Multi-task Learning for Macromolecule Classification, Segmentation and Coarse Structural Recovery in Cryo-Tomography
Multi-task Learning for Macromolecule Classification, Segmentation and Coarse Structural Recovery in Cryo-Tomography
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发表时间：
2018-05
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通讯作者：
Chang Liu;Xiangrui Zeng;Kaiwen Wang;Qiang Guo;Min Xu
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作者：
Chang Liu;Xiangrui Zeng;Kaiwen Wang;Qiang Guo;Min Xu
细胞电子冷冻断层扫描(CECT)是研究单细胞内大分子天然结构和组织的一种强大的三维成像工具。为了系统地识别和恢复CECT捕捉到的大分子结构，人们已经开发出了几种重要的方法，如亚断层图像分类和语义分割。然而，由于分子结构的多样性、拥挤的分子环境以及CECT成像的局限性，大分子结构的识别和恢复仍然是非常困难的。在本文中，我们提出了一种新的多任务三维卷积神经网络模型，用于同时对亚断层图像中感兴趣的大分子进行分类、分割和粗略结构恢复。在我们的模型中，一个任务的学习图像特征是共享的，从而相互加强了其他任务的学习。在实际模拟和实验的CECT数据上进行评估，我们的多任务学习模型在分类和分割方面优于所有的单任务学习方法。此外，我们还证明了我们的模型可以推广到发现、分割和恢复训练数据中不存在的新结构。
Cellular Electron Cryo-Tomography (CECT) is a powerful 3D imaging tool for studying the native structure and organization of macromolecules inside single cells. For systematic recognition and recovery of macromolecular structures captured by CECT, methods for several important tasks such as subtomogram classification and semantic segmentation have been developed. However, the recognition and recovery of macromolecular structures are still very difficult due to high molecular structural diversity, crowding molecular environment, and the imaging limitations of CECT. In this paper, we propose a novel multi-task 3D convolutional neural network model for simultaneous classification, segmentation, and coarse structural recovery of macromolecules of interest in subtomograms. In our model, the learned image features of one task are shared and thereby mutually reinforce the learning of other tasks. Evaluated on realistically simulated and experimental CECT data, our multi-task learning model outperformed all single-task learning methods for classification and segmentation. In addition, we demonstrate that our model can generalize to discover, segment and recover novel structures that do not exist in the training data.