title: A new osteophyte segmentation algorithm using partial shape model and its applications to rabbit femur anterior cruciate ligament transection via micro-CT imaging.
abstract: Osteophyte is an additional bony growth on a normal bone surface limiting or stopping motion at a deteriorating joint. Detection and quantification of osteophytes from CT images is helpful in assessing disease status as well as treatment and surgery planning. However, it is difficult to distinguish between osteophytes and healthy bones using simple thresholding or edge/texture features due to the similarity of their material composition. In this paper, we present a new method primarily based active shape model (ASM) to solve this problem and evaluate its application to anterior cruciate ligament transection (ACLT) rabbit femur model via CT imaging. The common idea behind most ASM based segmentation methods is to first build a parametric shape model from a training dataset and apply the model to find a shape instance in a target image. A common challenge with such approaches is that a diseased bone shape is significantly altered at regions with osteophyte deposition misguiding an ASM method and eventually leading to suboptimum segmentations. This difficulty is overcome using a new partial ASM method that uses bone shape over healthy regions and extrapolates it over the diseased region according to the underlying shape model. Finally, osteophytes are segmented by subtracting partial-ASM derived shape from the overall diseased shape. Also, a new semi-automatic method is presented in this paper for efficiently building a 3D shape model for an anatomic region using manual reference of a few anatomically defined fiducial landmarks that are highly reproducible on individuals. Accuracy of the method has been examined on simulated phantoms while reproducibility and sensitivity have been evaluated on CT images of 2-, 4- and 8-week post-ACLT and sham-treated rabbit femurs. Experimental results have shown that the method is highly accurate ( R2 = 0.99), reproducible (ICC = 0.97), and sensitive in detecting disease progression (p-values: 0.065,0.001 and < 0.001 for 2- vs. 4, 4- vs. 8- and 2- vs. 8-weeks, respectively).
authors.forename: P K
authors.initials: PK
authors.lastname: Saha
authors.forename: G
authors.initials: G
authors.lastname: Liang
authors.forename: J M
authors.initials: JM
authors.lastname: Elkins
authors.forename: A
authors.initials: A
authors.lastname: Coimbra
authors.forename: L T
authors.initials: LT
authors.lastname: Duong
authors.forename: D S
authors.initials: DS
authors.lastname: Williams
authors.forename: M
authors.initials: M
authors.lastname: Sonka
mesh_terms: D000465:Algorithms; D000818:Animals; D016118:Anterior Cruciate Ligament / Q000000981:diagnostic imaging* / Q000473:pathology; D000070598:Anterior Cruciate Ligament Injuries; D003198:Computer Simulation; D008953:Models, Anatomic; D008954:Models, Biological; D054850:Osteophyte / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011817:Rabbits; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*