DeepHCS<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:msup><mml:mrow/><mml:mrow><mml:mo>+</mml:mo><mml:mo>+</mml:mo></mml:mrow></mml:msup></mml:math>: Bright-field to fluorescence microscopy image conversion using multi-task learning with adversarial losses for label-free high-content screening

Lee, Gyuhyun; Oh, Jeong-Woo; Her, Nam-Gu; Jeong, Won-Ki

doi:10.1016/j.media.2021.101995

Cited by 21 publications

(14 citation statements)

References 22 publications

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“…In ART for humans and livestock, it is not acceptable to perform live-cell imaging labelled by fluorescent staining because of potential deleterious effects on the offspring. In recent years, some studies have proposed methods to enable highly accurate image analysis, such as segmentation, for data of various modalities (e.g., brightfield microscope images and fluorescence microscope images) [41][42][43][44].…”

Section: Discussionmentioning

confidence: 99%

Deep learning-based algorithm for predicting the live birth potential of mouse embryos

Tokuoka

Yamada

Mashiko

et al. 2021

Preprint

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In assisted reproductive technology (ART), embryos produced by in vitro fertilization (IVF) are graded according to their live birth potential, and high-grade embryos are preferentially transplanted. However, the rate of live birth following clinical ART remains low worldwide, suggesting that grading is inaccurate. One explanation is that grading is classically based on the characteristic shape of embryos at a limited number of developmental stages and does not consider the shape of embryos and intracellular structures, e.g., nuclei, at various stages important for normal embryogenesis. Therefore, here we developed a Normalized Multi-View Attention Network (NVAN) that directly predicts live birth potential from nuclear structural features in live-cell fluorescence images taken of mouse embryos across a wide range of stages. The classification accuracy of our method was 83.87%, which greatly exceeded that of existing machine-learning methods and that of visual inspection by embryo culture specialists. By visualizing the features that contributed most to the prediction of live birth potential, we found that the size and shape of the cell nucleus at the morula stage and at the time of cell division were important for live birth prediction. We anticipate that our method will help ART and developmental engineering as a new basic technology for IVF embryo selection.

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Section: Discussionmentioning

confidence: 99%

Deep learning-based algorithm for predicting the live birth potential of mouse embryos

Tokuoka

Yamada

Mashiko

et al. 2021

Preprint

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“…Lee et al provided the DeepHCS++ model for the fluorescent image translation task ( 40 ) . The architecture of the DeepHCS++ model consisted of two parts: transformation and refinement networks.…”

Section: Theoretical Preliminariesmentioning

confidence: 99%

“…Conditional adversarial loss calculations were only applied to the final output images rather than intermediate products. Results showed that the performance for translation-refinement networks was better than single translation networks ( 40 ) .…”

Section: Theoretical Preliminariesmentioning

confidence: 99%

Bright-field to fluorescence microscopy image translation for cell nuclei health quantification

Wang¹,

Butt²,

Cross³

et al. 2023

Biol. Imaging

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Microscopy is a widely used method in biological research to observe the morphology and structure of cells. Amongst the plethora of microscopy techniques, fluorescent labeling with dyes or antibodies is the most popular method for revealing specific cellular organelles. However, fluorescent labeling also introduces new challenges to cellular observation, as it increases the workload, and the process may result in nonspecific labeling. Recent advances in deep visual learning have shown that there are systematic relationships between fluorescent and bright-field images, thus facilitating image translation between the two. In this article, we propose the cross-attention conditional generative adversarial network (XAcGAN) model. It employs state-of-the-art GANs (GANs) to solve the image translation task. The model uses supervised learning and combines attention-based networks to explore spatial information during translation. In addition, we demonstrate the successful application of XAcGAN to infer the health state of translated nuclei from bright-field microscopy images. The results show that our approach achieves excellent performance both in terms of image translation and nuclei state inference.

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“…Fluorescence microscopy, while providing specificity, can inadvertently induce phototoxic effects and cytotoxicity, as well as interfere with the molecular interactions of its targets [6]. The innovation of in silico recovery of fluorescence images from brightfield images marks a groundbreaking shift in cellular imaging [6, 7, 8]. This approach, driven by advancements in deep learning and computer vision, not only addresses the limitations of fluorescent labeling but also enhances the information available from brightfield images.…”

Section: Introductionmentioning

confidence: 99%

Self-supervised Vision Transformers for image-to-image labeling: a BiaPy solution to the LightMyCells Challenge

Franco-Barranco,

González-Marfil,

Arganda-Carreras

2024

Preprint

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Fluorescence microscopy plays a crucial role in cellular analysis but is often hindered by phototoxicity and limited spectral channels. Label-free transmitted light microscopy presents an attractive alternative, yet recovering fluorescence images from such inputs remains difficult. In this work, we address the Cell Painting problem within the LightMyCells challenge at the International Symposium on Biomedical Imaging (ISBI) 2024, aiming to predict optimally focused fluorescence images from label-free transmitted light inputs. Leveraging advancements self-supervised Vision Transformers, our method overcomes the constraints of scarce annotated biomedical data and fluorescence microscopy's drawbacks. Four specialized models, each targeting a different organelle, are pretrained in a self-supervised manner to enhance model generalization. Our method, integrated within the open-source BiaPy library, contributes to the advancement of image-to-image deep-learning techniques in cellular analysis, offering a promising solution for robust and accurate fluorescence image prediction from label-free transmitted light inputs. Code and documentation can be found at https://github.com/danifranco/BiaPy and a custom tutorial to reproduce all results is available at https://biapy.readthedocs.io/en/latest/tutorials/image-to-image/lightmycells.html.

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DeepHCS++: Bright-field to fluorescence microscopy image conversion using multi-task learning with adversarial losses for label-free high-content screening

Cited by 21 publications

References 22 publications

Deep learning-based algorithm for predicting the live birth potential of mouse embryos

Deep learning-based algorithm for predicting the live birth potential of mouse embryos

Bright-field to fluorescence microscopy image translation for cell nuclei health quantification

Self-supervised Vision Transformers for image-to-image labeling: a BiaPy solution to the LightMyCells Challenge

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