Generic 3D Segmentation in Medicine based on a Self-learning Topological Model

Three-dimensional segmentation of medical image data is crucial in modern diagnostics and still subject of intensive research efforts. Most fully automated methods, e.g. the segmentation of the hippocampus, are highly specific for certain morphological regions and very sensitive to variations in input data, thus robustness is not sufficient to achieve sufficient accuracy to serve in differential diagnosis. In this work a processing pipeline for robust segmentation is presented. The flexibility of this novel generic segmentation method is based on entirely parameter-free pre-segmentation. Therefore a hybrid modification of the watershed algorithm is developed, employing both gradient and intensity metrics for the identification of connected regions depending on similar properties. In a further optimization step the vast number of small regions is condensed to anatomically meaningful structures by feature based classification. The core of the classification process is a topographical model of the segmented body region, representing a sufficient number of features from geometry and the texture domain. The model may learn from manual segmentation by experts or from its own results. The novel method is demonstrated for the human brain, based on the reference data set from brainweb. Results show high accuracy and the method proves to be robust. The method is easily extensible to other body regions and the novel concept shows high potential to introduce generic segmentation in the three-dimensional domain into a clinical work-flow.