# A convolutional autoencoder approach for mining features in cellular electron cryo-tomograms and weakly supervised coarse segmentation Source: [A convolutional autoencoder approach for mining features in cellular electron cryo-tomograms and weakly supervised coarse segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:56:20.400Z Authors: Xiangrui Zeng, Miguel Ricardo Leung, Tzviya Zeev-Ben-Mordehai, Min Xu Published: 2017-06-15T17:13:37.000Z ## Abstract Cellular electron cryo-tomography enables the 3D visualization of cellular organization in the near-native state and at submolecular resolution. However, the contents of cellular tomograms are often complex, making it difficult to automatically isolate different in situ cellular components. In this paper, we propose a convolutional autoencoder-based unsupervised approach to provide a coarse grouping of 3D small subvolumes extracted from tomograms. We demonstrate that the autoencoder can be used for efficient and coarse characterization of features of macromolecular complexes and surfaces, such as membranes. In addition, the autoencoder can be used to detect non-cellular features related to sample preparation and data collection, such as carbon edges from the grid and tomogram boundaries. The autoencoder is also able to detect patterns that may indicate spatial interactions between cellular components. Furthermore, we demonstrate that our autoencoder can be used for weakly supervised semantic segmentation of cellular components, requiring a very small amount of manual annotation. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()