title: Bone tumor segmentation on bone scans using context information and random forests.
abstract: Bone tumor segmentation on bone scans has recently been adopted as a basis for objective tumor assessment in several phase II and III clinical drug trials. Interpretation can be difficult due to the highly sensitive but non-specific nature of bone tumor appearance on bone scans. In this paper we present a machine learning approach to segmenting tumors on bone scans, using intensity and context features aimed at addressing areas prone to false positives. We computed the context features using landmark points, identified by a modified active shape model. We trained a random forest classifier on 100 and evaluated on 73 prostate cancer subjects from a multi-center clinical trial. A reference segmentation was provided by a board certified radiologist. We evaluated our learning based method using the Jaccard index and compared against the state of the art, rule based method. Results showed an improvement from 0.50 +/- 0.31 to 0.57 +/- 0.27. We found that the context features played a significant role in the random forest classifier, helping to correctly classify regions prone to false positives.
authors.forename: Gregory
authors.initials: G
authors.lastname: Chu
authors.forename: Pechin
authors.initials: P
authors.lastname: Lo
authors.forename: Bharath
authors.initials: B
authors.lastname: Ramakrishna
authors.forename: Hyun
authors.initials: H
authors.lastname: Kim
authors.forename: Darren
authors.initials: D
authors.lastname: Morris
authors.forename: Jonathan
authors.initials: J
authors.lastname: Goldin
authors.forename: Matthew
authors.initials: M
authors.lastname: Brown
mesh_terms: D000465:Algorithms; D059925:Anatomic Landmarks / Q000000981:diagnostic imaging*; D001185:Artificial Intelligence; D001859:Bone Neoplasms / Q000000981:diagnostic imaging* / Q000556:secondary*; D003627:Data Interpretation, Statistical; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D015588:Observer Variation; D010363:Pattern Recognition, Automated / Q000379:methods*; D011471:Prostatic Neoplasms / Q000000981:diagnostic imaging*; D011877:Radionuclide Imaging; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D051598:Whole Body Imaging / Q000379:methods*