Efficient segmentation algorithm for complex cellular image analysis system

Cellular image analysis system is a complex system that plays a critical role in disease diagnosis and pharmaceutical research. The analysis of image data is one of the most critical aspects of the system. However, there are differences in the distribution of cellular images, including cell morphology, cell density etc. This often requires careful algorithm customization, strict parameter tuning, or even inefficient manual processing, leading to low levels of automation. In this work, an efficient end‐to‐end cell segmentation algorithm, ECS‐Net, is proposed that can handle detection, segmentation, and counting tasks simultaneously. Two modules, proposal focus module (PFM) and enhance mask feature head (EMFH), are introduced to improve the segmentation accuracy. The proposed algorithm achieves better detection and segmentation accuracy with fewer parameters and computational cost, thus improving cellular image analysis systems. Furthermore, considering the medical IoT scenario, the scaled‐down model with only 5.8M parameters has only a small decrease in accuracy which has significant application value.