Semantic Context Forests for Learning-Based Knee Cartilage Segmentation in 3D MR Images

The automatic segmentation of human knee cartilage from 3D MR images is a useful yet challenging task due to the thin sheet structure of the cartilage with diffuse boundaries and inhomogeneous intensities. In this paper, we present an iterative multi-class learning method to segment the femoral, tibial and patellar cartilage simultaneously, which effectively exploits the spatial contextual constraints between bone and cartilage, and also between different cartilages. First, based on the fact that the cartilage grows in only certain area of the corresponding bone surface, we extract the distance features of not only to the surface of the bone, but more informatively, to the densely registered anatomical landmarks on the bone surface. Second, we introduce a set of iterative discriminative classifiers that at each iteration, probability comparison features are constructed from the class confidence maps derived by previously learned classifiers. These features automatically embed the semantic context information between different cartilages of interest. Validated on a total of 176 volumes from the Osteoarthritis Initiative (OAI) dataset, the proposed approach demonstrates high robustness and accuracy of segmentation in comparison with existing state-of-the-art MR cartilage segmentation methods.Comment: MICCAI 2013: Workshop on Medical Computer Visio

Let's Make Block Coordinate Descent Go Fast: Faster Greedy Rules, Message-Passing, Active-Set Complexity, and Superlinear Convergence

Block coordinate descent (BCD) methods are widely-used for large-scale numerical optimization because of their cheap iteration costs, low memory requirements, amenability to parallelization, and ability to exploit problem structure. Three main algorithmic choices influence the performance of BCD methods: the block partitioning strategy, the block selection rule, and the block update rule. In this paper we explore all three of these building blocks and propose variations for each that can lead to significantly faster BCD methods. We (i) propose new greedy block-selection strategies that guarantee more progress per iteration than the Gauss-Southwell rule; (ii) explore practical issues like how to implement the new rules when using "variable" blocks; (iii) explore the use of message-passing to compute matrix or Newton updates efficiently on huge blocks for problems with a sparse dependency between variables; and (iv) consider optimal active manifold identification, which leads to bounds on the "active set complexity" of BCD methods and leads to superlinear convergence for certain problems with sparse solutions (and in some cases finite termination at an optimal solution). We support all of our findings with numerical results for the classic machine learning problems of least squares, logistic regression, multi-class logistic regression, label propagation, and L1-regularization

Deep Learning-Based Part Labeling of Tree Components in Point Cloud Data

Point cloud data analysis plays a crucial role in forest management, remote sensing, and wildfire monitoring and mitigation, necessitating robust computer algorithms and pipelines for segmentation and labeling of tree components. This thesis presents a novel pipeline that employs deep learning models, such as the Point-Voxel Transformer (PVT), and synthetic tree point clouds for automatic tree part-segmentation. The pipeline leverages the expertise of environmental artists to enhance the quality and diversity of training data and investigates alternative subsampling methods to optimize model performance. Furthermore, we evaluate various label propagation techniques to improve the labeling of synthetic tree point clouds. By comparing different community detection methods and graph connectivity inference techniques, we demonstrate that K-NN connectivity inference and carefully selected community detection methods significantly enhance labeling accuracy, efficiency, and coverage. The proposed methods hold the potential to improve the quality of forest management and monitoring applications, enable better assessment of wildfire hazards, and facilitate advancements in remote sensing and forestry fields

Deep Learning-Based Part Labeling of Tree Components in Point Cloud Data

Point cloud data analysis plays a crucial role in forest management, remote sensing, and wildfire monitoring and mitigation, necessitating robust computer algorithms and pipelines for segmentation and labeling of tree components. This thesis presents a novel pipeline that employs deep learning models, such as the Point-Voxel Transformer (PVT), and synthetic tree point clouds for automatic tree part-segmentation. The pipeline leverages the expertise of environmental artists to enhance the quality and diversity of training data and investigates alternative subsampling methods to optimize model performance. Furthermore, we evaluate various label propagation techniques to improve the labeling of synthetic tree point clouds. By comparing different community detection methods and graph connectivity inference techniques, we demonstrate that K-NN connectivity inference and carefully selected community detection methods significantly enhance labeling accuracy, efficiency, and coverage. The proposed methods hold the potential to improve the quality of forest management and monitoring applications, enable better assessment of wildfire hazards, and facilitate advancements in remote sensing and forestry fields