چكيده به لاتين
The liver is one of the vital organs of the human body. In addition to breaking down toxic substances and bilirubin, it plays an important role in creating blood clots and storing sugar. Therefore, treating liver diseases such as liver cancer, hepatitis, and cirrhosis is very important. Liver cancer is the third leading cause of death, the sixth most common cancer, and one of the deadliest types of cancer worldwide. Liver surgery, often involving the removal of part of the liver, is the most common and, in many cases, the only possible treatment method. The liver has a complex anatomy, especially concerning its arterial and venous vessels, which increases the risk of surgery. Due to the sensitivity and importance of liver tissue, damage to it can have destructive and irreparable effects. The accuracy of liver surgery depends on the precise localization of tumors, vessel structures, and bile ducts. Before surgery, the surgeon examines the patient's tomography images and visualizes a three-dimensional model of the required information. This process is time-consuming, depends on the surgeon's skill, and requires much experience. Therefore, providing the necessary three-dimensional information to the surgeon before the operation is both necessary and challenging. As a result, segmenting the liver and its vessel structures from abdominal CT images is a fundamental step in diagnosing and treating liver diseases, as well as in preparing for liver surgeries. However, accurately segmenting liver vessels from abdominal CT images is challenging due to factors such as high noise, partial volume effects, varying vessel sizes, heterogeneous intensity distribution, and the many branches of the vessel's structure. Manual segmentation of vessels by doctors is often tedious, prone to human error, and very time-consuming, and it relies heavily on expert opinion. Consequently, semi-automatic and automatic segmentation of liver tissue and vessels has gained increasing attention. In particular, the hepatic and the portal vein are crucial due to their extensive presence in the liver, the large volume of blood they transport, and their tree-like structure. These veins play different roles in preoperative planning, making their accurate segmentation especially important.
Our goal in this research is to present a multi-class segmentation method for the segmentation of liver vessels and the visualization of a three-dimensional model of their structure. To achieve the segmentation of liver vessels, we used the IRCAD dataset and local dataset. In this context, we employed the U-Net Transformers (UNETR) network along with a combination of the Dice and Cross-Entropy loss functions. Using the proposed method, we achieved a Dice coefficient of 46.65% for the portal vein, 68.06% for the hepatic vein, and 76.40% for all liver vessels. For liver surgery, the surgeon needs to divide the liver vessels into four levels accurately, and in this study, the liver vessels are divided into four levels separately.
