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

TL;DR

This paper introduces PID-Net, a novel CNN architecture with pixel interval down-sampling, significantly improving dense tiny microorganism counting accuracy in environmental images, outperforming existing methods.

Contribution

The paper presents a new encoder-decoder CNN with pixel interval down-sampling operations, enhancing dense tiny object segmentation and counting accuracy.

Findings

Achieved 96.97% counting accuracy on yeast cell images

Outperformed state-of-the-art models like Attention U-Net, Swin U-Net, and Trans U-Net

Produced clearer boundaries and fewer debris in dense tiny object segmentation

Abstract

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