This paper is a summary of a comprehensive study of the problem of predicting the accelerations of a set of rigid, three‐dimensional bodies in contact in the presence of Coulomb friction. We begin with a brief introduction of this problem and its governing equations. This is followed by the introduction of complementarity formulations for the contact problem under two friction laws: Coulomb's Law of quadratic friction and an approximated pyramid law. Existence and uniqueness results for the complementary problems are presented. Algorithms for solving these problems are proposed and their convergence properties are discussed. Computational results are presented and conclusions are drawn.
Deep learning has emerged as a powerful artificial intelligence tool to interpret medical images for a growing variety of applications. However, the paucity of medical imaging data with high-quality annotations that is necessary for training such methods ultimately limits their performance. Medical data is challenging to acquire due to privacy issues, shortage of experts available for annotation, limited representation of rare conditions and cost. This problem has previously been addressed by using synthetically generated data. However, networks trained on synthetic data often fail to generalize to real data. Cinematic rendering simulates the propagation and interaction of light passing through tissue models reconstructed from CT data, enabling the generation of photorealistic images. In this paper, we present one of the first applications of cinematic rendering in deep learning, in which we propose to fine-tune synthetic data-driven networks using cinematically rendered CT data for the task of monocular depth estimation in endoscopy. Our experiments demonstrate that: (a) convolutional neural networks (CNNs) trained on synthetic data and fine-tuned on photorealistic cinematically rendered data adapt better to real medical images and demonstrate more robust performance when compared to networks with no fine-tuning, (b) these fine-tuned networks require less training data to converge to an optimal solution, and (c) fine-tuning with data from a variety of photorealistic rendering conditions of the same scene prevents the network from learning patient-specific information and aids in generalizability of the model. Our empirical evaluation demonstrates that networks fine-tuned with cinematically rendered data predict depth with 56.87% less error for rendered endoscopy images and 27.49% less error for real porcine colon endoscopy images.
Abstract. This paper describes a panoramic projection designed to increase the surface visibility during virtual endoscopies. The proposed projection renders five faces of a cubic viewing space into the plane in a continuous fashion. Using this real-time and interactive visualization technique as a screening method for colon cancer could lead to significantly shorter evaluation time. It avoids having to fly through the colon in both directions and prevents the occlusion of potential polyps behind haustral folds.
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