Studying pulmonary fibrosis due to microbial infection via automated microscopic image analysis
IntroductionPulmonary fibrosis is a consequential complication of microbial infections, which has notably been observed in SARS-CoV-2 infections in recent times. Macrophage polarization, specifically the M2-type, is a significant mechanism that induces pulmonary fibrosis, and its role in the develop...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-03-01
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| Series: | Frontiers in Microbiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmicb.2023.1176339/full |
| _version_ | 1797862212096753664 |
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| author | Yajie Chen Henghui He Licheng Luo Kangyi Liu Min Jiang Shiqi Li Xianqi Zhang Xin Yang Qian Liu |
| author_facet | Yajie Chen Henghui He Licheng Luo Kangyi Liu Min Jiang Shiqi Li Xianqi Zhang Xin Yang Qian Liu |
| author_sort | Yajie Chen |
| collection | DOAJ |
| description | IntroductionPulmonary fibrosis is a consequential complication of microbial infections, which has notably been observed in SARS-CoV-2 infections in recent times. Macrophage polarization, specifically the M2-type, is a significant mechanism that induces pulmonary fibrosis, and its role in the development of Post- COVID-19 Pulmonary Fibrosis is worth investigating. While pathological examination is the gold standard for studying pulmonary fibrosis, manual review is subject to limitations. In light of this, we have constructed a novel method that utilizes artificial intelligence techniques to analyze fibro-pathological images. This method involves image registration, cropping, fibrosis degree classification, cell counting and calibration, and it has been utilized to analyze microscopic images of COVID-19 lung tissue.MethodsOur approach combines the Transformer network with ResNet for fibrosis degree classification, leading to a significant improvement over the use of ResNet or Transformer individually. Furthermore, we employ semi-supervised learning which utilize both labeled and unlabeled data to enhance the ability of the classification network in analyzing complex samples. To facilitate cell counting, we applied the Trimap method to localize target cells. To further improve the accuracy of the counting results, we utilized an effective area calibration method that better reflects the positive density of target cells.ResultsThe image analysis method developed in this paper allows for standardization, precision, and staging of pulmonary fibrosis. Analysis of microscopic images of COVID-19 lung tissue revealed a significant number of macrophage aggregates, among which the number of M2-type macrophages was proportional to the degree of fibrosis.DiscussionThe image analysis method provids a more standardized approach and more accurate data for correlation studies on the degree of pulmonary fibrosis. This advancement can assist in the treatment and prevention of pulmonary fibrosis. And M2-type macrophage polarization is a critical mechanism that affects pulmonary fibrosis, and its specific molecular mechanism warrants further exploration. |
| first_indexed | 2024-04-09T22:16:39Z |
| format | Article |
| id | doaj.art-dd99fdfb993d4f61b8fdeac3ef405631 |
| institution | Directory Open Access Journal |
| issn | 1664-302X |
| language | English |
| last_indexed | 2024-04-09T22:16:39Z |
| publishDate | 2023-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Microbiology |
| spelling | doaj.art-dd99fdfb993d4f61b8fdeac3ef4056312023-03-23T04:32:28ZengFrontiers Media S.A.Frontiers in Microbiology1664-302X2023-03-011410.3389/fmicb.2023.11763391176339Studying pulmonary fibrosis due to microbial infection via automated microscopic image analysisYajie Chen0Henghui He1Licheng Luo2Kangyi Liu3Min Jiang4Shiqi Li5Xianqi Zhang6Xin Yang7Qian Liu8School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, ChinaDepartment of Forensic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaSchool of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, ChinaSchool of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, ChinaDepartment of Forensic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaDepartment of Forensic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaDepartment of Forensic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaSchool of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, ChinaDepartment of Forensic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, ChinaIntroductionPulmonary fibrosis is a consequential complication of microbial infections, which has notably been observed in SARS-CoV-2 infections in recent times. Macrophage polarization, specifically the M2-type, is a significant mechanism that induces pulmonary fibrosis, and its role in the development of Post- COVID-19 Pulmonary Fibrosis is worth investigating. While pathological examination is the gold standard for studying pulmonary fibrosis, manual review is subject to limitations. In light of this, we have constructed a novel method that utilizes artificial intelligence techniques to analyze fibro-pathological images. This method involves image registration, cropping, fibrosis degree classification, cell counting and calibration, and it has been utilized to analyze microscopic images of COVID-19 lung tissue.MethodsOur approach combines the Transformer network with ResNet for fibrosis degree classification, leading to a significant improvement over the use of ResNet or Transformer individually. Furthermore, we employ semi-supervised learning which utilize both labeled and unlabeled data to enhance the ability of the classification network in analyzing complex samples. To facilitate cell counting, we applied the Trimap method to localize target cells. To further improve the accuracy of the counting results, we utilized an effective area calibration method that better reflects the positive density of target cells.ResultsThe image analysis method developed in this paper allows for standardization, precision, and staging of pulmonary fibrosis. Analysis of microscopic images of COVID-19 lung tissue revealed a significant number of macrophage aggregates, among which the number of M2-type macrophages was proportional to the degree of fibrosis.DiscussionThe image analysis method provids a more standardized approach and more accurate data for correlation studies on the degree of pulmonary fibrosis. This advancement can assist in the treatment and prevention of pulmonary fibrosis. And M2-type macrophage polarization is a critical mechanism that affects pulmonary fibrosis, and its specific molecular mechanism warrants further exploration.https://www.frontiersin.org/articles/10.3389/fmicb.2023.1176339/fullmicrobial infectionpulmonary fibrosismicroscopic imageartificial intelligenceimage analysismacrophage |
| spellingShingle | Yajie Chen Henghui He Licheng Luo Kangyi Liu Min Jiang Shiqi Li Xianqi Zhang Xin Yang Qian Liu Studying pulmonary fibrosis due to microbial infection via automated microscopic image analysis Frontiers in Microbiology microbial infection pulmonary fibrosis microscopic image artificial intelligence image analysis macrophage |
| title | Studying pulmonary fibrosis due to microbial infection via automated microscopic image analysis |
| title_full | Studying pulmonary fibrosis due to microbial infection via automated microscopic image analysis |
| title_fullStr | Studying pulmonary fibrosis due to microbial infection via automated microscopic image analysis |
| title_full_unstemmed | Studying pulmonary fibrosis due to microbial infection via automated microscopic image analysis |
| title_short | Studying pulmonary fibrosis due to microbial infection via automated microscopic image analysis |
| title_sort | studying pulmonary fibrosis due to microbial infection via automated microscopic image analysis |
| topic | microbial infection pulmonary fibrosis microscopic image artificial intelligence image analysis macrophage |
| url | https://www.frontiersin.org/articles/10.3389/fmicb.2023.1176339/full |
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