To assist researchers to identify Environmental Microorganisms (EMs) effectively, a Multiscale CNN-CRF (MSCC) framework for the EM image segmentation is proposed in this paper. There are two parts in this framework: The first is a novel pixel-level segmentation approach, using a newly introduced Convolutional Neural Network (CNN), namely, “mU-Net-B3”, with a dense Conditional Random Field (CRF) postprocessing. The second is a VGG-16 based patch-level segmentation method with a novel “buffer” strategy, which further improves the segmentation quality of the details of the EMs. In the experiment, compared with the state-of-the-art methods on 420 EM images, the proposed MSCC method reduces the memory requirement from 355 MB to 103 MB, improves the overall evaluation indexes (Dice, Jaccard, Recall, Accuracy) from 85.24%, 77.42%, 82.27%, and 96.76% to 87.13%, 79.74%, 87.12%, and 96.91%, respectively, and reduces the volume overlap error from 22.58% to 20.26%. Therefore, the MSCC method shows great potential in the EM segmentation field.

中文翻译：

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用于环境微生物图像分割的多尺度CNN-CRF框架。

为帮助研究人员有效地识别环境微生物（EM），本文提出了一种用于EM图像分割的多尺度CNN-CRF（MSCC）框架。该框架有两个部分：第一个是新颖的像素级分割方法，使用新引入的卷积神经网络（CNN），即“ mU-Net-B3”，具有密集的条件随机场（CRF）后处理。第二种是基于VGG-16的补丁级别分割方法，具有新颖的“缓冲区”策略，可进一步提高EM细节的分割质量。在实验中，与420幅EM图像上的最新方法相比，所提出的MSCC方法将内存需求从355 MB减少到103 MB，并提高了总体评估指标（骰子，雅卡德，召回率，准确性）。分别从85.24％，77.42％，82.27％和96.76％降至87.13％，79.74％，87.12％和96.91％，并将体积重叠误差从22.58％降低至20.26％。因此，MSCC方法在EM分割领域显示出巨大的潜力。