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Deep learning permits imaging of multiple structures with the same fluorophores.
Jin, Luhong; Liu, Jingfang; Zhang, Heng; Zhu, Yunqi; Yang, Haixu; Wang, Jianhang; Zhang, Luhao; Kuang, Cuifang; Ji, Baohua; Zhang, Ju; Liu, Xu; Xu, Yingke.
Afiliación
  • Jin L; School of Information Science and Technology, Hangzhou Normal University, Hangzhou, China; Department of Biomedical Engineering, MOE Key Laboratory of Biomedical Engineering, State Key Laboratory of Modern Optical Instrumentation, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detect
  • Liu J; Department of Biomedical Engineering, MOE Key Laboratory of Biomedical Engineering, State Key Laboratory of Modern Optical Instrumentation, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, China.
  • Zhang H; Department of Biomedical Engineering, MOE Key Laboratory of Biomedical Engineering, State Key Laboratory of Modern Optical Instrumentation, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, China.
  • Zhu Y; Department of Biomedical Engineering, MOE Key Laboratory of Biomedical Engineering, State Key Laboratory of Modern Optical Instrumentation, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, China.
  • Yang H; Department of Biomedical Engineering, MOE Key Laboratory of Biomedical Engineering, State Key Laboratory of Modern Optical Instrumentation, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, China;
  • Wang J; Department of Biomedical Engineering, MOE Key Laboratory of Biomedical Engineering, State Key Laboratory of Modern Optical Instrumentation, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, China.
  • Zhang L; Department of Biomedical Engineering, MOE Key Laboratory of Biomedical Engineering, State Key Laboratory of Modern Optical Instrumentation, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, China;
  • Kuang C; State Key Laboratory of Extreme Photonics and Instrumentation, Department of Optical Engineering, Zhejiang University, Hangzhou, China.
  • Ji B; Institute of Biomechanics and Applications, Department of Engineering Mechanics, Zhejiang University, Hangzhou, China.
  • Zhang J; School of Information Science and Technology, Hangzhou Normal University, Hangzhou, China.
  • Liu X; State Key Laboratory of Extreme Photonics and Instrumentation, Department of Optical Engineering, Zhejiang University, Hangzhou, China.
  • Xu Y; Department of Biomedical Engineering, MOE Key Laboratory of Biomedical Engineering, State Key Laboratory of Modern Optical Instrumentation, Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Zhejiang University, Hangzhou, China;
Biophys J ; 2024 Sep 03.
Article en En | MEDLINE | ID: mdl-39233442
ABSTRACT
Fluorescence microscopy, which employs fluorescent tags to label and observe cellular structures and their dynamics, is a powerful tool for life sciences. However, due to the spectral overlap between different dyes, a limited number of structures can be separately labeled and imaged for live-cell applications. In addition, the conventional sequential channel imaging procedure is quite time consuming, as it needs to switch either different lasers or filters. Here, we propose a novel double-structure network (DBSN) that consists of multiple connected models, which can extract six distinct subcellular structures from three raw images with only two separate fluorescent labels. DBSN combines the intensity-balance model to compensate for uneven fluorescent labels for different structures and the structure-separation model to extract multiple different structures with the same fluorescent labels. Therefore, DBSN breaks the bottleneck of the existing technologies and holds immense potential applications in the field of cell biology.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Biophys J Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Biophys J Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos