title: Noninvasive differential diagnosis of dental periapical lesions in cone-beam CT scans.
abstract: OBJECTIVE
  This paper proposes a novel application of computer-aided diagnosis (CAD) to an everyday clinical dental challenge: the noninvasive differential diagnosis of periapical lesions between periapical cysts and granulomas. A histological biopsy is the most reliable method currently available for this differential diagnosis; however, this invasive procedure prevents the lesions from healing noninvasively despite a report that they may heal without surgical treatment. A CAD using cone-beam computed tomography (CBCT) offers an alternative noninvasive diagnostic tool which helps to avoid potentially unnecessary surgery and to investigate the unknown healing process and rate for the lesions.
  
  
  METHODS
  The proposed semiautomatic solution combines graph-based random walks segmentation with machine learning-based boosted classifiers and offers a robust clinical tool with minimal user interaction. As part of this CAD framework, the authors provide two novel technical contributions: (1) probabilistic extension of the random walks segmentation with likelihood ratio test and (2) LDA-AdaBoost: a new integration of weighted linear discriminant analysis to AdaBoost.
  
  
  RESULTS
  A dataset of 28 CBCT scans is used to validate the approach and compare it with other popular segmentation and classification methods. The results show the effectiveness of the proposed method with 94.1% correct classification rate and an improvement of the performance by comparison with the Simon's state-of-the-art method by 17.6%. The authors also compare classification performances with two independent ground-truth sets from the histopathology and CBCT diagnoses provided by endodontic experts.
  
  
  CONCLUSIONS
  Experimental results of the authors show that the proposed CAD system behaves in clearer agreement with the CBCT ground-truth than with histopathology, supporting the Simon's conjecture that CBCT diagnosis can be as accurate as histopathology for differentiating the periapical lesions.
authors.affiliation: Department of Computer Science, San Francisco State University, San Francisco, California 94132.
authors.forename: Kazunori
authors.initials: K
authors.lastname: Okada
authors.affiliation: Biomedical and Health Informatics Program, University of Washington, Seattle, Washington 98195.
authors.forename: Steven
authors.initials: S
authors.lastname: Rysavy
authors.affiliation: Computer Science and Engineering, University of California, San Diego, California 92093.
authors.forename: Arturo
authors.initials: A
authors.lastname: Flores
authors.affiliation: Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Medical Center, Washington, DC 20010 and Departments of Radiology and Pediatrics, George Washington University, Washington, DC 20037.
authors.forename: Marius George
authors.initials: MG
authors.lastname: Linguraru
mesh_terms: D054893:Cone-Beam Computed Tomography / Q000379:methods*; D066264:Datasets as Topic; D003937:Diagnosis, Differential; D016002:Discriminant Analysis; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D016013:Likelihood Functions; D016014:Linear Models; D000069550:Machine Learning; D010484:Periapical Granuloma / Q000175:diagnosis* / Q000000981:diagnostic imaging* / Q000473:pathology; D011842:Radicular Cyst / Q000175:diagnosis* / Q000000981:diagnostic imaging* / Q000473:pathology