Tubular structures segmentation of pediatric abdominal-visceral ceCT images with renal tumors: Assessment, comparison and improvement.

Renal tubular structures, such as ureters, arteries and veins, are very important for building a complete digital 3D anatomical model of a patient. However, they can be challenging to segment from ceCT images due to their elongated shape, diameter variation and intra- and inter-patient contrast heterogeneity. This task is even more difficult in pediatric and pathological subjects, due to high inter-subject anatomical variations, potential presence of tumors, small volume of these structures compared to the surrounding, and small available labeled datasets. Given the limited literature on methods dedicated to children, and in order to find inspirational approaches, a complete assessment of state-of-the-art methods for the segmentation of renal tubular structures on ceCT images on adults is presented. Then, these methods are tested and compared on a private pediatric and pathological dataset of 79 abdominal-visceral ceCT images with arteriovenous phase acquisitions. To the best of our knowledge, both assessment and comparison in this specific case are novel. Eventually, we also propose a new loss function which leverages for the first time the use of vesselness functions on the predicted segmentation. We show that the combination of this loss function with state-of-the-art methods improves the topological coherence of the segmented tubular structures.2.

Pose-invariant 3D proximal femur estimation through bi-planar image segmentation with hierarchical higher-order graph-based priors.

Low-dose CT-like imaging systems offer numerous perspectives in terms of clinical application, in particular for osteoarticular diseases. In this paper, we address the challenging problem of 3D femur modeling and estimation from bi-planar views. Our contributions are threefold. First, we propose a non-uniform hierarchical decomposition of the shape prior of increasing clinical-relevant precision which is achieved through curvature driven unsupervised clustering acting on the geodesic distances between vertices. Second, we introduce a graphical-model representation of the femur which can be learned from a small number of training examples and involves third-order and fourth-order priors, while being similarity and mirror-symmetry invariant and providing means of measuring regional and boundary supports in the bi-planar views. Last but not least, we adopt an efficient dual-decomposition optimization approach for efficient inference of the 3D femur configuration from bi-planar views. Promising results demonstrate the potential of our method.