Abstract
Microscopic phytoplankton segmentation is an important part of water quality assessment. The segmentation of microscopic phytoplankton still faces challenges for computer vision, such as being affected by background impurities and requiring a large number of manual annotation. In this paper, the characteristics of phytoplankton emitting fluorescence under excitation light were utilized to segment and annotate phytoplankton contours by fusing fluorescence images and bright field images. Morphological operations were used to process microscopic fluorescence images to obtain the initial contours of phytoplankton. Then, microscopic bright field images were processed by Active Contour to fine tune the contours. Seven algae species were selected as the experimental objects. Compared with manually labeling the contour in LabelMe, the recall, precision, FI score and IOU of the proposed segmentation method are 85.3%, 84.5%, 84.7%, and 74.6%, respectively. Mask-RCNN was used to verify the correctness of labels annotated by the proposed method. The average recall, precision, F1 score and IOU are 97.0%, 86.5%, 91.1%, and 84.2%, respectively, when the Mask-RCNN is trained with the proposed automatic labeling method. And the results corresponding to manual labeling are 95.3%, 86.1%, 90.3%, and 82.8% respectively. The experimental results show that the proposed method can segment the phytoplankton microscopic image accurately, and the automatically annotated contour data has the same effect as the manually annotated contour data in Mask-RCNN, which greatly reduces the manual annotation workload.
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The data that support outcomes of this study are available on reasonable request from the corresponding author.
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Funding
This work was supported by the Science and Technology Major Special Project of Anhui Province (202203a07020002,202003a07020007); the National Key Research and Development Program of China (2021YFC3200100); the National Natural Science Foundation of China (61875207, 62005001).
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Writing, renqing Jia; Funding acquisition, Gaofang Yin, Xiaoling Zhang, and Nanjing Zhao; Supervision, Qianfeng He, and Nanjing Zhao; Resources, Min Xu, Xiang Hu, Peng Huang, and Tianhong Liang; English polishing, Xiaowei Chen. All authors have read and agreed to the published version of the manuscript.
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Jia, R., Yin, G., Zhao, N. et al. Phytoplankton Image Segmentation and Annotation Method Based on Microscopic Fluorescence. J Fluoresc (2023). https://doi.org/10.1007/s10895-023-03515-6
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DOI: https://doi.org/10.1007/s10895-023-03515-6