Computer-aided diagnosis of digital chest X-ray (CXR) images critically depends on the automated segmentation of the lungs, which is a challenging problem due to the presence of strong edges at the rib cage and clavicle, the lack of a consistent lung shape among different individuals, and the appearance of the lung apex. From recently published results in this area, hybrid methodologies based on a combination of different techniques (e.g., pixel classification and deformable models) are producing the most accurate lung segmentation results. In this paper, we propose a new methodology for lung segmentation in CXR using a hybrid method based on a combination of distance regularized level set and deep structured inference. This combination brings together the advantages of deep learning methods (robust training with few annotated samples and top-down segmentation with structured inference and learning) and level set methods (use of shape and appearance priors and efficient optimization techniques). Using the publicly available Japanese Society of Radiological Technology (JSRT) dataset, we show that our approach produces the most accurate lung segmentation results in the field. In particular, depending on the initialization used, our methodology produces an average accuracy on JSTR that varies from 94.8% to 98.5%.

We propose a new fully automated non-rigid segmentation approach based on the distance regularized level set method that is initialized and constrained by the results of a structured inference using deep belief networks. This recently proposed level-set formulation achieves reasonably accurate results in several segmentation problems, and has the advantage of eliminating periodic re-initializations during the optimization process, and as a result it avoids numerical errors. Nevertheless, when applied to challenging problems, such as the left ventricle segmentation from short axis cine magnetic ressonance (MR) images, the accuracy obtained by this distance regularized level set is lower than the state of the art. The main reasons behind this lower accuracy are the dependence on good initial guess for the level set optimization and on reliable appearance models. We address these two issues with an innovative structured inference using deep belief networks that produces reliable initial guess and appearance model. The effectiveness of our method is demonstrated on the MICCAI 2009 left ventricle segmentation challenge, where we show that our approach achieves one of the most competitive results (in terms of segmentation accuracy) in the field.

This paper introduces a new semi-automated methodology combining a level set method with a top-down segmentation produced by a deep belief network for the problem of left ventricle segmentation from cardiac magnetic resonance images (MRI). Our approach combines the level set advantages that uses several a priori facts about the object to be segmented (e.g., smooth contour, strong edges, etc.) with the knowledge automatically learned from a manually annotated database (e.g., shape and appearance of the object to be segmented). The use of deep belief networks is justified because of its ability to learn robust models with few annotated images and its flexibility that allowed us to adapt it to a top-down segmentation problem. We demonstrate that our method produces competitive results using the database of the MICCAI grand challenge on left ventricle segmentation from cardiac MRI images, where our methodology produces results on par with the best in the field in each one of the measures used in that challenge (perpendicular distance, Dice metric, and percentage of good detections). Therefore, we conclude that our proposed methodology is one of the most competitive approaches in the field.