Luo Zhengrong, Wang Ye, Liu Shikun, Peng Jialin
College of Computer Science and Technology, Huaqiao University, Xiamen, China.
School of Statistics, Huaqiao University, Xiamen, China.
Front Neurosci. 2021 Jun 24;15:687832. doi: 10.3389/fnins.2021.687832. eCollection 2021.
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.
从电子显微镜（EM）图像中对线粒体进行语义分割是获取有关线粒体可靠形态统计信息的关键步骤。然而，要从复杂背景中以足够的精度自动勾勒出大量形状各异的线粒体具有挑战性。为应对这些挑战，我们开发了一种分层编码器-解码器网络（HED-Net），它具有三级嵌套U形架构以捕获丰富的上下文信息。鉴于线粒体形状不规则，我们引入了一种新颖的软标签分解策略，以利用手动标注中的形状知识。在模型训练中，我们不是简单地将真实标签图用作唯一监督，而是通过根据线粒体的椭圆率将每个手动标签图软分解为两个互补标签图来引入额外的子类别感知监督。这三个标签图与我们的HED-Net集成，以监督模型训练。原始标签图引导网络分割所有形状各异的线粒体，而辅助标签图分别引导网络分割圆形和椭圆形线粒体的子类别，这是更易于管理的任务。在两个公共基准上进行的大量实验表明，我们的HED-Net优于现有方法。
