Patent ID: 11880974
Assignee: ZHUHAI HENGQIN SANMED AITECH INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G  C | IPC G

Claim 10:
11. A non-transitory computer readable storage medium on which a computer program is stored, wherein the computer program, when executed by a processor, performs following steps of a method for detecting circulating abnormal cells:
segmenting and labelling cell nuclei included in dark field microscope images of a plurality of probe channels respectively, by using an image processing algorithm and a morphological algorithm;
inputting the dark field microscope images, in which the cell nuclei are labelled, of the plurality of probe channels into a pre-built circulating abnormal cell detection model to acquire a number of staining signals included in each of the labelled cell nuclei in the dark field microscope image of each of the probe channels; and
for each of the labelled cell nuclei, determining whether the labelled cell nucleus belongs to a circulating abnormal cell, based on the acquired number of the staining signals included in the labelled cell nucleus in the dark field microscope image of each of the probe channels, wherein building the circulating abnormal cell detection model comprises:
segmenting and labelling the cell nuclei included in the dark field microscope sample images of the plurality of probe channels respectively by using the image processing algorithm and the morphological algorithm, and segmenting the dark field microscope sample images, in which the cell nuclei are labelled, of the probe channels to acquire a plurality of cell nucleus sample images;
for each of the cell nucleus sample images, performing multiple convolution processes on the cell nucleus sample image to respectively acquire a first feature image, a second feature image, a third feature image, and a fourth feature image;
performing a convolution process on the fourth feature image to acquire a fifth feature image, and performing upsampling on the fifth feature image to acquire a sixth feature image;
performing a convolution process on the third feature image and the sixth feature image to acquire a seventh feature image, and performing upsampling on the seventh feature image to acquire an eighth feature image;
performing a convolution process on the second feature image and the eighth feature image to acquire a ninth feature image, and performing upsampling on the ninth feature image to acquire a tenth feature image;
performing a convolution process on the first feature image and the tenth feature image to acquire an eleventh feature image; and
training and testing a deep learning network by taking each of the cell nucleus sample images as an input of the deep learning network, fusing the seventh feature image, the ninth feature image, and the eleventh feature image as output prediction results in three scales of the deep learning network, and finally taking the number of the staining signals included in the labelled cell nuclei in the cell nucleus sample images as the output of the deep learning network, to acquire a circulating abnormal cell detection model,

wherein performing the multiple convolution processes on the cell nucleus sample image to respectively acquire the first feature image, the second feature image, the third feature image, and the fourth feature image comprises:
sequentially performing convolution processes of a first convolution layer, a second convolution layer, a third convolution layer, and a fourth convolution layer on the cell nucleus sample image to acquire the first feature image;
performing convolution processes of a fifth convolution layer and a sixth convolution layer on the first feature image to acquire the second feature image;
performing convolution processes of a seventh convolution layer and an eighth convolution layer on the second feature image to acquire the third feature image; and

performing convolution processes of a ninth convolution layer and a tenth convolution layer on the third feature image to acquire the fourth feature image.