Objective To minimize variations in treatment outcomes of L5/S1 percutaneous intervertebral radiofrequency thermocoagulation(PIRFT)arising from physician proficiency and achieve precise quantitative risk assessment of the puncture paths.Methods We used a self-developed deep neural network DWT-UNet for automatic segmentation of the magnetic resonance(MR)images of the L5/S1 segments into 7 key structures:L5,S1,Ilium,Disc,N5,Dura mater,and Skin,based on which a needle insertion path planning environment was modeled.Six hard constraints and 6 soft constraints were proposed based on clinical criteria for needle insertion,and the physician's experience was quantified into weights using the analytic hierarchy process and incorporated into the risk function for needle insertion paths to enhance individual case adaptability.By leveraging the proposed skin entry point sampling sub-algorithm and Kambin's triangle projection area sub-algorithm in conjunction with the analytic hierarchy process,and employing various technologies such as ray tracing,CPU multi-threading,and GPU parallel computing,a puncture path was calculated that not only met clinical hard constraints but also optimized the overall soft constraints.Results A surgical team conducted a subjective evaluation of the 21 needle puncture paths planned by the algorithm,and all the paths met the clinical requirements,with 95.24%of them rated excellent or good.Compared with the physician's planning results,the plans generated by the algorithm showed inferior DIlium,DS1,and Depth(P<0.05)but much better DDura,DL5,DN5,and AKambin(P<0.05).In the 21 cases,the planning time of the algorithm averaged 7.97±3.73 s,much shorter than that by the physicians(typically beyond 10 min).Conclusion The multi-constraint optimal puncture path planning algorithm offers an efficient automated solution for PIRFT of the L5/S1 segments with great potentials for clinical application.

Objective To minimize variations in treatment outcomes of L5/S1 percutaneous intervertebral radiofrequency thermocoagulation(PIRFT)arising from physician proficiency and achieve precise quantitative risk assessment of the puncture paths.Methods We used a self-developed deep neural network DWT-UNet for automatic segmentation of the magnetic resonance(MR)images of the L5/S1 segments into 7 key structures:L5,S1,Ilium,Disc,N5,Dura mater,and Skin,based on which a needle insertion path planning environment was modeled.Six hard constraints and 6 soft constraints were proposed based on clinical criteria for needle insertion,and the physician's experience was quantified into weights using the analytic hierarchy process and incorporated into the risk function for needle insertion paths to enhance individual case adaptability.By leveraging the proposed skin entry point sampling sub-algorithm and Kambin's triangle projection area sub-algorithm in conjunction with the analytic hierarchy process,and employing various technologies such as ray tracing,CPU multi-threading,and GPU parallel computing,a puncture path was calculated that not only met clinical hard constraints but also optimized the overall soft constraints.Results A surgical team conducted a subjective evaluation of the 21 needle puncture paths planned by the algorithm,and all the paths met the clinical requirements,with 95.24%of them rated excellent or good.Compared with the physician's planning results,the plans generated by the algorithm showed inferior DIlium,DS1,and Depth(P<0.05)but much better DDura,DL5,DN5,and AKambin(P<0.05).In the 21 cases,the planning time of the algorithm averaged 7.97±3.73 s,much shorter than that by the physicians(typically beyond 10 min).Conclusion The multi-constraint optimal puncture path planning algorithm offers an efficient automated solution for PIRFT of the L5/S1 segments with great potentials for clinical application.

To meet the requirement of pathological practice and development, we systematically analyzed the situation of pathological residents training and the importance of initiating the clinical scientific research training. Additionally, we proposed the principle and implementation strategy for clinical scientific research training. According to features of pathological practice, we employed the modularized teaching to divide the training courses into several modules: discussion module for clinical pathology, lecture module for advanced research, and training modules for basic scientific theory, technology and writing skill. With these approaches, the systematic and structured system of standardized residents training is implemented to improve the clinical research ability of pathological residents.