Iterative multi-class multi-scale stacked sequential learning: Definition and application to medical volume segmentation

• We present the iterative multiclass multiscale stacked sequential learning framework.
• This method learns a set of contextual properties from a stack of learners.
• We test the method on three medical volume multi-class segmentation problems.
• The method is easy to implement and independent to the feature space and classifier.

In this work we present the iterative multi-class multi-scale stacked sequential learning framework (IMMSSL), a novel learning scheme that is particularly suited for medical volume segmentation applications. This model exploits the inherent voxel contextual information of the structures of interest in order to improve its segmentation performance results. Without any feature set or learning algorithm prior assumption, the proposed scheme directly seeks to learn the contextual properties of a region from the predicted classifications of previous classifiers within an iterative scheme. Performance results regarding segmentation accuracy in three two-class and multi-class medical volume datasets show a significant improvement with respect to state of the art alternatives. Due to its easiness of implementation and its independence of feature space and learning algorithm, the presented machine learning framework could be taken into consideration as a first choice in complex volume segmentation scenarios.