozzi
Scuola Superiore SantAnna

Corresponding Author:[email protected]

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Lorena de Los Angeles Guachi Guachi
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Jacopo Ruspi
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Sabrina Ciancia
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Gabriele Baldi
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Dario Lunni
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Paola Scarlino
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Aliria Poliziani
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Alessandra Zucca
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Marco Bellini
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Gian Andrea Pedrazzini
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Andrea Cavazzana
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Leonardo Ricotti
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In anatomic pathology laboratories, several processes are still manual. Automated solutions can help standardizing the fabrication/processing of paraffin-embedded tissue blocks (PETBs) for a reliable and more effective diagnosis. Here, we propose a novel and automated strategy to introduce a fiducial marker within PETBs, serving as a reliable reference point. This would assist the clinician in automatically identifying specific regions of biological tissue in paraffin-embedded tissue blocks and paraffin-free tissue slices, in case the patient needs further laboratory analysis on that tissue portion to indicate a tailored treatment (e.g., oncological treatment). The proposed strategy implies the introduction of two automated platforms integrated into the conventional anatomic pathology workflow. The first one, named â\euroœIndexingâ\euro?, involves the insertion of a fiducial marker in tissue-free areas within PETBs. The second one, named â\euroœVirtual marker reconstructionâ\euro?, grounds on image analysis and virtually reconstructs the fiducial marker on the paraffin-free tissue slice. An algorithm that analyzes the similarity of the tissue was also developed to assist the traceability of the biological tissue along its processing from the embedding to the post-staining phase. Together, these platforms could assist the work of anatomopathologists, helping avoiding errors and supporting the final diagnosis in a future automated laboratory.