Alessia Giustolisi
Instance Segmentation of Giemsa and Immunohistochemical Lymphoma Cells: Towards AI-Enabled Virtual Flow Cytometry.
Rel. Massimo Salvi, Nicola Michielli. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Biomedica, 2024
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Abstract
In lymphoma diagnostics, flow cytometry is widely used for cell counting and sorting, providing essential insights into individual cell characteristics; however, it is limited by its single-cell analysis approach, which lacks spatial and morphological context. On the other hand, the manual analysis of whole-slide images (WSIs) from scanned slides is highly time-consuming and prone to both intra- and inter-operator variability. This thesis project proposes a tool for the rapid and automated delineation of cell nuclei in WSIs, with the goal of not only minimising diagnostic time but also reducing variability through the repeatable nature of automated analysis. Traditionally, Haematoxylin and Eosin (H&E) staining is among the most used methods in this field, with extensive related instance segmentation research in literature.
Although cytological stains like Giemsa and immunohistochemical (IHC) markers offer complementary information not visible in H&E, their automatic segmentation has been less explored in the literature
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