Enhanced Segment Anything Model for Accurate White Blood Cell Segmentation

Abstract

White blood cell image segmentation plays a vital role in the accurate analysis and diagnosis of blood-related diseases, facilitating the identification and quantification of white blood cells (WBCs) in microscopic images. This process is essential for early disease detection, treatment monitoring, and immune response studies, ultimately supporting clinical decision-making. In this paper, we propose an enhanced approach based on the segment anything model (SAM). First, contrast limited adaptive histogram equalisation is applied for preprocessing to enhance the features of WBCs. Then, SAM is utilised for segmentation. Experimental results demonstrate that our method achieves state-of-the-art performance on cross-domain datasets, providing accurate and reliable segmentation of WBCs.