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Optimal sparsity allows reliable system-aware restoration of fluorescence microscopy images.

ABSTRACT: Fluorescence microscopy is one of the most indispensable and informative driving forces for biological research, but the extent of observable biological phenomena is essentially determined by the content and quality of the acquired images. To address the different noise sources that can degrade these images, we introduce an algorithm for multiscale image restoration through optimally sparse representation (MIRO). MIRO is a deterministic framework that models the acquisition process and uses pixelwise noise correction to improve image quality. Our study demonstrates that this approach yields a remarkable restoration of the fluorescence signal for a wide range of microscopy systems, regardless of the detector used (e.g., electron-multiplying charge-coupled device, scientific complementary metal-oxide semiconductor, or photomultiplier tube). MIRO improves current imaging capabilities, enabling fast, low-light optical microscopy, accurate image analysis, and robust machine intelligence when integrated with deep neural networks. This expands the range of biological knowledge that can be obtained from fluorescence microscopy.

SUBMITTER: Mandracchia B

PROVIDER: S-EPMC10468132 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

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Optimal sparsity allows reliable system-aware restoration of fluorescence microscopy images.

Mandracchia Biagio B Liu Wenhao W Hua Xuanwen X Forghani Parvin P Lee Soojung S Hou Jessica J Nie Shuyi S Xu Chunhui C Jia Shu S

Science advances 20230830 35

Fluorescence microscopy is one of the most indispensable and informative driving forces for biological research, but the extent of observable biological phenomena is essentially determined by the content and quality of the acquired images. To address the different noise sources that can degrade these images, we introduce an algorithm for multiscale image restoration through optimally sparse representation (MIRO). MIRO is a deterministic framework that models the acquisition process and uses pixe ...[more]

PMID: 37647399

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Project description:Autofluorescence microscopy uses intrinsic sources of molecular contrast to provide cellular-level information without extrinsic labels. However, traditional cell segmentation tools are often optimized for high signal-to-noise ratio (SNR) images, such as fluorescently labeled cells, and unsurprisingly perform poorly on low SNR autofluorescence images. Therefore, new cell segmentation tools are needed for autofluorescence microscopy. Cellpose is a deep learning network that is generalizable across diverse cell microscopy images and automatically segments single cells to improve throughput and reduce inter-human biases. This study aims to validate Cellpose for autofluorescence imaging, specifically using multiphoton intensity images of NAD(P)H. Manually segmented nuclear masks of NAD(P)H images were used to train a new autofluorescence-trained model (ATM) in Cellpose for nuclear segmentation of NAD(P)H intensity images. These models were applied to PANC-1 cells treated with metabolic inhibitors and patient-derived cancer organoids (9 patients) treated with chemotherapies. These datasets include co-registered fluorescence lifetime imaging microscopy (FLIM) of NAD(P)H and FAD, so fluorescence decay parameters and the optical redox ratio (ORR) were compared between masks generated by the new ATM and manual segmentation. The Dice score between repeated manually segmented masks was significantly lower than that of repeated ATM masks (p < 0.0001) indicating greater reproducibility between ATM masks. There was also a high correlation (R2 > 0.9) between ATM and manually segmented masks for the ORR, mean NAD(P)H lifetime, and mean FAD lifetime across 2D and 3D cell culture treatment conditions. Masks generated from ATM and manual segmentation also maintain similar means, variances, and effect sizes between treatments for the ORR and FLIM parameters. Overall, the Cellpose ATM provides a fast, reliable, reproducible, and accurate method to segment single cells in autofluorescence microscopy images such that functional changes in cells are accurately captured in both 2D and 3D culture.

Dataset Information

Optimal sparsity allows reliable system-aware restoration of fluorescence microscopy images.

Publications

Optimal sparsity allows reliable system-aware restoration of fluorescence microscopy images.

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