Automated quantification and feature extraction of nuclei in diffuse large B-cell lymphoma using advanced imaging techniques
Year: 2025
Type: article
Abstract: Diffuse Large B-Cell Lymphoma is a subtype of non-Hodgkin lymphoma that occurs worldwide; around 80 thousand new cases were recorded in 2022. The accurate diagnosis and subtyping of non-Hodgkin lymphoma pose significant challenges that necessitate expertise, extensive experience, and meticulous morphological analysis. To address these challenges, a study was developed to segment and classify the slides of diffuse large B-cell lymphoma. This study utilizes a dataset consisting of 108 images of H&E-stained slides, including MYC-positive, MYC-negative, and normal slides. The images are initially pre-processed using colour deconvolution to highlight haematoxylin-stained nuclei before they are segmented to detect a nuclear area using the watershed algorithm and extract a morphological feature of nuclei. Results indicate that area showed a significant difference (P < 0.05), highlighting variations in nuclear size among groups. In contrast, perimeter, diameter, and circularity showed no significant differences. Colour analysis revealed significant differences in the standard deviation of the L component in LAB space and all RGB standard deviations, while the mean LAB and RGB values showed no significant differences.
Authors: Chee Chin Lim, Gei Ki Tang, Faezahtul Arbaeyah Hussain, Qi Wei Oung, Aidy Irman Yajid, Yen Fook Chong, Siti Nur Syahirah Mohamad Redwan
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,400
Awards: G5761061299, G7831024080
OpenAlex ID: W4414192783