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1. Localization of macromolecules in crowded cellular cryo-electron tomograms from extremely sparse labels

   https://doi.org/10.1093/bib/bbaf630  

   Uddin, Mostofa Rafid; Ahmed, Ajmain Yasar; Tabib, H_M Shadman; Tahmid, Md Toki; Alam, Md_Zarif Ul; Freyberg, Zachary; Xu, Min (November 2025, Briefings in Bioinformatics)

   Abstract Localizing macromolecules in crowded cellular cryo-electron tomography (cryo-ET) images or tomograms is crucial for determining their in situ structures. Traditional template matching-based approaches for this task suffer from template-specific biases and have low throughput. Given these problems, learning-based solutions are necessary. However, the paucity of annotated data for training poses substantial challenges for such learning-based methods. Moreover, preparing extensively annotated cellular tomograms for training macromolecule localization methods is extremely time-consuming and burdensome due to the large volume and low signal-to-noise ratio of the tomograms. In this work, we developed TomoPicker, an annotation-efficient macromolecule localization method for tomograms. To achieve such annotation-efficiency, TomoPicker regards macromolecule localization as a voxel classification problem and solves it with two different positive-unlabeled learning approaches. We evaluated TomoPicker on two experimental cryo-ET datasets of crowded eukaryotic cells and one experimental dataset of relatively less crowded prokaryotic cell. We observed that, with only 10 annotated macromolecule locations, TomoPicker with positive unlabeled learning achieved a performance comparable to that of state-of-the-art supervised methods trained with several hundred annotations. In other words, TomoPicker achieved plausible segmentation with up to 98% less data compared with supervised learning-based methods. Furthermore, it demonstrated substantial improvements over existing learning-based macromolecule localization methods under sparse annotation scenarios. 

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2. DeepCryoPicker: fully automated deep neural network for single protein particle picking in cryo-EM

   https://doi.org/10.1186/s12859-020-03809-7  

   Al-Azzawi, Adil; Ouadou, Anes; Max, Highsmith; Duan, Ye; Tanner, John J.; Cheng, Jianlin (December 2020, BMC Bioinformatics)

   null (Ed.)

   Abstract Background Cryo-electron microscopy (Cryo-EM) is widely used in the determination of the three-dimensional (3D) structures of macromolecules. Particle picking from 2D micrographs remains a challenging early step in the Cryo-EM pipeline due to the diversity of particle shapes and the extremely low signal-to-noise ratio of micrographs. Because of these issues, significant human intervention is often required to generate a high-quality set of particles for input to the downstream structure determination steps. Results Here we propose a fully automated approach (DeepCryoPicker) for single particle picking based on deep learning. It first uses automated unsupervised learning to generate particle training datasets. Then it trains a deep neural network to classify particles automatically. Results indicate that the DeepCryoPicker compares favorably with semi-automated methods such as DeepEM, DeepPicker, and RELION, with the significant advantage of not requiring human intervention. Conclusions Our framework combing supervised deep learning classification with automated un-supervised clustering for generating training data provides an effective approach to pick particles in cryo-EM images automatically and accurately. 

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3. Auto3DCryoMap: an automated particle alignment approach for 3D cryo-EM density map reconstruction

   https://doi.org/10.1186/s12859-020-03885-9  

   Al-Azzawi, Adil; Ouadou, Anes; Duan, Ye; Cheng, Jianlin (December 2020, BMC Bioinformatics)

   null (Ed.)

   Abstract Background Cryo-EM data generated by electron tomography (ET) contains images for individual protein particles in different orientations and tilted angles. Individual cryo-EM particles can be aligned to reconstruct a 3D density map of a protein structure. However, low contrast and high noise in particle images make it challenging to build 3D density maps at intermediate to high resolution (1–3 Å). To overcome this problem, we propose a fully automated cryo-EM 3D density map reconstruction approach based on deep learning particle picking. Results A perfect 2D particle mask is fully automatically generated for every single particle. Then, it uses a computer vision image alignment algorithm (image registration) to fully automatically align the particle masks. It calculates the difference of the particle image orientation angles to align the original particle image. Finally, it reconstructs a localized 3D density map between every two single-particle images that have the largest number of corresponding features. The localized 3D density maps are then averaged to reconstruct a final 3D density map. The constructed 3D density map results illustrate the potential to determine the structures of the molecules using a few samples of good particles. Also, using the localized particle samples (with no background) to generate the localized 3D density maps can improve the process of the resolution evaluation in experimental maps of cryo-EM. Tested on two widely used datasets, Auto3DCryoMap is able to reconstruct good 3D density maps using only a few thousand protein particle images, which is much smaller than hundreds of thousands of particles required by the existing methods. Conclusions We design a fully automated approach for cryo-EM 3D density maps reconstruction (Auto3DCryoMap). Instead of increasing the signal-to-noise ratio by using 2D class averaging, our approach uses 2D particle masks to produce locally aligned particle images. Auto3DCryoMap is able to accurately align structural particle shapes. Also, it is able to construct a decent 3D density map from only a few thousand aligned particle images while the existing tools require hundreds of thousands of particle images. Finally, by using the pre-processed particle images,Auto3DCryoMap reconstructs a better 3D density map than using the original particle images. 

