This paper presents a method for generating branching pattern reports of abdominal blood vessels for laparoscopic gastrectomy. In gastrectomy, it is very important to understand branching structure of abdominal arteries and veins, which feed and drain specific abdominal organs including the stomach, the liver and the pancreas. In the real clinical stage, a surgeon creates a diagnostic report of the patient anatomy. This report summarizes the branching patterns of the blood vessels related to the stomach. The surgeon decides actual operative procedure. This paper shows an automated method to generate a branching pattern report for abdominal blood vessels based on automated anatomical labeling. The report contains 3D rendering showing important blood vessels and descriptions of branching patterns of each vessel. We have applied this method for fifty cases of 3D abdominal CT scans and confirmed the proposed method can automatically generate branching pattern reports of abdominal arteries.
In abdominal surgery, understanding blood vessel structure is important because abdominal blood vessels have
large individual differences among patients. Computers must be used to support surgeons and their understanding
of blood vessel structures. This paper presents a method of automated anatomical labeling of abdominal veins.
A thinning process is applied to the abdominal vein regions extracted from a CT volume. The result of the
process is expressed as a tree structure. Since portal veins have a characteristic shape and position in the portal
system, we applied rule-based anatomical labeling to them. The names of other veins are assigned by classifiers
trained by a machine learning technique, where several likelihood functions are constructed for each vessel name.
Their weighted sum is used as the likelihood of the vessel name. The names of the branches in the tree structure
are labeled by searching for the branch whose likelihood of an anatomical name is maximum and assigning the
anatomical name to the branch. In an experiment using 50 cases of abdominal CT volumes, the recall rate, the
precision rate, and the F-measure were 87.5, 93.1, and 90.2%, respectively.