An application of Pixel Interval Down-sampling (PID) for dense tiny microorganism counting on environmental microorganism images

This paper proposes a novel pixel interval down-sampling network (PID-Net) for dense tiny objects (yeast cells) counting tasks with higher accuracy. The PID-Net is an end-to-end CNN model with encoder to decoder architecture. The pixel interval down-sampling operations are concatenated with max-pooling operations to combine the sparse and dense features. It addresses the limitation of contour conglutination of dense objects while counting. Evaluation was done using classical segmentation metrics (Dice, Jaccard, Hausdorff distance) as well as counting metrics. Experimental result shows that the proposed PID-Net has the best performance and potential for dense tiny objects counting tasks, which achieves 96.97% counting accuracy on the dataset with 2448 yeast cell images. By comparing with the state-of-the-art approaches like Attention U-Net, Swin U-Net and Trans U-Net, the proposed PID-Net can segment the dense tiny objects with clearer boundaries and fewer incorrect debris, which shows the great potential of PID-Net in the task of accurate counting tasks.