Eeg332
发表于 2025-3-23 09:52:19
Memory Efficient LDDMM for Lung CT,s in memory consumption and runtime are demonstrated for registration of lung CT images. State-of-the-art accuracy is shown for the challenging DIR-Lab chronic obstructive pulmonary disease (COPD) lung CT data sets obtaining a mean landmark distance after registration of 1.03 mm and the best average results so far.
挥舞
发表于 2025-3-23 15:57:41
Inertial Demons: A Momentum-Based Diffeomorphic Registration Framework, a diffeomorphic registration. Our method outperforms all the analysed demons approaches in terms of speed and accuracy. Furthermore, this improvement is not limited to the demons algorithm, but applicable in most typical deformable registration algorithms.
reperfusion
发表于 2025-3-23 18:42:58
Diffeomorphic Density Registration in Thoracic Computed Tomography,lty on local tissue compressibility. This algorithm appropriately models highly compressible areas of the body (such as the lungs) and incompressible areas (such as surrounding soft tissue and bones).
Cardioplegia
发表于 2025-3-24 01:32:49
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Pseudoephedrine
发表于 2025-3-24 04:12:33
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Measured
发表于 2025-3-24 09:27:36
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Virtues
发表于 2025-3-24 13:20:54
Accuracy Estimation for Medical Image Registration Using Regression Forests,e classes (correct, poor and wrong registration) is 93.4 %, comparing favorably to a competing method. In conclusion, a method was proposed that for the first time shows the feasibility of automatic registration assessment by means of regression, and promising results were obtained.
Hiatus
发表于 2025-3-24 15:21:19
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gruelling
发表于 2025-3-24 19:20:24
A Deep Metric for Multimodal Registration,tric is validated on intersubject deformable registration on a dataset different from the one used for training, demonstrating good generalization. In this task, we outperform mutual information by a significant margin.
cornucopia
发表于 2025-3-25 02:19:43
Deformation Estimation with Automatic Sliding Boundary Computation,forced by disallowing separation or overlap between regions. Optimization alternates between discrete segmentation estimation and continuous deformation estimation. We demonstrate our method on chest 4DCT data showing sliding motion of the lungs against the thoracic cage during breathing.