Stain-Standardized Deep Learning Framework for Robust Leukocyte Segmentation Across Heterogeneous Cytological Datasets

Accurate leukocyte segmentation remains challenging in automated hematological analysis due to staining variability, heterogeneous imaging conditions, and morphological diversity across cytological datasets, severely limiting deep learning model generalization. This work proposes a dual-module framework designed to achieve stain-invariant and robust leukocyte segmentation. The first module performs explicit stain standardization by combining a VGG-based encoder, a transformer bottleneck, and a convolutional decoder to harmonize diverse inputs toward a Wright–Giemsa reference appearance. The second module introduces a multi-encoder segmentation architecture integrating complementary spatial, leukocyte-specific, and nucleus-focused representations extracted from multiple color spaces. The framework is evaluated on six public and clinical datasets covering multiple staining protocols, magnifications, and imaging scenarios. Experimental results demonstrate consistent high performance, with Dice coefficients exceeding 96% on most datasets and systematic improvements over state-of-the-art methods. Extensive ablation studies confirm the synergistic contributions of stain-standardization and multi-encoder fusion to model robustness and cross-dataset generalization. This framework overcomes stain variability and domain shift, offering a practical tool for automated leukocyte analysis in clinical settings.