This article explores the application of a tree-based feature extraction algorithm for the widely-used MSER features, and proposes a Tree of Shapes based detector of Maximally Stable Regions. Changing an underlying component tree in the algorithm allows considering alternative properties and pixel orderings for extracting the Maximally Stable Regions. Differences introduced to the region structure with changing the underlying tree are discussed, as well as the spatial organization of the detected regions imposed by using a self-dual image representation for detection. Performance evaluation is carried out on a standard matching benchmark in terms of repeat ability and matching score under different image transformations, as well as in a large scale image retrieval setup, measuring Mean Average Precision. The proposed de-scriptor is compared to the standard MSER implementation as well as a tree-based MSER implementation, achieving competitive results in the matching setup and outperforming the baseline MSER in the retrieval experiments.