Vision techniques for anatomical structures in laparoscopic surgery: a comprehensive review.

Area of Science:

• Medical imaging
• Computer vision
• Surgical technology

Background:

• Laparoscopic surgery presents challenges that computer vision can address.
• Computer vision is crucial for classifying, segmenting, and detecting abdominal anatomical structures.
• This field has seen significant research growth over the past decade.

Purpose of the Study:

• To provide a comprehensive review of computer vision applications in laparoscopic surgery over the last ten years.
• To analyze datasets, technical approaches, and evaluation methods for key computer vision tasks.
• To identify research gaps and future development potential.

Main Methods:

• Categorized overview of computer vision subtasks relevant to medical scenarios.
• Statistical analysis of datasets used in experimental validation.
• Detailed exploration of technical approaches and trends in classification, segmentation, and target detection.

Main Results:

• Discussion of advantages, limitations, and practical implications of various technical approaches.
• Analysis of evaluation methods for classification, segmentation, and target detection tasks.
• Identification of current research gaps and areas for future development.

Conclusions:

• Computer vision is a rapidly advancing field with significant potential to enhance laparoscopic surgery.
• Further research is needed to address identified gaps and fully realize the benefits of computer vision in surgical procedures.
• The review highlights the importance of robust datasets and advanced algorithms for real-world medical applications.