Liu Jing, Li Weifu, Xiao Chi, Hong Bei, Xie Qiwei, Han Hua
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:628-631. doi: 10.1109/EMBC.2018.8512393.
Investigating the link between mitochondrial function and its physical structure is a hot topic in neurobiology research. With the rapid development of Scanning Electron Microscope (SEM), we can look closely into the fine mitochondrial structure with high resolution. Consequently, many meaningful researches have focused on how to detect and segment the mitochondria from EM images. Due to the complex background, hand-crafted features designed by traditional algorithms cannot provide satisfying results. In this paper, we propose an effective deep neural network improved from Mask R-CNN to produce the detection and segmentation results. On this base, we use the morphological processing and mitochondrial context information to rectify the local misleading results. The valuation was performed on two widely used datasets (FIB-SEM and ATUMSEM), and the results demonstrate that the proposed method has comparable performance than state-of-the-art methods.
研究线粒体功能与其物理结构之间的联系是神经生物学研究中的一个热门话题。随着扫描电子显微镜（SEM）的迅速发展，我们能够以高分辨率仔细观察线粒体的精细结构。因此，许多有意义的研究都集中在如何从电子显微镜图像中检测和分割线粒体。由于背景复杂，传统算法设计的手工特征无法提供令人满意的结果。在本文中，我们提出了一种从Mask R-CNN改进而来的有效深度神经网络，以产生检测和分割结果。在此基础上，我们使用形态学处理和线粒体上下文信息来纠正局部误导性结果。在两个广泛使用的数据集（FIB-SEM和ATUMSEM）上进行了评估，结果表明所提出的方法具有与现有最先进方法相当的性能。
