3D Statistical Shape Models Incorporating Landmark-Wise Random Regression Forests for Omni-Directional Landmark Detection

• Published in: IEEE transactions on medical imaging Vol. 36; no. 1; pp. 155 - 168
• Main Authors: Norajitra, Tobias, Maier-Hein, Klaus H.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 3D Statistical Shape Models; Adaptation models; Algorithms; Computational modeling; Data processing; Humans; Image enhancement; Image processing; Image segmentation; Independence from Model Initialization; Kidneys; Liver; Mathematical models; Medical imaging; Models, Statistical; Multi-Organ Segmentation; Multiscale analysis; Omni-directional Search; Organs; Random Forest Regression Voting; Regression analysis; Reproducibility of Results; Searching; Shape; Solid modeling; Spleen; Statistical analysis; Three dimensional models; Three-dimensional displays; Tomography, X-Ray Computed; Training

3D Statistical Shape Models (3D-SSM) are widely used for medical image segmentation. However, during segmentation, they typically perform a very limited unidirectional search for suitable landmark positions in the image, relying on weak learners or use-case specific appearance models that solely tak...