论文标题
使用成对的深度学习特征对弱可见的环境微生物图像进行分割
Segmentation of Weakly Visible Environmental Microorganism Images Using Pair-wise Deep Learning Features
论文作者
论文摘要
环境微生物（EMS）的使用通过监测和分解污染物，为环境污染提供了高效，低成本和无害的补救措施。这取决于如何正确分段和识别EMS。为了增强透明，嘈杂且对比度低的弱可见EM图像的分割，在本研究中提出了成对深度学习特征网络（PDLF-NET）。 PDLFS的使用使网络通过将每个图像的成对深度学习特征与基本模型Segnet的不同块相连，从而使网络更加关注前景（EMS）。利用SHI和TOMAS描述符，我们在贴片上提取了每个图像的深度特征，这些图像使用VGG-16模型以每个描述符为中心。然后，为了学习描述符之间的中间特征，基于Delaunay三角定理进行功能的配对以形成成对的深度学习特征。在此实验中，PDLF-NET可在精度，iou，dice，dice，voe，voe，敏感性，精确度和特定性上分别达到89.24％，63.20％，77.27％，35.15％，89.72％，89.72％，91.44％和89.30％的出色分割结果。
The use of Environmental Microorganisms (EMs) offers a highly efficient, low cost and harmless remedy to environmental pollution, by monitoring and decomposing of pollutants. This relies on how the EMs are correctly segmented and identified. With the aim of enhancing the segmentation of weakly visible EM images which are transparent, noisy and have low contrast, a Pairwise Deep Learning Feature Network (PDLF-Net) is proposed in this study. The use of PDLFs enables the network to focus more on the foreground (EMs) by concatenating the pairwise deep learning features of each image to different blocks of the base model SegNet. Leveraging the Shi and Tomas descriptors, we extract each image's deep features on the patches, which are centered at each descriptor using the VGG-16 model. Then, to learn the intermediate characteristics between the descriptors, pairing of the features is performed based on the Delaunay triangulation theorem to form pairwise deep learning features. In this experiment, the PDLF-Net achieves outstanding segmentation results of 89.24%, 63.20%, 77.27%, 35.15%, 89.72%, 91.44% and 89.30% on the accuracy, IoU, Dice, VOE, sensitivity, precision and specificity, respectively.
