| abstract |
The invention relates to the technical field of segmentation of pelvis and pelvic arterial blood vessel tree, in particular to a multi-level segmentation method of pelvis and its arterial vessels based on deep learning, which is used to solve the problem that in the prior art, multi-resolution CT cannot be performed. Automatic, efficient and accurate segmentation of abdominal pelvis and pelvic arterial vascular tree in images. The present invention includes step 1: data preparation and labeling; step 2: data preprocessing; step 3: construction of a first-level segmentation model based on a multi-level segmentation 3D convolutional neural network; step 4: construction of a second-level segmentation model; 5: Use the calibrated data and the synthesized loss function to train the first-level segmentation model and the second-level segmentation model; Step 6: Use the first-level segmentation model and the second-level segmentation model trained in step 5 to input 3D CT images Perform abdominal information segmentation. In the present invention, the abdominal pelvis and the pelvic vascular tree can be automatically, efficiently and accurately segmented in the multi-resolution CT image. |