CN111709911A - Ovarian follicle automatic counting method based on neural network - Google Patents

Ovarian follicle automatic counting method based on neural network Download PDF

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CN111709911A
CN111709911A CN202010418981.7A CN202010418981A CN111709911A CN 111709911 A CN111709911 A CN 111709911A CN 202010418981 A CN202010418981 A CN 202010418981A CN 111709911 A CN111709911 A CN 111709911A
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蔡辉煌
程雨夏
吴卿
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Hangzhou Dianzi University
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Abstract

The invention discloses an ovarian follicle automatic counting method based on a neural network, which generates different training sets, verification sets and test sets for each time by adopting a random seed mode for all data images; continuously updating the network parameters through training of the neural network, and verifying through the IOU of the verification set so as to keep the optimal parameters of the network model; carrying out thresholding processing and noise processing on a prediction image output by a neural network to remove noise in the prediction image and convert an image into a gray image; then separating the follicles which are contacted with each other by using a distance conversion algorithm and a watershed algorithm; and finally, counting the number of the ovarian follicles by using a connected region analysis method to realize the counting function of the ovarian follicles.

Description

Ovarian follicle automatic counting method based on neural network
Technical Field
The invention relates to the field of deep learning of Watershed algorithm (Watershed) and CNN (rational Neural network), in particular to a method for automatically counting ovarian follicles of mice.
Background
The ovary is a complex endocrine organ, is an important gonadal organ for female reproduction and mainly has the functions of ovulation and female hormone secretion. Among them, the follicle plays a crucial part in the ovary, and the follicle is composed of an oocyte and many small follicular cells. The follicles in the ovary can be divided into six types according to the changes in morphology and function during follicle development: primordial follicles, primary follicles, secondary follicles, pre-antral follicles, and tertiary follicles. In the absence of any drug, normal women can find in the ovary that small follicles grow gradually to grow up and develop into follicles in different stages in one month, and a mature follicle is discharged in the ovulation phase. However, genetic mutations, toxins and some specific drugs have an effect on follicles. This is important in order to determine whether these effects are promoting or inhibiting for ovulation of the follicles.
The convolutional neural network is composed of a plurality of convolutional layers and fully-connected layers (corresponding to a classical neural network), and also comprises an activation layer and a pooling layer. Compared with other deep learning structures, the convolutional neural network can give better results in the aspects of image segmentation and recognition.
The watershed algorithm is an image segmentation algorithm based on analysis of geographic morphology, and simulates geographic structures (such as mountains, ravines, basins) to segment different objects.
Due to this importance, current stage is mainly directed to microscopic images of mouse ovary. The data set was obtained by staining and labeling the ovaries of the mice, and then by studying the development of follicles in the reproductive system of the mice and determining the number of their various follicle types.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a neural network-based ovarian follicle automatic counting method.
In order to solve the problem, the invention is realized by the following technical scheme:
a follicle counting method based on a neural network and a watershed algorithm comprises the following steps:
the method comprises the following steps: and dividing the data set into a training set, a verification set and a test set by using all data in a random seed mode. The data input by the neural network each time comprises an original picture and a marked picture;
step two: processing the pre-input pictures by using a data enhancement method for a training set when a network model is trained, and converting the picture data into a vector form;
step three: loading parameters of a pre-training model to initialize a neural network, executing forward propagation to calculate network parameters, and obtaining an output vectorO iObtaining a classification result vector y of each pixel point through a Log _ Softmax functioni(ii) a This vector yiThe maximum value of the medium weight is the final classification result predicted by the network for each pixel point, and the specific formula is yi＝log_softmax(O i) ); the obtained classification result yiAnd the current correct tag value
Figure BDA0002496127480000021
Respectively serving as two inputs of an NLLloss loss function, and calculating a loss value; transmitting the error signal to the output of each layer, and obtaining the gradient of the network parameter through the derivative of the function of each layer to the network parameter; and updating and calculating network parameters influencing model training and model output by a stochastic gradient descent method optimizer to enable the network parameters to approach or reach an optimal value, so that a loss function is minimized or maximized.
Step four: after each training, calculating the cross-over ratio of the verification set
Figure BDA0002496127480000022
And storing the maximum model parameter of IOU when the training times are up toStopping training at a certain number of times, and loading the model parameter with the largest IOU to obtain the final model of the neural network, wherein
Figure BDA0002496127480000023
Is a true value, and y is a predicted value;
step five: the model obtained in the fourth step is an optimal model, input data are transmitted into a trained neural network to obtain an output result, the output image format is an RGB image, the RGB image is converted into a gray image through graying, an image gray histogram is established to select threshold values capable of partitioning different follicles, and then the follicles are partitioned according to the different threshold values;
step six: respectively processing the noise of the pictures obtained in the step five by using an on operation
Figure BDA0002496127480000024
