Microscopy cell nuclei segmentation with enhanced U-Net
增强U-Net技术在显微细胞核分割中的应用
作者: Feixiao Long
作者单位: 1Hudongfeng Technology (Beijing) Co., Ltd., Sanjianfang South No.4, DREAM 2049 B05, Chaoyang District, Beijing, China
刊名: BMC Bioinformatics, 2020, Vol.21 (1), pp.115-125
来源数据库: Springer Nature Journal
DOI: 10.1186/s12859-019-3332-1
关键词: Cell and cell nuclei segmentationDeep learningEnhanced U-Net
英文摘要: Abstract(#br)Background(#br)Cell nuclei segmentation is a fundamental task in microscopy image analysis, based on which multiple biological related analysis can be performed. Although deep learning (DL) based techniques have achieved state-of-the-art performances in image segmentation tasks, these methods are usually complex and require support of powerful computing resources. In addition, it is impractical to allocate advanced computing resources to each dark- or bright-field microscopy, which is widely employed in vast clinical institutions, considering the cost of medical exams. Thus, it is essential to develop accurate DL based segmentation algorithms working with resources-constraint computing. Results(#br)An enhanced, light-weighted U-Net (called U-Net+) with modified encoded branch...
全文获取路径: Springer Nature  (合作)
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影响因子:3.024 (2012)

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