Search for: All records

Abstract Particle picking in cryo-electron tomograms (cryo-ET) is crucial for in situ structure detection of macro-molecules and protein complexes. The traditional template-matching-based approaches for particle picking suffer from template-specific biases and have low throughput. Given these problems, learning-based solutions are necessary for particle picking. However, the paucity of annotated data for training poses substantial challenges for such learning-based approaches. Moreover, preparing extensively annotated cryo-ET tomograms for particle picking is extremely time-consuming and burdensome. Addressing these challenges, we present TomoPicker, an annotation-efficient particle-picking approach that can effectively pick particles when only a minuscule portion (∼ 0.3 − 0.5%) of the total particles in a cellular cryo-ET dataset is provided for training. TomoPicker regards particle picking as a voxel classification problem and solves it with two different positive-unlabeled learning approaches. We evaluated our method on a benchmark cryo-ET dataset of eukaryotic cells, where we observed about 30% improvement by TomoPicker against the most recent state-of-the-art annotation efficient learning-based picking approaches.