Dataset Information

TestSTORM: Versatile simulator software for multimodal super-resolution localization fluorescence microscopy.

ABSTRACT: Optimization of sample, imaging and data processing parameters is an essential task in localization based super-resolution microscopy, where the final image quality strongly depends on the imaging of single isolated fluorescent molecules. A computational solution that uses a simulator software for the generation of test data stacks was proposed, developed and tested. The implemented advanced physical models such as scalar and vector based point spread functions, polarization sensitive detection, drift, spectral crosstalk, structured background etc., made the simulation results more realistic and helped us interpret the final super-resolved images and distinguish between real structures and imaging artefacts.

SUBMITTER: Novak T

PROVIDER: S-EPMC5430448 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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TestSTORM: Versatile simulator software for multimodal super-resolution localization fluorescence microscopy.

Novák Tibor T Gajdos Tamás T Sinkó József J Szabó Gábor G Erdélyi Miklós M

Scientific reports 20170419 1

Optimization of sample, imaging and data processing parameters is an essential task in localization based super-resolution microscopy, where the final image quality strongly depends on the imaging of single isolated fluorescent molecules. A computational solution that uses a simulator software for the generation of test data stacks was proposed, developed and tested. The implemented advanced physical models such as scalar and vector based point spread functions, polarization sensitive detection, ...[more]

PMID: 28424492

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Project description:BackgroundAutomated segmentation of nuclei in microscopic images has been conducted to enhance throughput in pathological diagnostics and biological research. Segmentation accuracy and speed has been significantly enhanced with the advent of convolutional neural networks. A barrier in the broad application of neural networks to nuclei segmentation is the necessity to train the network using a set of application specific images and image labels. Previous works have attempted to create broadly trained networks for universal nuclei segmentation; however, such networks do not work on all imaging modalities, and best results are still commonly found when the network is retrained on user specific data. Stochastic optical reconstruction microscopy (STORM) based super-resolution fluorescence microscopy has opened a new avenue to image nuclear architecture at nanoscale resolutions. Due to the large size and discontinuous features typical of super-resolution images, automatic nuclei segmentation can be difficult. In this study, we apply commonly used networks (Mask R-CNN and UNet architectures) towards the task of segmenting super-resolution images of nuclei. First, we assess whether networks broadly trained on conventional fluorescence microscopy datasets can accurately segment super-resolution images. Then, we compare the resultant segmentations with results obtained using networks trained directly on our super-resolution data. We next attempt to optimize and compare segmentation accuracy using three different neural network architectures.ResultsResults indicate that super-resolution images are not broadly compatible with neural networks trained on conventional bright-field or fluorescence microscopy images. When the networks were trained on super-resolution data, however, we attained nuclei segmentation accuracies (F1-Score) in excess of 0.8, comparable to past results found when conducting nuclei segmentation on conventional fluorescence microscopy images. Overall, we achieved the best results utilizing the Mask R-CNN architecture.ConclusionsWe found that convolutional neural networks are powerful tools capable of accurately and quickly segmenting localization-based super-resolution microscopy images of nuclei. While broadly trained and widely applicable segmentation algorithms are desirable for quick use with minimal input, optimal results are still found when the network is both trained and tested on visually similar images. We provide a set of Colab notebooks to disseminate the software into the broad scientific community ( https://github.com/YangLiuLab/Super-Resolution-Nuclei-Segmentation ).

Dataset Information

TestSTORM: Versatile simulator software for multimodal super-resolution localization fluorescence microscopy.

Publications

TestSTORM: Versatile simulator software for multimodal super-resolution localization fluorescence microscopy.

Similar Datasets

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