Related Experiment Video
Updated: Feb 5, 2026
13:19
Deep Neural Networks for Image-Based Dietary Assessment
Published on: March 13, 2021
10.0K
The application of convolution neural network based cell segmentation during cryopreservation.
Momoh Karmah Mbogba1, Zeeshan Haider1, S M Chapal Hossain1
1Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027, China.
Cryobiology
|September 17, 2018
Summary
A novel convolutional neural network (CNN) accurately segments cells during cryopreservation. This image processing breakthrough overcomes challenges posed by ice formation, improving cryopreservation protocols.
Area of Science:
- Cell biology
- Biophysics
- Image analysis
Background:
- Accurate cell segmentation is crucial for cryopreservation, but challenging due to ice formation and variable backgrounds at subzero temperatures.
- Existing segmentation techniques struggle with the complexities of cryo-stage microscopy images.
Purpose of the Study:
- To develop a robust and accurate segmentation approach for extracting cell boundaries during cryopreservation.
- To address the limitations of conventional algorithms in handling ice formation and background variations.
Main Methods:
- A modified U-Net-like convolutional neural network (CNN) architecture was designed for cell boundary extraction.
- Images were acquired using a cryo-stage microscope.
- Segmentation results were validated using Hausdorff distance and statistical analysis.
Main Results:
- The proposed CNN model effectively extracted cell contours from the background in subzero conditions.
- The CNN approach demonstrated superior coherence and effectiveness compared to traditional segmentation methods.
- Validation confirmed the model's accuracy in segmenting cells during freezing.
Conclusions:
- The developed CNN model provides a more accurate and reliable method for cell segmentation in cryopreservation.
- This advancement can significantly improve the optimization of cryopreservation protocols by enabling precise measurement of cell properties.
- The study highlights the potential of deep learning in overcoming challenges in biophysical image analysis.
Related Concept Videos
01:17Convolution Properties II
588
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
588
02:26Protein Networks
4.6K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.6K
02:26Protein Networks
2.9K
2.9K
01:20Convolution Properties I
606
Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
606
02:18Network Covalent Solids
16.2K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.2K
01:24Convolution: Math, Graphics, and Discrete Signals
955
In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
955