ArticleAPL bioengineering2021
Leveraging multimodal microscopy to optimize deep learning models for cell segmentation.
Article in APL bioengineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- PixlMap: A generalisable pixel classifier for cellular phenotyping in multiplex immunofluorescence images.PloS one · 2025Article
- Addressing annotation and data scarcity when designing machine learning strategies for neurophotonics.Neurophotonics · 2023Article
- Automatic granular and spinous epidermal cell identification and analysis onJournal of biomedical optics · 2023Article
- Label-free cell segmentation of diverse lymphoid tissues in 2D and 3D.Cell reports methods · 2023Article
- Targeting Apollo-NADPACS sensors · 2022Article
- Laminin matrix regulates beta-cell FGFR5 expression to enhance glucose-stimulated metabolism.Scientific reports · 2022Article
- Computational solutions for spatial transcriptomics.Computational and structural biotechnology journal · 2022Review
- Training a deep learning model for single-cell segmentation without manual annotation.Scientific reports · 2021Article
- Leveraging multimodal microscopy to optimize deep learning models for cell segmentation.APL bioengineering · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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Abstract
Deep learning provides an opportunity to automatically segment and extract cellular features from high-throughput microscopy images. Many labeling strategies have been developed for this purpose, ranging from the use of fluorescent markers to label-free approaches. However, differences in the channels available to each respective training dataset make it difficult to directly compare the effectiveness of these strategies across studies. Here, we explore training models using subimage stacks composed of channels sampled from larger, "hyper-labeled," image stacks. This allows us to directly compare a variety of labeling strategies and training approaches on identical cells. This approach revealed that fluorescence-based strategies generally provide higher segmentation accuracies but were less accurate than label-free models when labeling was inconsistent. The relative strengths of label and label-free techniques could be combined through the use of merging fluorescence channels and using out-of-focus brightfield images. Beyond comparing labeling strategies, using subimage stacks for training was also found to provide a method of simulating a wide range of labeling conditions, increasing the ability of the final model to accommodate a greater range of candidate cell labeling strategies.
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Registered trials
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