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4. Self‐supervised learning improves classification of agriculturally important insect pests in plants

   https://doi.org/10.1002/ppj2.20079  

   Kar, Soumyashree; Nagasubramanian, Koushik; Elango, Dinakaran; Carroll, Matthew E.; Abel, Craig A.; Nair, Ajay; Mueller, Daren S.; O'Neal, Matthew E.; Singh, Asheesh K.; Sarkar, Soumik; et al (December 2023, The Plant Phenome Journal)

   Abstract Insect pests cause significant damage to food production, so early detection and efficient mitigation strategies are crucial. There is a continual shift toward machine learning (ML)‐based approaches for automating agricultural pest detection. Although supervised learning has achieved remarkable progress in this regard, it is impeded by the need for significant expert involvement in labeling the data used for model training. This makes real‐world applications tedious and oftentimes infeasible. Recently, self‐supervised learning (SSL) approaches have provided a viable alternative to training ML models with minimal annotations. Here, we present an SSL approach to classify 22 insect pests. The framework was assessed on raw and segmented field‐captured images using three different SSL methods, Nearest Neighbor Contrastive Learning of Visual Representations (NNCLR), Bootstrap Your Own Latent, and Barlow Twins. SSL pre‐training was done on ResNet‐18 and ResNet‐50 models using all three SSL methods on the original RGB images and foreground segmented images. The performance of SSL pre‐training methods was evaluated using linear probing of SSL representations and end‐to‐end fine‐tuning approaches. The SSL‐pre‐trained convolutional neural network models were able to perform annotation‐efficient classification. NNCLR was the best performing SSL method for both linear and full model fine‐tuning. With just 5% annotated images, transfer learning with ImageNet initialization obtained 74% accuracy, whereas NNCLR achieved an improved classification accuracy of 79% for end‐to‐end fine‐tuning. Models created using SSL pre‐training consistently performed better, especially under very low annotation, and were robust to object class imbalances. These approaches help overcome annotation bottlenecks and are resource efficient. 

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5. Vox-UDA: Voxel-wise Unsupervised Domain Adaptation for Cryo-Electron Subtomogram Segmentation with Denoised Pseudo-Labeling

   https://doi.org/10.1609/aaai.v39i1.32019  

   Li, Haoran; Li, Xingjian; Shi, Jiahua; Chen, Huaming; Du, Bo; Kihara, Daisuke; Barthelemy, Johan; Shen, Jun; Xu, Min (April 2025, Proceedings of the AAAI Conference on Artificial Intelligence)

   Cryo-Electron Tomography (cryo-ET) is a 3D imaging technology that facilitates the study of macromolecular structures at near-atomic resolution. Recent volumetric segmentation approaches on cryo-ET images have drawn widespread interest in the biological sector. However, existing methods heavily rely on manually labeled data, which requires highly professional skills, thereby hindering the adoption of fully-supervised approaches for cryo-ET images. Some unsupervised domain adaptation (UDA) approaches have been designed to enhance the segmentation network performance using unlabeled data. However, applying these methods directly to cryo-ET image segmentation tasks remains challenging due to two main issues: 1) the source dataset, usually obtained through simulation, contains a fixed level of noise, while the target dataset, directly collected from raw-data from the real-world scenario, have unpredictable noise levels. 2) the source data used for training typically consists of known macromoleculars. In contrast, the target domain data are often unknown, causing the model to be biased towards those known macromolecules, leading to a domain shift problem. To address such challenges, in this work, we introduce a voxel-wise unsupervised domain adaptation approach, termed Vox-UDA, specifically for cryo-ET subtomogram segmentation. Vox-UDA incorporates a noise generation module to simulate target-like noises in the source dataset for cross-noise level adaptation. Additionally, we propose a denoised pseudo-labeling strategy based on the improved Bilateral Filter to alleviate the domain shift problem. More importantly, we construct the first UDA cryo-ET subtomogram segmentation benchmark on three experimental datasets. Extensive experimental results on multiple benchmarks and newly curated real-world datasets demonstrate the superiority of our proposed approach compared to state-of-the-art UDA methods. 

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