The Construction and Effect Verification of a Deep Learning-based Automated Semantic Segmentation Model for Medical Imaging

The construction of a deep learning model for semantic segmentation of images during laparoscopic hepatobiliary and pancreatic surgery, focusing on classifying surgical stages, anatomical structures, surgical instruments, and surgical gestures, along with the validation of model performance (Intersection over Union, Dice score, accuracy, precision, recall, and F1 score).