The invention relates to the technical field of medical
image processing, and discloses a
human body abdominal fat analysis method based on a medical image, which comprises the following steps: acquiring multi-
modal medical image data such as CT (
Computed Tomography), MRI (
Magnetic Resonance Imaging) and ultrasonic images, constructing a fusion
database through multi-scale affine transformation alignment, de-noising by using a model based on a residual self-
encoder, and enhancing a boundary in combination with Canny
edge detection. A segmentation model is constructed based on an improved U-Net architecture, multi-
modal features are fused, and a dynamic
convolution kernel and a channel attention mechanism are used for optimization. A quantitative analysis model is established by adopting a multi-task joint learning framework, gradient conflicts are solved, and a lightweight sub-network is searched and generated. Carrying out uncertainty modeling on the segmentation model, and carrying out active learning
annotation to optimize the performance. A multi-
granularity feature fusion framework is designed, anatomical priori knowledge is embedded to construct an association graph, a
structured analysis report is generated,
abdominal fat can be accurately analyzed, and diagnosis and treatment of
obesity-related diseases are assisted.