title: A knowledge-based approach to automatic detection of the spinal cord in CT images.
abstract: Accurate planning of radiation therapy entails the definition of treatment volumes and a clear delimitation of normal tissue of which unnecessary exposure should be prevented. The spinal cord is a radiosensitive organ, which should be precisely identified because an overexposure to radiation may lead to undesired complications for the patient such as neuronal disfunction or paralysis. In this paper, a knowledge-based approach to identifying the spinal cord in computed tomography images of the thorax is presented. The approach relies on a knowledge-base which consists of a so-called anatomical structures map (ASM) and a task-oriented architecture called the plan solver. The ASM contains a frame-like knowledge representation of the macro-anatomy in the human thorax. The plan solver is responsible for determining the position, orientation and size of the structures of interest to radiation therapy. The plan solver relies on a number of image processing operators. Some are so-called atomic (e.g., thresholding and snakes) whereas others are composite. The whole system has been implemented on a standard PC. Experiments performed on the image material from 23 patients show that the approach results in a reliable recognition of the spinal cord (92% accuracy) and the spinal canal (85% accuracy). The lamina is more problematic to locate correctly (accuracy 72%). The position of the outer thorax is always determined correctly.
authors.affiliation: Computer Science Department, University of Neuchâtel, Emile-Argand 11, CH 2007, Neuchâtel, Switzerland. neculai.archip@ece.ubc.ca
authors.forename: Neculai
authors.initials: N
authors.lastname: Archip
authors.forename: Pierre-Jean
authors.initials: PJ
authors.lastname: Erard
authors.forename: Michael
authors.initials: M
authors.lastname: Egmont-Petersen
authors.forename: Jean-Marie
authors.initials: JM
authors.lastname: Haefliger
authors.forename: Jean-Francois
authors.initials: JF
authors.lastname: Germond
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D001185:Artificial Intelligence; D016208:Databases, Factual; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D013902:Radiography, Thoracic / Q000379:methods; D011874:Radiometry / Q000379:methods; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013116:Spinal Cord / Q000033:anatomy & histology / Q000000981:diagnostic imaging*; D013899:Thoracic Neoplasms / Q000000981:diagnostic imaging / Q000532:radiotherapy; D014057:Tomography, X-Ray Computed / Q000379:methods*