Dataset Information

Deep learning autofluorescence-harmonic microscopy.

ABSTRACT: Laser scanning microscopy has inherent tradeoffs between imaging speed, field of view (FOV), and spatial resolution due to the limitations of sophisticated mechanical and optical setups, and deep learning networks have emerged to overcome these limitations without changing the system. Here, we demonstrate deep learning autofluorescence-harmonic microscopy (DLAM) based on self-alignment attention-guided residual-in-residual dense generative adversarial networks to close the gap between speed, FOV, and quality. Using the framework, we demonstrate label-free large-field multimodal imaging of clinicopathological tissues with enhanced spatial resolution and running time advantages. Statistical quality assessments show that the attention-guided residual dense connections minimize the persistent noise, distortions, and scanning fringes that degrade the autofluorescence-harmonic images and avoid reconstruction artifacts in the output images. With the advantages of high contrast, high fidelity, and high speed in image reconstruction, DLAM can act as a powerful tool for the noninvasive evaluation of diseases, neural activity, and embryogenesis.

SUBMITTER: Shen B

PROVIDER: S-EPMC8964717 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

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Deep learning autofluorescence-harmonic microscopy.

Shen Binglin B Liu Shaowen S Li Yanping Y Pan Ying Y Lu Yuan Y Hu Rui R Qu Junle J Liu Liwei L

Light, science & applications 20220329 1

Laser scanning microscopy has inherent tradeoffs between imaging speed, field of view (FOV), and spatial resolution due to the limitations of sophisticated mechanical and optical setups, and deep learning networks have emerged to overcome these limitations without changing the system. Here, we demonstrate deep learning autofluorescence-harmonic microscopy (DLAM) based on self-alignment attention-guided residual-in-residual dense generative adversarial networks to close the gap between speed, FOV ...[more]

PMID: 35351853

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Project description:BackgroundIn diseases such as interstitial lung diseases (ILDs), patient diagnosis relies on diagnostic analysis of bronchoalveolar lavage fluid (BALF) and biopsies. Immunological BALF analysis includes differentiation of leukocytes by standard cytological techniques that are labor-intensive and time-consuming. Studies have shown promising leukocyte identification performance on blood fractions, using third harmonic generation (THG) and multiphoton excited autofluorescence (MPEF) microscopy.ObjectiveTo extend leukocyte differentiation to BALF samples using THG/MPEF microscopy, and to show the potential of a trained deep learning algorithm for automated leukocyte identification and quantification.MethodsLeukocytes from blood obtained from three healthy individuals and one asthma patient, and BALF samples from six ILD patients were isolated and imaged using label-free microscopy. The cytological characteristics of leukocytes, including neutrophils, eosinophils, lymphocytes, and macrophages, in terms of cellular and nuclear morphology, and THG and MPEF signal intensity, were determined. A deep learning model was trained on 2D images and used to estimate the leukocyte ratios at the image-level using the differential cell counts obtained using standard cytological techniques as reference.ResultsDifferent leukocyte populations were identified in BALF samples using label-free microscopy, showing distinctive cytological characteristics. Based on the THG/MPEF images, the deep learning network has learned to identify individual cells and was able to provide a reasonable estimate of the leukocyte percentage, reaching >90% accuracy on BALF samples in the hold-out testing set.ConclusionsLabel-free THG/MPEF microscopy in combination with deep learning is a promising technique for instant differentiation and quantification of leukocytes. Immediate feedback on leukocyte ratios has potential to speed-up the diagnostic process and to reduce costs, workload and inter-observer variations.

Dataset Information

Deep learning autofluorescence-harmonic microscopy.

Publications

Deep learning autofluorescence-harmonic microscopy.

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