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We introduce a dynamic multiscale tree (DMT) architecture that learns how toleverage the strengths of different existing classifiers for supervisedmulti-label image segmentation. Unlike previous works that simply aggregate orcascade classifiers for addressing image segmentation and labeling tasks, wepropose to embed strong classifiers into a tree structure that allowsbi-directional flow of information between its classifier nodes to graduallyimprove their performances. Our DMT is a generic classification model thatinherently embeds different cascades of classifiers while enhancing learningtransfer between them to boost up their classification accuracies.Specifically, each node in our DMT can nest a Structured Random Forest (SRF)classifier or a Bayesian Network (BN) classifier. The proposed SRF-BN DMTarchitecture has several appealing properties. First, while SRF operates at apatch-level (regular image region), BN operates at the super-pixel level(irregular image region), thereby enabling the DMT to integrate multi-levelimage knowledge in the learning process. Second, although BN is powerful inmodeling dependencies between image elements (superpixels, edges) and theirfeatures, the learning of its structure and parameters is challenging. On theother hand, SRF may fail to accurately detect very irregular object boundaries.The proposed DMT robustly overcomes these limitations for both classifiersthrough the ascending and descending flow of contextual information betweeneach parent node and its children nodes. Third, we train DMT using differentscales, where we progressively decrease the patch and superpixel sizes as we godeeper along the tree edges nearing its leaf nodes. Last, DMT demonstrates itsoutperformance in comparison to several state-of-the-art segmentation methodsfor multi-labeling of brain images with gliomas.

Dynamic Multiscale Tree Learning Using Ensemble Strong Classifiers for Multi-label Segmentation of Medical Images with Lesions