Detection-Guided ROI-Constrained Diffusion for Weakly Supervised White Blood Cell Segmentation: A Retrospective Internal and External Dataset Evaluation
Abstract
Background: Accurate white blood cell (WBC) segmentation is important for quantitative microscopic image analysis, but conventional supervised approaches depend on labor-intensive pixel-level annotations and may exhibit reduced robustness across datasets. This study proposes a detector-guided, region of interest (ROI)-constrained framework that combines automatic localization, pseudo-mask-based weak supervision, and diffusion-assisted segmentation. Methods: You Only Look Once version 13 Nano (YOLOv13-N) was used to localize WBCs and define ROIs, within which segmentation was performed using the proposed diffusion-assisted model. Automatically generated pseudo-masks served as segmentation-training targets, while expert masks were retained for reference evaluation. Experiments used a verified Dicle cohort of 14,721 records, partitioned into 10,305 training, 2208 validation, and 2208 held-out test records. Performance was evaluated separately at ROI-conditional and end-to-end levels. Generalization was assessed on 11,200 Raabin-WBC records using the frozen framework without external tuning. Results: The automatically generated pseudo-masks achieved a mean Dice score of approximately 0.702, demonstrating usable but imperfect weak supervision. On the held-out Dicle test set, the proposed framework achieved a Dice score of 0.7188, intersection over union (IoU) of 0.5796, precision of 0.8966, and recall of 0.6422 under ROI-conditional evaluation. End-to-end performance was 0.6705…
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