Sara Siragusa
Proliferation Index estimation in Immunohistochemical images via Deep Learning: Stain Normalization Impact assessment in Nuclei Segmentation and Classification.
Rel. Massimo Salvi, Nicola Michielli. Politecnico di Torino, Master of science program in Biomedical Engineering, 2024
Abstract
In pathology, immunohistochemical (IHC) biomarkers are essential for characterizing cancers and improving therapy responses through targeted approaches. The proliferation index (PI) plays a vital role in histopathologic diagnostics, especially for tumors, as it reflects the rate of cell proliferation within a tissue sample. Therefore, PI is a crucial metric in pathology, and it is determined by measuring diaminobenzidine (DAB) intensity expression through immunohistochemistry. Typically, PI is visually assessed by pathologists through the examination of tissue samples. However, this manual counting method is suboptimal due to its significant intra- and inter-observer variability and the time-consuming nature of the process. Accurately distinguishing between proliferating (immuno-positive) and non-proliferating (immuno-negative) cells in breast tissue is essential for precise diagnosis and effective treatment planning.
This thesis project addresses these issues through the implementation of a deep learning-based method for the segmentation and classification of nuclei within IHC-stained images
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