The Construction and Effect Verification of a Deep Learning-based Automated Semantic Segmentation Model for Medical Imaging

Condition(s) studied

Investigational drug(s) / intervention(s)

Whether the patient received diagnosis and treatment at Zhujiang Hospital of Southern Medical University and retained medical images such as abdominal: The construction of a deep learning model for semantic segmentation of images during laparoscopic hepatobiliary and pancreatic surgery, focusing on classifying surgical stages, anatomical structures, surgical instruments, and surgical gestures, along with the validation of model performance (Intersection over Union, Dice score, accuracy, precision, recall, and F1 score).

Study summary

Hepatocellular Carcinoma(HCC) is a common disease in China, ranking as the fourth most prevalent malignant tumor and the third leading cause of cancer-related deaths in the country. Along with other liver, biliary, pancreatic, and splenic diseases, it poses a serious threat to the lives and health of the Chinese population. Precise organ resection techniques, centered around accurate preoperative imaging and functional assessment as well as meticulous surgical operations, have become the mainstream in hepatobiliary surgery in the 21st century. These techniques require precise dissection of intrahepatic blood vessels, the biliary system, and the pancreatic-splenic duct system to achieve an optimal balance between eradicating lesions and preserving the normal function of the organs while minimizing trauma to the body.

Precise tissue resection via laparoscopy is a prerequisite for successful hepatobiliary surgery. Addressing how to assist surgeons in performing surgeries more safely and effectively, as well as how to enhance learning outcomes during training, are pressing issues that need to be resolved. Efficient learning and analysis of surgical videos may help improve surgeons' intraoperative performance.

In recent years, advancements in artificial intelligence (AI) have led to a surge in the application of computer vision (CV) in medical image analysis, including surgical videos. Laparoscopic surgery generates a large amount of surgical video data, providing a new opportunity for the enhancement of laparoscopic surgical CV technology. AI-based CV technology can utilize these surgical video data to develop real-time automated decision support tools and surgical training systems, offering new directions for addressing the shortcomings of laparoscopic surgery.

However, the application of deep learning models in surgical procedures still has some shortcomings. Based on this, the present study aims to conduct a retrospective analysis of cases involving laparoscopic hepatobiliary and pancreatic surgeries performed at Zhujiang Hospital, Southern Medical University, between 2017 and 2024. The goal is to investigate the recognition and validation of deep learning models for classifying surgical phase images in medical imaging, as well as for semantic segmentation of anatomical structures, surgical instruments, and surgical gestures, including abdominal CT and MRI.

Eligibility

Primary outcome measure(s)

• F1 score — Day 1
  In the accuracy of medical image segmentation, TP represents a case predicted as positive, and the true label is positive; FN represents a case predicted as negative, but the true label is positive; FP represents a case predicted as positive, but the true label is negative; TN represents a case predicted as negative, and the true label is also negative. The F1 score is calculated as F1 = 2 × \[Precision × Recall / (Precision + Recall)\], and it takes into account both the precision and recall of the classification model, defined as the harmonic mean of the model's precision and recall.

Trial sites (1)

More Zhujiang Hospital trials in China

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