Mitochondria Segmentation From EM Images via Hierarchical Structured Contextual Forest.

Delineation of mitochondria from electron microscopy (EM) images is crucial to investigate its morphology and distribution, which are directly linked to neural dysfunction. However, it is a challenging task due to the varied appearances, sizes and shapes of mitochondria, and complicated surrounding structures. Exploiting sufficient contextual information about interactions in extended neighborhood is crucial to address the challenges. To this end, we introduce a novel class of contextual features, namely local patch pattern (LPP), to eliminate the ambiguity of local appearance and texture features. To achieve accurate segmentation, we propose an automatic method by iterative learning of hierarchical structured contextual forest. With a novel median fusion strategy, the probability predictions from long history iterations are augmented to encode spatial and temporal contexts and suppress false detections. Moreover, the LPP features are extracted on both images and history predictions, resulting in a hierarchy of contextual features with increasing receptive fields. Other than using computationally demanding graph based methods, we perform joint label prediction using structured random forest. In addition to direct 3D segmentation of EM volumes, we introduce a 2D variant without sacrificing accuracy using a novel hierarchical multi-view fusion strategy. We evaluated our proposed methods on public EPFL Hippocampus benchmark, achieving state-of-the-art performance of 90.9% in Dice. Quantitative comparison showed the effectiveness of the proposed features and strategies.