Spectroscopic Approach to Correction and Visualisation of Bright-Field Light Transmission Microscopy Biological Data

Platonova, Ganna; Štys, Dalibor; Souček, Pavel; Lonhus, Kirill; Valenta, J.; Rychtáriková, Renata

doi:10.3390/photonics8080333

Cited by 12 publications

(4 citation statements)

References 30 publications

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“…Several time-lapse experiments were completed with HeLa cells using a reflected bright-field microscope (Section 2.1). The microscope control software calibrated the microscope optical path and corrected all image series using the algorithm proposed in [27] to avoid image background inhomogeneities and noise.…”

Section: Data Preparation and Pre-processingmentioning

confidence: 99%

Symmetry Breaking in the U-Net: Hybrid Deep-Learning Multi-Class Segmentation of HeLa Cells in Reflected Light Microscopy Images

Ghaznavi,

Rychtáriková,

Císař

et al. 2024

Symmetry

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Multi-class segmentation of unlabelled living cells in time-lapse light microscopy images is challenging due to the temporal behaviour and changes in cell life cycles and the complexity of these images. The deep-learning-based methods achieved promising outcomes and remarkable success in single- and multi-class medical and microscopy image segmentation. The main objective of this study is to develop a hybrid deep-learning-based categorical segmentation and classification method for living HeLa cells in reflected light microscopy images. A symmetric simple U-Net and three asymmetric hybrid convolution neural networks—VGG19-U-Net, Inception-U-Net, and ResNet34-U-Net—were proposed and mutually compared to find the most suitable architecture for multi-class segmentation of our datasets. The inception module in the Inception-U-Net contained kernels with different sizes within the same layer to extract all feature descriptors. The series of residual blocks with the skip connections in each ResNet34-U-Net’s level alleviated the gradient vanishing problem and improved the generalisation ability. The m-IoU scores of multi-class segmentation for our datasets reached 0.7062, 0.7178, 0.7907, and 0.8067 for the simple U-Net, VGG19-U-Net, Inception-U-Net, and ResNet34-U-Net, respectively. For each class and the mean value across all classes, the most accurate multi-class semantic segmentation was achieved using the ResNet34-U-Net architecture (evaluated as the m-IoU and Dice metrics).

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Section: Data Preparation and Pre-processingmentioning

confidence: 99%

Symmetry Breaking in the U-Net: Hybrid Deep-Learning Multi-Class Segmentation of HeLa Cells in Reflected Light Microscopy Images

Ghaznavi,

Rychtáriková,

Císař

et al. 2024

Symmetry

Self Cite

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“…2.1). The algorithm proposed in [21] was fully automated and implemented in the microscope control software to calibrate the microscope optical path and correct all image series to avoid image background inhomogeneities and noise.…”

Section: Data Acquisitionmentioning

confidence: 99%

Cell segmentation from telecentric bright-field transmitted light microscopy images using a Residual Attention U-Net: a case study on HeLa line

Ghaznavi,

Rychtarikova,

Saberioon

et al. 2022

Preprint

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Living cell segmentation from bright-field light microscopic images is challenging due to the image complexity and temporal changes in the living cells. Recently developed deep learning (DL)-based methods became popular in medical and microscopic image segmentation tasks due to their success and promising outcomes. The main objective of this paper is to develop a deep learning, U-Net-based method to segment the living cells of the HeLa line in bright-field transmitted light microscopy. To find the most suitable architecture for our datasets, we have proposed a residual attention U-Net and compared it with an attention and a simple U-Net architecture. The attention mechanism highlights the remarkable features and suppresses activations in the irrelevant image regions. The residual mechanism overcomes with vanishing gradient problem. The Mean-IoU score for our datasets reaches 0.9505, 0.9524, and 0.9530 for the simple, attention, and residual attention U-Net, respectively. We achieved the most accurate semantic segmentation results in the Mean-IoU and Dice metrics by applying the residual and attention mechanisms together. The watershed method applied to this best -Residual Attention -semantic segmentation result gave the segmentation with the specific

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“…For biological and medical detection and analysis, a microscope is a very important optical instrument, one which directly promotes the development of biotechnology, medicine and pathology. Nowadays, research-level and desktop microscopes have been widely used in medical centers and scientific institutes [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ], including bright-field microscopes, dark-field microscopes, fluorescence microscopes, Zernike-phase-contrast (ZPC) microscopes, differential-interference-contrast (DIC) microscopes, laser confocal microscopes and super-resolution microscopy imaging systems, etc. These traditional microscopy imaging systems are suitable for hospitals and research-level laboratories.…”

Section: Introductionmentioning

confidence: 99%

Computational Portable Microscopes for Point-of-Care-Test and Tele-Diagnosis

Bian

Xing

Jiao

et al. 2022

Cells

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In bio-medical mobile workstations, e.g., the prevention of epidemic viruses/bacteria, outdoor field medical treatment and bio-chemical pollution monitoring, the conventional bench-top microscopic imaging equipment is limited. The comprehensive multi-mode (bright/dark field imaging, fluorescence excitation imaging, polarized light imaging, and differential interference microscopy imaging, etc.) biomedical microscopy imaging systems are generally large in size and expensive. They also require professional operation, which means high labor-cost, money-cost and time-cost. These characteristics prevent them from being applied in bio-medical mobile workstations. The bio-medical mobile workstations need microscopy systems which are inexpensive and able to handle fast, timely and large-scale deployment. The development of lightweight, low-cost and portable microscopic imaging devices can meet these demands. Presently, for the increasing needs of point-of-care-test and tele-diagnosis, high-performance computational portable microscopes are widely developed. Bluetooth modules, WLAN modules and 3G/4G/5G modules generally feature very small sizes and low prices. And industrial imaging lens, microscopy objective lens, and CMOS/CCD photoelectric image sensors are also available in small sizes and at low prices. Here we review and discuss these typical computational, portable and low-cost microscopes by refined specifications and schematics, from the aspect of optics, electronic, algorithms principle and typical bio-medical applications.

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Spectroscopic Approach to Correction and Visualisation of Bright-Field Light Transmission Microscopy Biological Data

Cited by 12 publications

References 30 publications

Symmetry Breaking in the U-Net: Hybrid Deep-Learning Multi-Class Segmentation of HeLa Cells in Reflected Light Microscopy Images

Symmetry Breaking in the U-Net: Hybrid Deep-Learning Multi-Class Segmentation of HeLa Cells in Reflected Light Microscopy Images

Cell segmentation from telecentric bright-field transmitted light microscopy images using a Residual Attention U-Net: a case study on HeLa line

Computational Portable Microscopes for Point-of-Care-Test and Tele-Diagnosis

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