AITom: Open-source AI platform for cryo-electron tomography data analysis

Zeng, Xiangrui; Xu, Min

doi:10.48550/arxiv.1911.03044

Cited by 5 publications

(6 citation statements)

References 26 publications

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“…We will disseminate the subtomogram averages into EM Data Bank [71]. The trained models and demo data will be disseminated into AITom [25].…”

Section: Data Sourcementioning

confidence: 99%

“…Furthermore, we have demonstrated other functionalities of DUAL: (1) DUAL can convert the styles between experimental tomograms of different imaging sources, such as low-SNR tomograms to high-SNR tomograms, for the purpose of noise reduction to desired levels, tomogram simulation with natural packing models, and domain adaptation for neural network training; and (2) DUAL can perform unsupervised learning based missing wedge compensation directly on the 3D reconstructed tomograms for reducing resolution anisotropy. The tutorial, code, and demo models will be available through the open-source Github software AITom [25] to provide easy and user-friendly access to the cryo-ET community.…”

Section: Introductionmentioning

confidence: 99%

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DUAL: deep unsupervised simultaneous simulation and denoising for cryo-electron tomography

Zeng,

Ding,

Zhang

et al. 2024

Preprint

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Recent biotechnological developments in cryo-electron tomography allow direct visualization of native sub-cellular structures with unprecedented details and provide essential information on protein functions/dysfunctions. Denoising can enhance the visualization of protein structures and distributions. Automatic annotation via data simulation can ameliorate the time-consuming manual labeling of large-scale datasets. Here, we combine the two major cryo-ET tasks together in DUAL, by a specific cyclic generative adversarial network with novel noise disentanglement. This enables end-to-end unsupervised learning that requires no labeled data for training. The denoising branch outperforms existing works and substantially improves downstream particle picking accuracy on benchmark datasets. The simulation branch provides learning-based cryo-ET simulation for the first time and generates synthetic tomograms indistinguishable from experimental ones. Through comprehensive evaluations, we showcase the effectiveness of DUAL in detecting macromolecular complexes across a wide range of molecular weights in experimental datasets. The versatility of DUAL is expected to empower cryo-ET researchers by improving visual interpretability, enhancing structural detection accuracy, expediting annotation processes, facilitating cross-domain model adaptability, and compensating for missing wedge artifacts. Our work represents a significant advancement in the unsupervised mining of protein structures in cryo-ET, offering a multifaceted tool that facilitates cryo-ET research.

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“…We will disseminate the subtomogram averages into EM Data Bank [71]. The trained models and demo data will be disseminated into AITom [25].…”

Section: Data Sourcementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

DUAL: deep unsupervised simultaneous simulation and denoising for cryo-electron tomography

Zeng,

Ding,

Zhang

et al. 2024

Preprint

Self Cite

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“…In recent years, several computational groups have focused on automatically detecting and segmenting biomolecular shapes in such tomograms by using techniques such as deep learning [ 2 , 3 , 4 ]. Furthermore, a number of toolboxes have been developed for tomography analysis [ 5 , 6 , 7 ]. These efforts have mainly been aimed at improving the signal-to-noise ratio by averaging multiple particles in subtomogram averaging [ 8 ]; however, the averaging approach is unsuitable for long cytoskeletal filaments that exhibit variable shapes.…”

Section: Introductionmentioning

confidence: 99%

Spaghetti Tracer: A Framework for Tracing Semiregular Filamentous Densities in 3D Tomograms

Sazzed

Scheible

et al. 2022

Biomolecules

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Within cells, cytoskeletal filaments are often arranged into loosely aligned bundles. These fibrous bundles are dense enough to exhibit a certain regularity and mean direction, however, their packing is not sufficient to impose a symmetry between—or specific shape on—individual filaments. This intermediate regularity is computationally difficult to handle because individual filaments have a certain directional freedom, however, the filament densities are not well segmented from each other (especially in the presence of noise, such as in cryo-electron tomography). In this paper, we develop a dynamic programming-based framework, Spaghetti Tracer, to characterizing the structural arrangement of filaments in the challenging 3D maps of subcellular components. Assuming that the tomogram can be rotated such that the filaments are oriented in a mean direction, the proposed framework first identifies local seed points for candidate filament segments, which are then grown from the seeds using a dynamic programming algorithm. We validate various algorithmic variations of our framework on simulated tomograms that closely mimic the noise and appearance of experimental maps. As we know the ground truth in the simulated tomograms, the statistical analysis consisting of precision, recall, and F1 scores allows us to optimize the performance of this new approach. We find that a bipyramidal accumulation scheme for path density is superior to straight-line accumulation. In addition, the multiplication of forward and backward path densities provides for an efficient filter that lifts the filament density above the noise level. Resulting from our tests is a robust method that can be expected to perform well (F1 scores 0.86–0.95) under experimental noise conditions.

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“…Extensive experiments demonstrate that our method can achieve state-of-the-art results on both simulated cryo-ET benchmark and real dataset. The code is available in AITom [10].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Multi-Task Learning for 3D Subtomogram Image Alignment, Clustering and Segmentation

Zhu

Wang

et al. 2022

2022 IEEE International Conference on Image Processing (ICIP)

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3D subtomogram image alignment, clustering, and segmentation are vital to macromolecular structure recognition in cryo-electron tomography (cryo-ET). However, acquiring ground-truth labels to train a unified deep learning model that can simultaneously deal with these tasks is unaffordable. To this end, we propose an end-to-end unified multi-task learning framework to simultaneously complete the three tasks, where models are trained in an unsupervised manner without using any labels. In particular, we have three parallel branches. In the alignment branch, we adopt a two-stage training scheme, i.e., self-supervised pretraining and constrained unsupervised training using our proposed skip correlation attention layer and constrained loss. Synchronously, in the clustering branch, the learned deep cluster features are utilized to iteratively cluster subtomograms into groups using pseudo-labels from an image-wise Gaussian Mixture Model (GMM). Meanwhile, in the segmentation branch, we use rough pseudo-labels generated from a voxel-wise GMM as supervision signals, and prior knowledge from humans is utilized to jointly learn how to correct these labels as well as predict reliable segmentation results. Benefiting from the end-to-end unified network architecture, our method achieves overall state-of-the-art performance on both simulated and real subtomogram processing benchmarks.

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AITom: Open-source AI platform for cryo-electron tomography data analysis

Cited by 5 publications

References 26 publications

DUAL: deep unsupervised simultaneous simulation and denoising for cryo-electron tomography

DUAL: deep unsupervised simultaneous simulation and denoising for cryo-electron tomography

Spaghetti Tracer: A Framework for Tracing Semiregular Filamentous Densities in 3D Tomograms

Unsupervised Multi-Task Learning for 3D Subtomogram Image Alignment, Clustering and Segmentation

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