| Autor: |
Lindsey G. Siegfried, Sophie M. Bilik, Jamie L. Burgess, Paola Catanuto, Ivan Jozic, Irena Pastar, Rivka C. Stone, Marjana Tomic-Canic |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
JID Innovations, Vol 4, Iss 3, Pp 100270- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2667-0267 |
| DOI: |
10.1016/j.xjidi.2024.100270 |
| Popis: |
Advancements in pathology have given rise to software applications intended to minimize human error and improve efficacy of image analysis. Still, the subjectivity of image quantification performed manually and the limitations of the most ubiquitous tissue stain analysis software requiring parameters tuned by the observer, reveal the need for a highly accurate, automated nuclear quantification software specific to immunohistochemistry, with improved precision and efficiency compared with the methods currently in use. We present a method for the quantification of immunohistochemical biomarkers in keratinocyte nuclei proposed to overcome these limitations, contributing sensitive shape-focused segmentation, accurate nuclear detection, and automated device-independent color assessment, without observer-dependent analysis parameters. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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