Data-Dependent Higher-Order Clique Selection for Artery-Vein Segmentation by Energy Minimization
We propose a novel segmentation method based on energy minimization of higher-order potentials. We introduce higher-order terms into the energy to incorporate prior knowledge on the shape of the segments. The terms encourage certain sets of pixels to be entirely in one segment or the other. The sets can for instance be smooth curves in order to help delineate pulmonary vessels, which are known to run in almost straight lines. The higher-order terms can be converted to submodular first-order terms by adding auxiliary variables, which can then be globally minimized using graph cuts. We also determine the weight of these terms, or the degree of the aforementioned encouragement, in a principled way by learning from training data with the ground truth. We demonstrate the effectiveness of the method in a real-world application in fully-automatic pulmonary artery-vein segmentation in CT images.
Code (0)
등록된 구현이 없습니다.
Tasks
SegmentationSimilar Papers 제목 키워드 기반
Node Classification via Simplicial Interaction with Augmented Maximal Clique Selection
Considering higher-order interactions allows for a more comprehensive understanding of network structures beyond simple pairwise connections. While leveraging all cliques in a network to handle higher-order interactions …
Node ClassificationScaling Higher-Order Graph Learning with Maximal Clique Complexes
Graph neural networks (GNNs) are limited to modeling pairwise interactions, while higher-order models based on cell complexes achieve greater expressivity but often suffer from poor scalability. We introduce simplified a…
Computational EfficiencyGraph LearningComputing Cliques and Cavities in Networks
Complex networks contain complete subgraphs such as nodes, edges, triangles, etc., referred to as simplices and cliques of different orders. Notably, cavities consisting of higher-order cliques play an important role in …
Min Norm Point Algorithm for Higher Order MRF-MAP Inference
Many tasks in computer vision and machine learning can be modelled as the inference problems in an MRF-MAP formulation and can be reduced to minimizing a submodular function. Using higher order clique potentials to mode…
Nonlinearly Constrained MRFs: Exploring the Intrinsic Dimensions of Higher-Order Cliques
This paper introduces an efficient approach to integrating non-local statistics into the higher-order Markov Random Fields (MRFs) framework. Motivated by the observation that many non-local statistics (e.g., shape priors…
Image SegmentationSemantic Segmentation