Instance-Based Learning Algorithms
Machine Learning
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Inter-subject modelling of liver deformation during radiation therapy
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Construction of patient specific atlases from locally most similar anatomical pieces
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
Prediction framework for statistical respiratory motion modeling
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
3D organ motion prediction for MR-guided high intensity focused ultrasound
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part II
A bayesian framework for estimating respiratory liver motion from sparse measurements
MICCAI'11 Proceedings of the Third international conference on Abdominal Imaging: computational and Clinical Applications
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The effective modeling and predicting of respiratory motion in abdominal organs is crucial to the task of tumor treatments. Current approaches in statistical respiration modeling either build a subject-specific model which is only suitable for that single subject or create a population model by assuming a coherent population with a relatively simple distribution and, therefore, fail to account for variations of the breathing pattern among different subjects. To bridge this gap, we propose a more flexible method based on exemplar models, which is able to cope with heterogeneous population data and can be better adapted to a previously unseen subject. We have showed that, in contrary to principal component analysis based models, our method is capable of effectively utilizing complementary information provided by increasing number of examples taken from a population. In addition to being more robust against outliers, the proposed method also achieved lower mean errors in leave-one-out experiments.