LEECAU-Net: A Latent Entropy-Quantized and Efficient Channel Attention-Aided U-Net Model for Microscopic Medical Image Segmentation

Microscopic medical image segmentation is essential for detailed cellular-level analysis in computational pathology, offering insights into disease mechanisms. However, this task is particularly challenging due to the high similarity between different cellular classes and variability within each class, especially in densely packed or overlapping cell regions. To this end, we present LEECAU-Net (Latent Entropy-quantized and Efficient Channel Attention-aided U-Net), a U-Net-based model designed to improve segmentation accuracy through a dual-attention mechanism. Built on an Inceptionv4 backbone, LEECAU-Net's encoder integrates Latent Entropy-quantized Channel Attention (LECA) and Efficient Channel Attention (ECA) modules. The ECA module enhances the network's ability to focus on essential channel dependencies, while LECA leverages entropy quantification to prioritize channels rich in information, leading to better differentiation between similar structures. We have tested LEECAU-Net on three challenging publicly available datasets: Triple Negative Breast Cancer (TNBC), Multi-organ Nuclei Segmentation (MoNuSeg), and Cervical Nucleus Segmentation (CNS). Across these datasets, our model achieves higher performance metrics than numerous state-of-the-art approaches reported in the literature. The improved results underscore the effectiveness of LEECAU-Net's architecture in handling the complexity of microscopic medical images, making it a valuable tool for advancing automated cellular analysis in biomedical research. The source code and more results are made available at https://github.com/Cmatermedicalimageanalysis/LEECAU-Net.