Quantitative tomography of mouse early embryo

Pogorelova, M. A.; Yashin, V. A.; Pogorelov, A. G.; Голиченков, В. А.

doi:10.1134/s0012496608010213

Cited by 7 publications

(5 citation statements)

References 7 publications

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“…4b). The volume of the mouse embryo at the 2-cell stage has been determined as approximately 56,000 µm 3 [29]. Therefore, the scale of the nucleus volume was within that of the mouse embryo, and our value of the nuclear volume was reasonable.…”

Section: Acquisition Of Time-series Data Of Quantitative Criteria Of supporting

confidence: 53%

Convolutional Neural Network-Based Instance Segmentation Algorithm to Acquire Quantitative Criteria of Early Mouse Development

Tokuoka

Yamada

Hiroi

et al. 2018

Preprint

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Multicellular organisms develop from the fertilized eggs through numbers of cell divisions.While the pattern of cell division has specific rules for healthy development, some fluctuations for this pattern are allowable. In order to uncover this robust mechanism of development, the position of cells in each embryo must be analyzed quantitatively. In the embryonic developmental biology, various studies are attempted to acquire the quantitative criteria from time-series three-dimensional microscopic images by image analysis such as segmentation, to understand the mechanism of development. Unfortunately, due to the fact of inaccuracy of nuclei detection and segmentation, it is a hard task to evaluate an embryo quantitatively from bioimages automatically. Based on these demands of quantitative analysis, we developed QCA Net, which accurately performs nuclear segmentation of three-dimensional fluorescence microscopic images for early-stage mouse embryos. QCA Net is based on Convolu-tional Neural Network. We trained QCA Net using a part of one early-stage mouse embryo.As a test, QCA Net performed segmentation of different 11 mouse embryos images. We succeeded in accurately acquiring the shape of the nucleus without fusion of nuclear regions.Besides, we achieved accurate extraction of the time-series data of nuclear number, volume, surface area, and center of gravity coordinates as the quantitative criteria of mouse development, from the segmentation images acquired by QCA Net. To our surprise, these results suggested that QCA Net recognized and distinguished the nucleus and a polar body formed in meiosis process. We consider that QCA Net can drastically contribute to performing segmentation of various bioimages in the embryonic developmental biology. The various quantitative criteria obtained from segmented images can uncover various unknown mechanisms of embryonic development, such as the robust mechanism of development.The recent improvement of the microscopy and bioimaging technologies established technologies for live cell imaging 1-7 . This technology helped to get sufficient amount and quality of time-series three-dimensional (3D) fluorescence microscopic images of the development. In the embryonic developmental biology, various studies are attempted to acquire the quantitative criteria such as abnormal chromosome numbers, the synchrony of cell division, and the rate of development [8][9][10] . These studies analyzed time-series 3D microscopic images of the developmental embryo, whose cell nucleus is labeled fluorescently, utilizing image analysis such as segmentation. The previous segmentation methods in bioimage processing consisted of many types of image processing such as filtering, thresholding, morphological operation, watershed, and mask processing 4, 10-13 . These methods required parameter value to execute image processing. Even though the optimal value of parameters depends on the features of each image and the imaging condition of the microscopy system, the value is arbitrarily set by the analyst, and o...

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Section: Acquisition Of Time-series Data Of Quantitative Criteria Of supporting

confidence: 53%

Convolutional Neural Network-Based Instance Segmentation Algorithm to Acquire Quantitative Criteria of Early Mouse Development

Tokuoka

Yamada

Hiroi

et al. 2018

Preprint

View full text Add to dashboard Cite

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“…The volume of individual blastomeres was measured by quantitative LMT [2][3][4]14]. The specimens were then examined under a Leica TCS scanning microscope (Leica).…”

Section: Methodsmentioning

confidence: 99%

“…The cell volume was measured by quantitative laser microtomography (LMT) allowing volume measurements for objects (blastomere) with complex spatial structure [2][3][4]14]. …”

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confidence: 99%

Estimation of Isotonic Point of Incubation Medium For Two-Cell Mouse Embryo

et al. 2011

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Osmolarity of Dulbecco's medium at which the volume of two-cell mouse embryo remained similar to that of intact embryo was determined. The method is based on comparison of kinetic curves describing the volume of embryonic cell in solutions of different osmolarity. The blastomere volume was measured by quantitative laser microtomography after fixed osmotic stress intervals. It was found that Dulbecco's saline with 125 mM NaCl solution is an isotonic solution for two-cell mouse embryo. This concentration corresponds to 290 mOsm, which is lower than osmolarity (~310 mOsm) of media routinely used for culturing of differentiated cells or biological fluids, e.g. blood plasma.

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“…The volume of a blastomer was measured by quantitative microtomography [4,15]. The preparation was examined under an LSM 510 confocal microscope (Zeiss).…”

Section: Methodsmentioning

confidence: 99%

Analysis of Changes in Cell Volume of a Mouse Early Embryo Exposed to Osmotic Shock

et al. 2009

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The impact of the osmotic component of the incubation medium for the volume of mouse early embryonic cell was studied by laser scanning microscopy. Common Dulbecco's medium caused a prolonged hyperosmotic effect. Adaptive phase of regulatory compensation for the osmotic shock was observed under hypotonic conditions. From these data, water permeability of the blastomer membrane is evaluated as 0.4 micro/(minxatm).

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Quantitative tomography of mouse early embryo

Cited by 7 publications

References 7 publications

Convolutional Neural Network-Based Instance Segmentation Algorithm to Acquire Quantitative Criteria of Early Mouse Development

Convolutional Neural Network-Based Instance Segmentation Algorithm to Acquire Quantitative Criteria of Early Mouse Development

Estimation of Isotonic Point of Incubation Medium For Two-Cell Mouse Embryo

Analysis of Changes in Cell Volume of a Mouse Early Embryo Exposed to Osmotic Shock

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