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Explicit topological priors for deep-learning based image segmentation using persistent homology
We present a novel method to explicitly incorporate topological prior kn...
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Segmentation of the cortical plate in fetal brain MRI with a topological loss
The fetal cortical plate undergoes drastic morphological changes through...
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Detection and Visualization of Endoleaks in CT Data for Monitoring of Thoracic and Abdominal Aortic Aneurysm Stents
In this paper we present an efficient algorithm for the segmentation of ...
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Topology-Preserving Deep Image Segmentation
Segmentation algorithms are prone to make topological errors on fine-sca...
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Multi-appearance Segmentation and Extended 0-1 Program for Dense Small Object Tracking
Aiming to address the fast multi-object tracking for dense small object ...
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PatchPerPix for Instance Segmentation
In this paper we present a novel method for proposal free instance segme...
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Highly Efficient Follicular Segmentation in Thyroid Cytopathological Whole Slide Image
In this paper, we propose a novel method for highly efficient follicular...
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Deep Small Bowel Segmentation with Cylindrical Topological Constraints
We present a novel method for small bowel segmentation where a cylindrical topological constraint based on persistent homology is applied. To address the touching issue which could break the applied constraint, we propose to augment a network with an additional branch to predict an inner cylinder of the small bowel. Since the inner cylinder is free of the touching issue, a cylindrical shape constraint applied on this augmented branch guides the network to generate a topologically correct segmentation. For strict evaluation, we achieved an abdominal computed tomography dataset with dense segmentation ground-truths. The proposed method showed clear improvements in terms of four different metrics compared to the baseline method, and also showed the statistical significance from a paired t-test.
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