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. Precisely localizing shapes in medical images is of paramount importance in a host of medical image applications, involving organ segmentation, tracking, registration and atlas building. In our work we consider the problem of localizing a set of landmarks that are strung together along a contour; our task is to recover this landmark sequence by exploiting both the local appearance information around the individual landmarks, as well as their ordering constraints. We focus on learning the merit function being optimized so as to improve the shape localization performance of DCMs. Earlier research has involved enhancing the geometric terms in DCMs, including their formulation in intrinsic geometric terms [7] , the incorporation of better regularization terms [8] and the introduction of shape priors [9] in curve evolution. Current works in medical imaging estimate the model parameters in a two-stage training manner, using e.g. maximum likelihood (ML) estimation for the pairwise terms, and potentially other combinations of boosting/eigenspaces for the landmark appearance models ([10]/[11] respectively). In our experience, the resulting unary and pairwise terms can often be uncommensurate, and hand-tuning the relative contribution of the resulting terms may be needed. The minimal intensity and shape cost path (MISCP) algorithm [12] uses exhaustive leave-one out experiments to calibrate unary and pairwise terms. Our work advances the state of the art in DCM-based medical image analysis primarily along the following lines: First, we learn the merit function of DCMs in an joint manner; we express it as the inner product of a weight vector with appropriately formed appearance and geometric features (Sec. 2) and then estimate the optimal weight vector by casting the training problem as structured prediction (Sec. 4). This comes with the guarantees of optimality for the task at hand, in the sense that we can directly exploit the structure of the space of solutions. Our joint training tackles the estimation of all model parameters in terms of a single objective that directly reflects the performance in the task being solved. In particular, we give as input to our training algorithm, as appearance feature, dense (i.e. computable at every point) image descriptors as opposed to convolution features, which further boost shape detection performance. Second, we use a model that efficiently recovers a globally optimal configuration without requiring initialization (as e.g. ASM/AAM-based models do [3]) or a shortlist of candidate landmark locations (as e.g. [13, 11] do). We use a chain-structured contour representation, hence DP [5] can recover the global optimum efficiently -and for the particular form of our geometric terms, the solution is obtained in a few seconds with Generalized Distance Transforms (GDTs) [14, 15] . Furthermore we accommodate scale changes through a multi-scale detection algorithm that uses an image pyramid.
Exemplified in the approach presented by Boussaid et al. REF , the problem is addressed as a deformable contour model that uses SIFT features to describe the embedding object appearance.
Discriminative learning of deformable contour models
2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)
2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI)
Computer Science