基于深度学习的电子冷冻子图监督语义分割。

Chang Liu, Xiangrui Zeng, Ruogu Lin, Xiaodan Liang, Zachary Freyberg, Eric Xing, Min Xu
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引用次数: 14

摘要

细胞电子冷冻层析成像(CECT)是一种强大的成像技术,可在亚分子分辨率下对细胞结构和组织进行三维可视化。它能够分析大分子复合物的天然结构及其在单细胞内的空间组织。然而,由于结构的高度复杂性和实际成像的局限性,CECT图像中系统的大分子结构恢复仍然具有挑战性。特别是,由于高分子拥挤,大分子的回收可能会受到其相邻结构的影响。为了减少偏差,我们在这里引入了一种受全卷积网络和编码器-解码器架构启发的新型3D卷积神经网络,用于子图中感兴趣的大分子的监督分割。我们的模型在真实模拟的CECT数据上的测试表明,与基线方法相比,我们的新方法显著提高了分割性能。此外,我们还证明了所提出的模型具有泛化能力,可以分割训练数据中不存在的新结构。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DEEP LEARNING BASED SUPERVISED SEMANTIC SEGMENTATION OF ELECTRON CRYO-SUBTOMOGRAMS.

Cellular Electron Cryo-Tomography (CECT) is a powerful imaging technique for the 3D visualization of cellular structure and organization at submolecular resolution. It enables analyzing the native structures of macromolecular complexes and their spatial organization inside single cells. However, due to the high degree of structural complexity and practical imaging limitations, systematic macromolecular structural recovery inside CECT images remains challenging. Particularly, the recovery of a macromolecule is likely to be biased by its neighbor structures due to the high molecular crowding. To reduce the bias, here we introduce a novel 3D convolutional neural network inspired by Fully Convolutional Network and Encoder-Decoder Architecture for the supervised segmentation of macromolecules of interest in subtomograms. The tests of our models on realistically simulated CECT data demonstrate that our new approach has significantly improved segmentation performance compared to our baseline approach. Also, we demonstrate that the proposed model has generalization ability to segment new structures that do not exist in training data.

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