Chronic lymphocytic leukemia (CLL) screening and abnormality detection based on multi-layer fluorescence imaging signal enhancement and compensation.

Purpose: Fluorescence in situ hybridization (FISH) plays a critical role in cancer screening but faces challenges in signal clarity and manual intervention. This study aims to enhance FISH signal clarity, improve screening efficiency, and reduce false negatives through an automated image acquisition and signal enhancement framework.

Methods: An automated workflow was developed, integrating a dynamic signal enhancement method that optimizes global and local features.

Fluorescence in Situ Hybridization Cell Image Segmentation Method.

Fluorescence in situ hybridization (FISH) is a molecular cytogenetic technique that provides reliable imaging biomarkers for the diagnosis of cancer and genetic diseases at the cellular level. An important prerequisite for identifying cancer cells in FISH images is the accurate segmentation of cells to quantify the DNA/RNA signal within each cell. However, FISH images often have problems such as low contrast, unclear cell boundaries, and potential cell defects, which hinder the complete identification and effective segmentation of cells.