A CRF approach to fitting a generalized hand skeleton model

Abstract

We present a new point distribution model capable of modeling joint subluxation (shifting) in rheumatoid arthritis (RA) patients and an approach to fitting this model to posteroanterior view hand radiographs. We formulate this shape fitting problem as inference in a conditional random field. This model combines potential functions that focus on specific anatomical structures and a learned shape prior. We evaluate our approach on two datasets: one containing relatively healthy hands and one containing hands of rheumatoid arthritis patients. We provide an empirical analysis of the relative value of different potential functions. We also show how to use the fitted hand skeleton to initialize a process for automatically estimating bone contours, which is a challenging, but important, problem in RA disease progression assessment.