PRISM: Perinuclear Ring-based Image Segmentation Method for Acute Lymphoblastic Leukemia Classification

2026-05-12

AI Summary

This study addresses the challenges in classifying acute lymphoblastic leukemia (ALL) cells from blood smears, where low cytoplasmic contrast and high morphological variability render conventional membrane-based segmentation methods ineffective and limit the generalization of existing deep learning models. To overcome these limitations, the authors propose PRISM, a novel approach that eschews precise cytoplasmic segmentation and instead constructs adaptive concentric rings around the nucleus. This framework integrates color features with gray-level co-occurrence matrix–derived texture information and employs a calibrated stacked ensemble classifier for robust discrimination. By eliminating reliance on cell boundary delineation, PRISM demonstrates strong robustness and generalizability across diverse staining protocols and imaging conditions, achieving 98.46% accuracy and a PR-AUC of 0.9937 in ALL classification.

Abstract

Automated analysis of peripheral blood smears for Acute Lymphoblastic Leukemia (ALL) is hindered by low contrast and substantial variability in cytoplasmic appearance, which complicate conventional membrane-based segmentation. We found that many recent approaches rely on heavy neural architectures and extensive training, but still struggle to generalize across staining and acquisition variability. To address these limitations, we propose the Perinuclear Ring-based Image Segmentation Method (PRISM), which replaces explicit cytoplasmic delineation with adaptive concentric zones constructed around the nucleus. These perinuclear regions enable the extraction of robust cytoplasmic descriptors by integrating color information with texture statistics derived from grey-level co-occurrence patterns, without requiring accurate cell-boundary detection. A calibrated stacking ensemble of traditional classifiers leverages these descriptors to achieve a high performance, with an accuracy of 98.46% and a precision-recall AUC of 0.9937.

Problem

• Acute Lymphoblastic Leukemia
• image segmentation
• cytoplasmic variability
• low contrast
• generalization

Innovation

• perinuclear ring
• image segmentation
• acute lymphoblastic leukemia
• texture statistics
• stacking ensemble