Automatic Segmentation of Mitochondria from EM Images via Hierarchical Context Forest

To solve the problem of automatic mitochondria segmentation from electron microscope (EM) images, a hierarchical context forest (HCF) model using multi-level context features was proposed. Exploring effective contextual information is crucial to address the challenges caused by the varied appearances and shapes of mitochondria, and the complicated image content. To this end, a novel class of features named local patch pattern (LPP) are designed to characterize local contextual information, which are used to resolve the ambiguity caused by similar appearances and intensities of different organelles. Furthermore, to capture long-range contextual information, we also extract LPP features on intermediate probability predictions of the HCF model. Moreover, a multiscale strategy is used to capture different sizes of mitochondria. Solid validations of our method conducted on public dataset demonstrated the effectiveness of both of the proposed LPP features and the proposed model. The result of comparison showed that, the proposed method achieved distinct improvement of results in terms of Precision, Recall and F1-value score.