Development of deep learning framework for anatomical landmark detection and guided dissection line during laparoscopic cholecystectomy

Pruittikorn Smithmaitrie; Methasit Khaonualsri; Wannipa Sae-Lim; Piyanun Wangkulangkul; Supakool Jearanai; Siripong Cheewatanakornkul

doi:10.1016/j.heliyon.2024.e25210

Development of deep learning framework for anatomical landmark detection and guided dissection line during laparoscopic cholecystectomy

Heliyon. 2024 Jan 29;10(3):e25210. doi: 10.1016/j.heliyon.2024.e25210. eCollection 2024 Feb 15.

Authors

Pruittikorn Smithmaitrie¹, Methasit Khaonualsri¹, Wannipa Sae-Lim², Piyanun Wangkulangkul³, Supakool Jearanai³, Siripong Cheewatanakornkul³

Affiliations

¹ Department of Mechanical and Mechatronics Engineering, Faculty of Engineering, Prince of Songkla University, Thailand.
² Department of Computer Science, Faculty of Science, Prince of Songkla University, Thailand.
³ Minimally Invasive Surgery Unit, Department of Surgery, Faculty of Medicine, Prince of Songkla University, Thailand.

Abstract

Background: Bile duct injuries during laparoscopic cholecystectomy can arise from misinterpretation of biliary anatomy, leading to dissection in improper areas. The integration of a deep learning framework into laparoscopic procedures offers the potential for real-time anatomical landmark recognition, ensuring accurate dissection. The objective of this study is to develop a deep learning framework that can precisely identify anatomical landmarks, including Rouviere's sulcus and the liver base of segment IV, and provide a guided dissection line during laparoscopic cholecystectomy.

Methods: We retrospectively collected 40 laparoscopic cholecystectomy videos and extracted 80 images form each video to establish the dataset. Three surgeons annotated the bounding boxes of anatomical landmarks on a total of 3200 images. The YOLOv7 model was trained to detect Rouviere's sulcus and the liver base of segment IV as anatomical landmarks. Additionally, the guided dissection line was generated between these two landmarks by the proposed algorithm. To evaluate the performance of the detection model, mean average precision (mAP), precision, and recall were calculated. Furthermore, the accuracy of the guided dissection line was evaluated by three surgeons. The performance of the detection model was compared to the scaled-YOLOv4 and YOLOv5 models. Finally, the proposed framework was deployed in the operating room for real-time detection and visualization.

Results: The overall performance of the YOLOv7 model on validation set and testing set were 98.1 % and 91.3 %, respectively. Surgeons accepted the visualization of guide dissection line with a rate of 95.71 %. In the operating room, the well-trained model accurately identified the anatomical landmarks and generated the guided dissection line in real-time.

Conclusions: The proposed framework effectively identifies anatomical landmarks and generates a guided dissection line in real-time during laparoscopic cholecystectomy. This research underscores the potential of using deep learning models as computer-assisted tools in surgery, providing an assistant tool to accommodate with surgeons.

Keywords: Anatomical landmark detection; Bile duct injury; Convolutional neural network; Deep learning; Guided dissection line; Laparoscopic cholecystectomy.