Computational approach for counting of SISH amplification signals for HER2 status assessment.

ABSTRACT: The human epidermal growth factor receptor 2 (HER2) gene is a critical biomarker for determining amplification status and targeting clinical therapies in breast cancer treatment. This study introduces a computer-aided method that automatically measures and scores HER2 gene status from invasive tissue regions of breast cancer using whole slide images (WSI) through silver in situ hybridization (SISH) staining. Image processing and deep learning techniques are employed to isolate untruncated and non-overlapping single nuclei from cancer regions. The Stardist deep learning model is fine-tuned on our HER2-SISH data to identify nuclei regions, followed by post-processing based on identified HER2 and CEP17 signals. Conventional thresholding techniques are used to segment HER2 and CEP17 sig

SUBMITTER: Rehman ZU 

PROVIDER: S-EPMC11623010 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature