Tracing of thin tubular structures in computer tomographic data.
For many applications in diagnostics and in the planning of surgical interventions, specific structures have to be identified in a patient's volume data set. In this article we give an outline of how the detection of thin tubular structures (e.g., nerves and vessels) can be automated, requiring very little initialization from a human expert. We focused on the nervus alveolaris inferior in the lower jaw and were looking at three details: data acquisition, detection, and validation of accuracy. Our method can be easily adapted to many similar cases such as other nerves, arteries, and veins or bundles thereof.
Stein
W
W
Department of Oral and Maxillofacial Surgery, University of Heidelberg, Germany. v97@ix.urz.uni-heidelberg.de
Hassfeld
S
S
Muhling
J
J
D000465:Algorithms; D000792:Angiography / Q000379:methods*; D001331:Automation; D006801:Humans; D008334:Mandible / Q000000981:diagnostic imaging / Q000294:innervation; D008340:Mandibular Nerve / Q000000981:diagnostic imaging; D010347:Patient Care Planning; D010525:Peripheral Nerves / Q000000981:diagnostic imaging*; D010690:Phlebography; D011856:Radiographic Image Enhancement / Q000379:methods*; D015203:Reproducibility of Results; D014057:Tomography, X-Ray Computed / Q000379:methods*