论文标题
像素间隔下采样（PID）的应用，以在环境微生物图像上计算密集的微生物
An application of Pixel Interval Down-sampling (PID) for dense tiny microorganism counting on environmental microorganism images
论文作者
论文摘要
本文提出了一个新颖的像素间隔下采样网络（PID-NET），以较高的精度计数密集的微小物体（酵母单元）计数任务。 PID-NET是具有编码器架构的端到端卷积神经网络（CNN）模型。像素间隔下采样操作与最大式操作相连，以结合稀疏和密集的特征。这解决了计数时浓密物体的轮廓凝结的局限性。使用经典分割指标（骰子，Jaccard和Hausdorff距离）以及计数指标进行评估。实验结果表明，所提出的PID-NET具有最佳的性能和潜力，可以实现密集的微小对象计数任务，该任务在数据集中具有2448个酵母单元图像的数据集上的计数精度为96.97 \％。通过与最新的方法进行比较，例如注意U-NET，SWIN U-NET和TRANS U-NET，所提出的PID-NET可以分割具有更清晰边界和较少不正确的碎屑的密集的微小物体，这表明PID-NET在准确计数的任务中的巨大潜力。
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.
