论文标题
使用空间连续性从电子显微镜图像对线粒体进行半监督分割
Semi-Supervised Segmentation of Mitochondria from Electron Microscopy Images Using Spatial Continuity
论文作者
论文摘要
线粒体的形态在介导其生理功能中起关键作用。从3D电子显微镜（EM）图像对线粒体的准确分割对于在纳米尺度上对其形态的定量表征至关重要。为此任务开发的全面监督的深度学习模型具有出色的性能，但需要大量的带注释的数据进行培训。然而，EM图像的手动注释由于其大量，有限的对比度和低信噪比（SNR）（SNR）而费时且耗时。为了克服这一挑战，我们提出了一个半监督的深度学习模型，该模型通过在标记和未标记的图像中利用其结构，形态和上下文信息的空间连续性来分割线粒体。我们使用随机的分段仿射转换来综合综合和现实的线粒体形态来增强训练数据。 EPFL数据集的实验表明，我们的模型的性能与最先进的完全监督模型相似，但仅需要约20％的注释培训数据。我们的半监督模型具有通用性，还可以从EM图像中准确地分割其他空间连续结构。该研究的数据和代码可在https://github.com/cbmi-group/mpp上公开访问。
Morphology of mitochondria plays critical roles in mediating their physiological functions. Accurate segmentation of mitochondria from 3D electron microscopy (EM) images is essential to quantitative characterization of their morphology at the nanometer scale. Fully supervised deep learning models developed for this task achieve excellent performance but require substantial amounts of annotated data for training. However, manual annotation of EM images is laborious and time-consuming because of their large volumes, limited contrast, and low signal-to-noise ratios (SNRs). To overcome this challenge, we propose a semi-supervised deep learning model that segments mitochondria by leveraging the spatial continuity of their structural, morphological, and contextual information in both labeled and unlabeled images. We use random piecewise affine transformation to synthesize comprehensive and realistic mitochondrial morphology for augmentation of training data. Experiments on the EPFL dataset show that our model achieves performance similar as that of state-of-the-art fully supervised models but requires only ~20% of their annotated training data. Our semi-supervised model is versatile and can also accurately segment other spatially continuous structures from EM images. Data and code of this study are openly accessible at https://github.com/cbmi-group/MPP.
