基于nnU-Net的肾脏区域分割方法及其术中应用OA

Objective Explore the application of deep learning methods based on nnU-Net in kidney surgery,particularly the feasibility of intraoperative image segmentation.As the complexity of kidney tumor surgery increases,precise intraoperative image segmentation becomes crucial for surgical navigation.The research goal is to improve the automatic recognition and localization accuracy of the renal region during surgery using the nnU-Net framework,thereby providing technical support for intelligent assisted surgery systems.Methods Based on the left-eye endoscopic images obtained during surgery,a 2D semantic segmentation model was constructed using the nnU-Net framework.The training data were generated by extracting individual frames from the intraoperative images and combining them with manually annotated masks.The annotated regions include renal parenchyma,tumors,and surface-adhered fat,which were identified as a unified region of Interest(ROI).For data validation,an independent test set was used,consisting of five kidney surgery cases that did not participate in the training and validation.The model's performance in intraoperative image segmentation was evaluated by comparing it with manually annotated masks.Results The model performed well on the independent test set,with an average Dice similarity coefficient(DSC)of 0.9335,intersection over union(IoU)of 0.878 4,precision of 0.951 7,recall of 0.921 1,and F1 score of 0.933 5.These results indicate that nnU-Net can effectively segment the renal region and maintain high segmentation accuracy under various conditions.In terms of inference speed,the average single-frame segmentation time of the model is approximately 0.38 seconds,demonstrating strong processing capability,making it suitable for rapid intraoperative image updates and localization tasks.Conclusions The nnU-Net model maintains high accuracy and robustness even in complex intraoperative image environments,validating its potential for real-time image segmentation tasks.nnU-Net can provide reliable technical support for intelligent assisted surgery systems,showing promising clinical application prospects.