Related Experiment Video
Updated: Jan 30, 2026
Transpupillary Two-Photon In Vivo Imaging of the Mouse Retina
Published on: February 13, 2021
Efficient implementation of convolutional neural networks in the data processing of two-photon in vivo imaging
Yangzhen Wang1,2,3, Feng Su1,3,4,5, Shanshan Wang1,3
1School of Basic Medical Sciences, Beijing Key Laboratory of Neural Regeneration and Repair, Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
Motivation:
Functional imaging at single-neuron resolution offers a highly efficient tool for studying the functional connectomics in the brain. However, mainstream neuron-detection methods focus on either the morphologies or activities of neurons, which may lead to the extraction of incomplete information and which may heavily rely on the experience of the experimenters.
Results:
We developed a convolutional neural networks and fluctuation method-based toolbox (ImageCN) to increase the processing power of calcium imaging data. To evaluate the performance of ImageCN, nine different imaging datasets were recorded from awake mouse brains. ImageCN demonstrated superior neuron-detection performance when compared with other algorithms. Furthermore, ImageCN does not require sophisticated training for users.
Availability And Implementation:
ImageCN is implemented in MATLAB. The source code and documentation are available at https://github.com/ZhangChenLab/ImageCN.
Supplementary Information:
Supplementary data are available at Bioinformatics online.
Related Concept Videos
01:17Convolution Properties II
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...
01:25Role of Communication in the Nursing Process II: Planning and Implementation
01:24Nursing Process for Patient and Caregiver Teaching II: Planning and Implementation
02:26Protein Networks
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,...
01:20Convolution Properties I
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:
01:15Nursing Implementation
The five steps to implementing effective nursing care include reassessing the patient, reviewing and revising the existing nursing care plan, organizing the resources and care delivery, anticipating and preventing complications, and implementing nursing interventions.