Discriminative learning of deformable contour models

Abstract

In this work we propose a machine learning approach to improve shape detection accuracy in medical images with deformable contour models (DCMs). Our DCMs can efficiently recover globally optimal solutions that take into account constraints on shape and appearance in the model fitting criterion; our model can also deal with global scale variations by operating in a multi-scale pyramid. Our main contribution consists in formulating the task of learning the DCM score function as a large-margin structured prediction problem. Our algorithm trains DCMs in an joint manner - all the parameters are learned simultaneously, while we use rich local features for landmark localization. We evaluate our method on lung field, heart, and clavicle segmentation tasks using 247 standard posterior-anterior (PA) chest radiographs from the Segmentation in Chest Radiographs (SCR) benchmark. Our learned DCMs systematically outperform the state of the art methods according to a host of validation measures including the overlap coefficient, mean contour distance and pixel error rate.