Method and apparatus for processing fundus image

An apparatus for processing a fundus image, comprising:

• at least one processor;
• and
• a memory storing instructions, the instructions when executed by the at least one processor, cause the at least one processor to perform operations, the operations comprising:
• acquiring a target fundus image;
• dividing the target fundus image into at least two first image blocks;
• inputting a first image block into a pre-trained deep learning model, to obtain a first output value, wherein the pre-trained deep learning model is used to characterize a corresponding relationship between an image block and an output value;
• and
• determining, based on the first output value and a threshold, whether the first image block is a fundus image block containing a predetermined type of image region, wherein the threshold is determined by the following steps:
• acquiring a fundus image containing the predetermined type of image region;
• dividing the fundus image containing the predetermined type of image region into at least two second image blocks;
• inputting the at least two second image blocks into the pre-trained deep learning model respectively to obtain at least two third output values;
• and selecting a maximum value from the at least two third output values as the threshold.

A non-transitory computer-readable medium, storing a computer program thereon, wherein the computer program, when executed by a processor, causes the processor to implement operations, the operations comprise:

• acquiring a target fundus image;
• dividing the target fundus image into at least two first image blocks;
• inputting a first image block into a pre-trained deep learning model, to obtain a first output value, wherein the pre-trained deep learning model is used to characterize a corresponding relationship between an image block and an output value;
• and
• determining, based on the first output value and a threshold, whether the first image block is a fundus image block containing a predetermined type of image region, wherein the threshold is determined by the following steps:
• acquiring a fundus image containing the predetermined type of image region;
• dividing the fundus image containing the predetermined type of image region into at least two second image blocks;
• inputting the at least two second image blocks into the pre-trained deep learning model respectively to obtain at least two third output values;
• and selecting a maximum value from the at least two third output values as the threshold.