Xu Yinan, Ban Yutong, Zhao Yue, Krauss Dolores, Bruns Christiane, Eckhoff Jennifer, Fuchs Hans
Department of General, Visceral and Cancer Surgery, University Hospital of Cologne, Cologne, Germany.
UM-SJTU JI, Shanghai Jiao Tong University, Shanghai, China.
Comput Struct Biotechnol J. 2025 Aug 5;28:294-305. doi: 10.1016/j.csbj.2025.07.056. eCollection 2025.
Laparoscopic Cholecystectomy (LC) is one of the most performed complex surgeries. Integrating Artificial Intelligence (AI) into LC shows great potential for assisting in anatomical structure detection. To be dependable, AI must be accurate, robust, and effective. In this study, a relation-based model was proposed to enhance surgical object detection in LC images. The model employs a positional relation encoder and refines progressive attention mechanism to analyze object relationships. Two widely used LC datasets were selected to validate the proposed model. We strictly followed the official split and evaluator protocols for fair comparison with benchmark models. The Macroscopic Correlation (MC) results revealed distinct differences in position relation strength between the two datasets, enabling comprehensive evaluation of the proposed models under different circumstances. The experimental results demonstrated the accuracy and effectiveness of the proposed models in both datasets. The proposed model outperformed the best-performing benchmark model by an improvement of 33.95 % in overall mean Average Precision (AP) on the Endoscapes dataset. For classes Cystic Plate and HC Triangle, the detection AP was improved by 90.32 % and 92.46 %, respectively. For the m2cai16-tool-locations dataset, the proposed models also demonstrated effective performance, improving the overall mAP by up to 17.97 % compared to benchmark models. The experimental results proved the accuracy and effectiveness of the proposed model. Due to the analysis of position relation, the detection of key objects is significantly improved. The postprocessing steps effectively reduce redundant bounding boxes by over 90 %. Future work could focus on expanding to more clinical and practical applications.
腹腔镜胆囊切除术（LC）是最常开展的复杂手术之一。将人工智能（AI）集成到LC中显示出在辅助解剖结构检测方面的巨大潜力。为了可靠，AI必须准确、稳健且有效。在本研究中，提出了一种基于关系的模型来增强LC图像中的手术目标检测。该模型采用位置关系编码器并改进渐进注意力机制来分析目标关系。选择了两个广泛使用的LC数据集来验证所提出的模型。我们严格遵循官方的划分和评估协议，以便与基准模型进行公平比较。宏观相关性（MC）结果揭示了两个数据集在位置关系强度上的明显差异，从而能够在不同情况下对所提出的模型进行全面评估。实验结果证明了所提出模型在两个数据集中的准确性和有效性。在Endoscapes数据集上，所提出的模型在总体平均精度均值（AP）方面比表现最佳的基准模型提高了33.95%。对于胆囊板和肝总管三角类，检测AP分别提高了90.32%和92.46%。对于m2cai16工具位置数据集，所提出的模型也表现出有效性能，与基准模型相比，总体平均精度均值（mAP）提高了高达17.97%。实验结果证明了所提出模型的准确性和有效性。由于对位置关系的分析，关键目标的检测得到了显著改善。后处理步骤有效地减少了超过90%的冗余边界框。未来的工作可以集中在扩展到更多临床和实际应用上。
