Exponential distance transform maps for cell localization

Bo Li, Jie Chen, Hang Yi, Min Feng, Yongquan Yang, Qikui Zhu, Hong Bu

Abstract: Cell localization in medical image analysis aims for precise identification of cell positions. Existing methods involve predicting density maps from images, followed by post-processing to extract cell location and number details. The quality of generated density maps significantly impacts the model’s localization and counting performance. However, density maps produced with Gaussian kernels exhibit stacking in dense regions, resulting in inaccurate cell location information and suboptimal localization performance. In this study, we propose an exponential distance transform map that ensures accurate location information and provides well-defined gradient details for effective model learning, setting a new benchmark for high performance. Additionally, to address the challenge of substantial variations in cell color within images, we introduce a multi-scale gradient aggregation module that enhances the model’s color recognition robustness through gradient information utilization. Experimental results across diverse datasets showcase notable improvements, establishing a novel benchmark for cell localization.