Semantic segmentation of mitochondria from electron microscopy (EM) images is an essential step to obtain reliable morphological statistics about mitochondria. However, automatically delineating plenty of mitochondria of varied shapes from complex backgrounds with sufficient accuracy is challenging. To address these challenges, we develop a hierarchical encoder-decoder network (HED-Net), which has a three-level nested U-shape architecture to capture rich contextual information. Given the irregular shape of mitochondria, we introduce a novel soft label-decomposition strategy to exploit shape knowledge in manual labels. Rather than simply using the ground truth label maps as the unique supervision in the model training, we introduce additional subcategory-aware supervision by softly decomposing each manual label map into two complementary label maps according to mitochondria's ovality. The three label maps are integrated with our HED-Net to supervise the model training. While the original label map guides the network to segment all the mitochondria of varied shapes, the auxiliary label maps guide the network to segment subcategories of mitochondria of circular shape and elliptic shape, respectively, which are much more manageable tasks. Extensive experiments on two public benchmarks show that our HED-Net performs favorably against state-of-the-art methods.
Keywords: convolutional neural networks; electron microscopy image; hierarchical encoder-decoder; image segmentation; mitochondria segmentation.
Copyright © 2021 Luo, Wang, Liu and Peng.