Combined irrigation provides sufficient reduction in temperature changes during drilling, and it may be more beneficial in deeper site osteotomies. Further studies to optimize the effects of a combined irrigation are needed.
Abstract-A fast AAM search algorithm based on canonical correlation analysis (CCA-AAM) is introduced. It efficiently models the dependency between texture residuals and model parameters during search. Experiments show that CCAAAMs, while requiring similar implementation effort, consistently outperform standard search with regard to convergence speed by a factor of four.Index Terms-Image processing and computer vision, active appearance models, statistical image models, subspace methods, medical imaging.
The identification and quantification of markers in medical images is critical for diagnosis, prognosis, and disease management. Supervised machine learning enables the detection and exploitation of findings that are known a priori after annotation of training examples by experts. However, supervision does not scale well, due to the amount of necessary training examples, and the limitation of the marker vocabulary to known entities. In this proof-of-concept study, we propose unsupervised identification of anomalies as candidates for markers in retinal Optical Coherence Tomography (OCT) imaging data without a constraint to a priori definitions. We identify and categorize marker candidates occurring frequently in the data, and demonstrate that these markers show predictive value in the task of detecting disease. A careful qualitative analysis of the identified data driven markers reveals how their quantifiable occurrence aligns with our current understanding of disease course, in early-and late age-related macular degeneration (AMD) patients. A multiscale deep denoising autoencoder is trained on healthy images, and a one-class support vector machine identifies anomalies in new data. Clustering in the anomalies identifies stable categories. Using these markers to classify healthy-, early AMD-and late AMD cases yields an accuracy of 81.40%. In a second binary classification experiment on a publicly available data set (healthy vs. intermediate AMD) the model achieves an AUC of 0.944.
Graphical abstractHighlights► Automatic localization of landmarks in complex, repetitive anatomical structures. ► Random Forest classifiers for every landmark as a pre-filtering stage. ► Hough regression model for refining the landmark candidate positions. ► Parts-based model of global landmark topology to select the final landmark positions. ► Results on three challenging data sets, median residuals of 0.80 mm, 1.19 mm, 2.71 mm.
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