For eliminating small objects, using closed-loop operations
Figure BDA0002496127480000025
Filling small cavities in the object, wherein
Figure BDA0002496127480000026
And
Figure BDA0002496127480000027
respectively, expansion and corrosion;
step seven: by distance conversion
Figure BDA0002496127480000028
Calculating the distance from the value of each pixel to the nearest background pixel to obtain Euclidean distance map, and then finding the final erosion point of each local area, namely the closer to the center of the follicle, the larger the corresponding value, wherein X is the target point, B isxIs the closest background point to X;
step eight: then, taking the maximum value of each local area as a water injection point of a watershed, and expanding the area of each mark point from the points as much as possible, namely, either until the edge of the local area reaches or the edge of the area of another mark point reaches, thereby segmenting the follicles which are contacted with each other;
step nine: setting different thresholds for different types of follicles, judging the follicles to be noise when the area in the picture is smaller than a given threshold, analyzing and calculating the number of the connected regions in the picture by adopting the connected regions of the four neighborhoods, and marking the number of the connected regions, wherein the number of the connected regions is the number of the follicles.
Preferably, the marking picture marks different follicle types through different colors.
Preferably, the data enhancement method comprises random horizontal flipping, random vertical flipping, 360-degree random rotation, contrast, brightness, saturation, sharpening and standardization.
Compared with the prior art, the invention has the following effects:
1) the invention provides a novel ovarian follicle counting mode, which counts ovarian follicles by combining a convolution neural network of semantic segmentation and watershed calculation for the first time.
2) The number counted by the counting mode is closer to the real data, and the accuracy is higher.
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FIG. 1 is an overall flow diagram of the present invention;
fig. 2 is a diagram of a neural network architecture employed.
Detailed Description
The invention is further described with reference to the following detailed description and accompanying drawings:
fig. 1 shows a novel method for ovarian follicle counting based on neural network and watershed algorithm:
1) and dividing the data set into a training set, a verification set and a test set by using all data in a random seed mode. The data input by the neural network each time comprises an original picture and a marked picture, wherein the marked picture marks different follicle types through different colors.
2) When a network model is trained, processing a pre-input picture by using a data enhancement method (random horizontal turning, random vertical turning, 360-degree random rotation, contrast, brightness, saturation, sharpening and standardization) on a training set, and converting picture data into a vector form;
3) loading parameters of the pre-trained model to initialize the neural network (as shown in FIG. 2), performing forward propagation to calculate network parameters, and obtaining the output vectorO iObtaining a classification result vector y of each pixel point through a Log _ Softmax functioni(ii) a This vector yiThe maximum value of the medium weight is the final classification result predicted by the network for each pixel point, and the specific formula is yi＝log_softmax(O i) ); the obtained classification result yiAnd the current correct tag value
Figure BDA0002496127480000031
Respectively serving as two inputs of an NLLloss loss function, and calculating a loss value; transmitting the error signal to the output of each layer, and obtaining the gradient of the network parameter through the derivative of the function of each layer to the network parameter; and updating and calculating network parameters influencing model training and model output by a stochastic gradient descent method optimizer to enable the network parameters to approach or reach an optimal value, so that a loss function is minimized or maximized.
4) After each training, calculating the cross-over ratio of the verification set
Figure BDA0002496127480000032
Storing the maximum model parameter of IOU, stopping training when the training times reach a certain number, and loading the maximum model parameter of IOU to obtain the final model of the neural network, wherein
Figure BDA0002496127480000033
Is a true value, and y is a predicted value;
5) the model obtained in the fourth step is an optimal model, input data are transmitted into a trained neural network to obtain an output result, the output image format is an RGB image, the RGB image is converted into a gray image through graying, an image gray histogram is established to select threshold values capable of partitioning different follicles, and then the follicles are partitioned according to the different threshold values;
6) respectively processing the noise of the pictures obtained in the step five by using an on operation
Figure BDA0002496127480000041
For eliminating small objects, using closed-loop operations
Figure BDA0002496127480000042
Filling small cavities in the object, wherein
Figure BDA0002496127480000043
And
Figure BDA0002496127480000044
respectively, expansion and corrosion;
7) by distance conversion
Figure BDA0002496127480000045
Calculating the distance from the value of each pixel to the nearest background pixel to obtain Euclidean distance map, and then finding the final erosion point of each local area, namely the closer to the center of the follicle, the larger the corresponding value, wherein X is the target point, B isxIs the closest background point to X;
8) then, taking the maximum value of each local area as a water injection point of a watershed, and expanding the area of each mark point from the points as much as possible, namely, either until the edge of the local area reaches or the edge of the area of another mark point reaches, thereby segmenting the follicles which are contacted with each other;
9) setting different thresholds for different types of follicles, judging the follicles to be noise when the area in the picture is smaller than a given threshold, analyzing and calculating the number of the connected regions in the picture by adopting the connected regions of the four neighborhoods, and marking the number of the connected regions, wherein the number of the connected regions is the number of the follicles.

