Disclosed are an image processing method, an image processing apparatus, and a readable storage medium. First, an image to be processed is received, and the received image to be processed is divided into regions of interest by region segmentation means. Next, the regions of interest are detected by calling a pre-stored full convolution network structure model, to obtain probability image segments. Finally, the probability image segments are synthesized to generate a target probability image. Wherein, the pre-stored full convolution network structure model includes a full convolution structure. A linear regression layer is replaced by an equivalent convolution layer in the full convolution structure. A blank padding operation layer and an up-sampling layer are removed from the full convolution structure.