CPC G06V 20/69 (2022.01) [G06T 3/4046 (2013.01); G06T 7/0012 (2013.01); G06T 7/10 (2017.01); G06T 2207/10056 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30024 (2013.01)] | 13 Claims |
1. A system for identifying cells on a microscopic image, comprising,
a non-transitory processor-readable medium that stores processor-readable instructions; and
a processor communicably coupled to the non-transitory processor-readable medium and configured to,
receive the microscopic image; and
process the received microscopic image with a convolutional neural network (CNN) model, wherein the CNN model has a U-Net architecture comprising,
a plurality of down-sampling sets for extracting the features of the microscopic image thereby generating feature maps; and
a plurality of up-sampling sets respectively for generating a segmented image from the feature maps; wherein
each down-sampling set comprises at least one convolution layer and at least one pooling layer preceded by the convolution layer; and
the extraction is carried out by the plurality of down-sampling sets on a set-by-set basis by using equation (1),
where x and y are respectively the pixels of the height and width of an input image processed by each down-sampling set, C is the channel number of the input image, h and w are respectively the pixels of the height and width of the microscopic image, S is the stride of the pooling layer, i is the number of each down-sampling set in the plurality of down-sampling sets, and F is a constant for deciding the channel number of the input image.
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