This disclosure relates to a model training method and apparatus and an image processing method and apparatus. The model training method includes: obtaining a first sample image and a first standard region proportion corresponding to a first object in the first sample image; obtaining a standard region segmentation result corresponding to the first sample image based on the first standard region proportion; and training a first initial segmentation model based on the first sample image and the standard region segmentation result, to obtain a first target segmentation model.

This disclosure relates to a model training method and apparatus and an image processing method and apparatus. The model training method includes: obtaining a first sample image and a first standard region proportion corresponding to a first object in the first sample image; obtaining a standard region segmentation result corresponding to the first sample image based on the first standard region proportion; and training a first initial segmentation model based on the first sample image and the standard region segmentation result, to obtain a first target segmentation model.

A system for performing adaptive focusing of a microscopy device comprises a microscopy device configured to acquire microscopy images depicting cells and one or more processors executing instructions for performing a method that includes extracting pixels from the microscopy images. Each set of pixels corresponds to an independent cell. The method further includes using a trained classifier to assign one of a plurality of image quality labels to each set of pixels indicating the degree to which the independent cell is in focus. If the image quality labels corresponding to the sets of pixels indicate that the cells are out of focus, a focal length adjustment for adjusting focus of the microscopy device is determined using a trained machine learning model. Then, executable instructions are sent to the microscopy device to perform the focal length adjustment.

A system for performing adaptive focusing of a microscopy device comprises a microscopy device configured to acquire microscopy images depicting cells and one or more processors executing instructions for performing a method that includes extracting pixels from the microscopy images. Each set of pixels corresponds to an independent cell. The method further includes using a trained classifier to assign one of a plurality of image quality labels to each set of pixels indicating the degree to which the independent cell is in focus. If the image quality labels corresponding to the sets of pixels indicate that the cells are out of focus, a focal length adjustment for adjusting focus of the microscopy device is determined using a trained machine learning model. Then, executable instructions are sent to the microscopy device to perform the focal length adjustment.

An image processing method is provided. For each frame of a video stream, a pixel digital frame mask in the respective frame of the video stream is obtained. The pixel digital frame mask of the respective frame includes a plurality of preset pixel position sets. At least two target preset pixel position sets are determined from the plurality of preset pixel position sets that form a frame sequence number of the respective frame based on values of pixels included in the at least two target preset pixel position sets. A frame sequence number corresponding to the respective frame of the video stream is determined according to positions of the at least two target preset pixel position sets in the pixel digital frame mask in the respective frame. Further, video fluency of the video stream is determined based on the frame sequence numbers.

An image processing method is provided. For each frame of a video stream, a pixel digital frame mask in the respective frame of the video stream is obtained. The pixel digital frame mask of the respective frame includes a plurality of preset pixel position sets. At least two target preset pixel position sets are determined from the plurality of preset pixel position sets that form a frame sequence number of the respective frame based on values of pixels included in the at least two target preset pixel position sets. A frame sequence number corresponding to the respective frame of the video stream is determined according to positions of the at least two target preset pixel position sets in the pixel digital frame mask in the respective frame. Further, video fluency of the video stream is determined based on the frame sequence numbers.

In some embodiments, a non-transitory processor-readable medium stores code representing instructions to be executed by a processor. The code includes code to cause the processor to receive a plurality of sets of images associated with a sample treated with fluorescence in situ hybridization (FISH) probes. Each image from that set of images is associated with a different focal length using a fluorescence microscope. Each FISH probe can selectively bind to a unique location on chromosomal DNA in the sample. The code further causes the processor to identify cell nuclei in the images. The code further causes the processor to apply a convolutional neural network (CNN) to each set of images. The CNN is configured to identify a probe indication from a plurality of probe indications for that set of images. The code further causes the processor to identify the sample as containing circulating tumor cells.

A theme icon generation method includes obtaining an application icon, where the application icon includes a transparent region and an opaque region, and the opaque region includes an icon background and a first logo graphic; segmenting a first logo graphic from the opaque region; adjusting a size of the first logo graphic to generate a second logo graphic; and fusing the second logo graphic with a theme template to generate a theme icon

An apparatus for determining defective hair follicles includes an image acquiring unit for acquiring an image of a follicle and a hair for each follicle separated from a scalp of an alopecic patient in an incisional hair transplant or each follicle directly extracted from an alopecic patient in a non-incisional hair transplant, an image processing unit for extracting an outline pattern of the image of the follicle and the hair by performing a contour detection process or an edge detection process on the image, a follicle shape database for storing hair pixel patterns related to various shapes of hairs and follicle pixel patterns related to various shapes of follicles, and a follicle determining unit for determining whether a follicle is normal follicle or defective follicle by comparing the outline pattern of the image with the hair pixel patterns and follicle pixel patterns stored in the follicle shape database.

An apparatus for determining defective hair follicles includes an image acquiring unit for acquiring an image of a follicle and a hair for each follicle separated from a scalp of an alopecic patient in an incisional hair transplant or each follicle directly extracted from an alopecic patient in a non-incisional hair transplant, an image processing unit for extracting an outline pattern of the image of the follicle and the hair by performing a contour detection process or an edge detection process on the image, a follicle shape database for storing hair pixel patterns related to various shapes of hairs and follicle pixel patterns related to various shapes of follicles, and a follicle determining unit for determining whether a follicle is normal follicle or defective follicle by comparing the outline pattern of the image with the hair pixel patterns and follicle pixel patterns stored in the follicle shape database.