Claims (3)

1. A method for automatically counting ovarian follicles based on a neural network is characterized by comprising the following steps:
the method comprises the following steps: dividing a data set into a training set, a verification set and a test set by using all data in a random seed mode; the data input by the neural network each time comprises an original picture and a marked picture;
step two: processing the pre-input pictures by using a data enhancement method for a training set when a network model is trained, and converting the picture data into a vector form;
step three: loading parameters of a pre-training model to initialize a neural network, executing forward propagation to calculate network parameters, and obtaining an output vectorO iObtaining a classification result vector y of each pixel point through a Log _ Softmax functioni(ii) a This vector yiThe maximum value of the medium weight is the final classification result predicted by the network for each pixel point, and the specific formula is yi＝log_soft max(O i) ); the obtained classification result yiAnd the current correct tag value
Figure FDA0002496127470000011
Respectively serving as two inputs of an NLLloss loss function, and calculating a loss value; transmitting the error signal to the output of each layer, and obtaining the gradient of the network parameter through the derivative of the function of each layer to the network parameter; updating and calculating network parameters influencing model training and model output by a random gradient descent method optimizer to enable the network parameters to approach or reach an optimal value, so that a loss function is minimized or maximized;
step four: after each training, calculating the cross-over ratio of the verification set
Figure FDA0002496127470000012
Storing the maximum model parameter of IOU, stopping training when the training times reach a certain number, and loading the maximum model parameter of IOU to obtain the final model of the neural network, wherein
Figure FDA0002496127470000013
Is a true value, and y is a predicted value;
step five: the model obtained in the fourth step is an optimal model, input data are transmitted into a trained neural network to obtain an output result, the output image format is an RGB image, the RGB image is converted into a gray image through graying, an image gray histogram is established to select threshold values capable of partitioning different follicles, and then the follicles are partitioned according to the different threshold values;
step six: respectively processing the noise of the pictures obtained in the step five by using an on operation
Figure FDA0002496127470000014
For eliminating small objects, using closed-loop operations
Figure FDA0002496127470000015
Filling small cavities in the object, wherein
Figure FDA0002496127470000016
And
Figure FDA0002496127470000017
respectively, expansion and corrosion;
step seven: by distance conversion
Figure FDA0002496127470000018
Calculating the distance from the value of each pixel to the nearest background pixel to obtain Euclidean distance map, and then finding the final erosion point of each local area, namely the closer to the center of the follicle, the larger the corresponding value, wherein X is the target point, B isxIs the closest background point to X;
step eight: then, taking the maximum value of each local area as a water injection point of a watershed, and expanding the area of each mark point from the points as much as possible, namely, either until the edge of the local area reaches or the edge of the area of another mark point reaches, thereby segmenting the follicles which are contacted with each other;
step nine: setting different thresholds for different types of follicles, judging the follicles to be noise when the area in the picture is smaller than a given threshold, analyzing and calculating the number of the connected regions in the picture by adopting the connected regions of the four neighborhoods, and marking the number of the connected regions, wherein the number of the connected regions is the number of the follicles.
2. The method for neural network-based ovarian follicle auto-counting according to claim 1, characterized in that: the marking picture marks different follicle types through different colors.
3. The method for neural network-based ovarian follicle auto-counting according to claim 1, characterized in that: the data enhancement method comprises random horizontal turning, random vertical turning, 360-degree random rotation, contrast, brightness, saturation, sharpening and standardization.
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CN113744184A (en) * 2021-07-27 2021-12-03 江苏农林职业技术学院 Snakehead ovum counting method based on image processing
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