Deep Learning Based Supervised Semantic Segmentation of Electron Cryo-Subtomograms

Chang, Liu; Zeng, Xiangrui; Lin, Ruogu; Liang, Xiaodan; Freyberg, Zachary; Xing, Eric P.; Xu, Min

doi:10.1109/icip.2018.8451386

Cited by 12 publications

(13 citation statements)

References 25 publications

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“…This encoder-decoder variant of the FCN adds skip connections in the upsampling phase that combines the higher layers with the lower pooling layers to fully utilize global context information while integrating the local information to accurately map low-resolution images to voxel-wise predictions. In a previously published paper, it is found that this model has consistently outperformed other similar variants of FCNs for the segmentation task [7]. Our simulation provides 3D simulated cryo-ET data that can serve as the training data for EDSSN3D network and reduce the amount of training data preparation required of this model and thus successfully break the originally thought trade-off between accuracy and data preparation.…”

Section: Test Of Semantic Segmentation Methods Using Fully Convolutionmentioning

confidence: 65%

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A unified framework for packing deformable and non-deformable subcellular structures in crowded cryo-electron tomogram simulation

et al. 2020

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Background Cryo-electron tomography is an important and powerful technique to explore the structure, abundance, and location of ultrastructure in a near-native state. It contains detailed information of all macromolecular complexes in a sample cell. However, due to the compact and crowded status, the missing edge effect, and low signal to noise ratio (SNR), it is extremely challenging to recover such information with existing image processing methods. Cryo-electron tomogram simulation is an effective solution to test and optimize the performance of the above image processing methods. The simulated images could be regarded as the labeled data which covers a wide range of macromolecular complexes and ultrastructure. To approximate the crowded cellular environment, it is very important to pack these heterogeneous structures as tightly as possible. Besides, simulating non-deformable and deformable components under a unified framework also need to be achieved. Result In this paper, we proposed a unified framework for simulating crowded cryo-electron tomogram images including non-deformable macromolecular complexes and deformable ultrastructures. A macromolecule was approximated using multiple balls with fixed relative positions to reduce the vacuum volume. A ultrastructure, such as membrane and filament, was approximated using multiple balls with flexible relative positions so that this structure could deform under force field. In the experiment, 400 macromolecules of 20 representative types were packed into simulated cytoplasm by our framework, and numerical verification proved that our method has a smaller volume and higher compression ratio than the baseline single-ball model. We also packed filaments, membranes and macromolecules together, to obtain a simulated cryo-electron tomogram image with deformable structures. The simulated results are closer to the real Cryo-ET, making the analysis more difficult. The DOG particle picking method and the image segmentation method are tested on our simulation data, and the experimental results show that these methods still have much room for improvement. Conclusion The proposed multi-ball model can achieve more crowded packaging results and contains richer elements with different properties to obtain more realistic cryo-electron tomogram simulation. This enables users to simulate cryo-electron tomogram images with non-deformable macromolecular complexes and deformable ultrastructures under a unified framework. To illustrate the advantages of our framework in improving the compression ratio, we calculated the volume of simulated macromolecular under our multi-ball method and traditional single-ball method. We also performed the packing experiment of filaments and membranes to demonstrate the simulation ability of deformable structures. Our method can be used to do a benchmark by generating large labeled cryo-ET dataset and evaluating existing image processing methods. Since the content of the simulated cryo-ET is more complex and crowded compared with previous ones, it will pose a greater challenge to existing image processing methods.

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Section: Test Of Semantic Segmentation Methods Using Fully Convolutionmentioning

confidence: 65%

“…In the fields of protein visualization and structural biology, various machine learning methods are applied to the analyse the structure of macromolecules and ultrastructures [5][6][7]. These methods can resolve the structure of macromolecules to a large extent, but the accuracy still need to be improved.…”

Section: Introductionmentioning

confidence: 99%

A unified framework for packing deformable and non-deformable subcellular structures in crowded cryo-electron tomogram simulation

et al. 2020

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“…This network design allows the training of the three tasks to mutually reinforce each other for better feature extraction and therefore higher accuracy. The accuracy of this model for classification and semantic segmentation outperformed single-task models (10,11).…”

Section: Simultaneous Classification and Segmentation By Multi-task Lmentioning

confidence: 93%

“…Two 3D semantic segmentation convolutional neural networks (SSN3D) and their variants segmenting the main structural region from subtomograms have been proposed (10). This is a very useful step in subtomogram analysis because masking out neighboring structures can significantly reduce the structural bias for further analysis such as averaging and classification.…”

Section: Semantic Segmentation Using Convolutional Neural Networkmentioning

confidence: 99%

Template-Based and Template-Free Approaches in Cellular Cryo-Electron Tomography Structural Pattern Mining

Zeng

Zhu

et al. 2019

Computational Biology

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Cryo-electron tomography (Cryo-ET) has made possible the observation of cellular organelles and macromolecular complexes at nanometer resolution in native conformations. Without disrupting the cell, Cryo-ET directly visualizes both known and unknown structures in situ and reveals their spatial and organizational relationships. Consequently, structural pattern mining (a.k.a. visual proteomics) needs to be performed to detect, identify and recover different sub-cellular components and their spatial organization in a systematic fashion for further biomedical analysis and interpretation. This chapter presents three major Cryo-ET structural pattern mining approaches to give an overview of traditional methods and recent advances in Cryo-ET data analysis. Template-based, supervised deep learning-based and template-free approaches are introduced in detail. Examples of recent biological and medical applications and future perspectives are provided.

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“…CNN [17] are well-known for extracting features from a image by using convolutional kernels and pooling layers to emulates the response of an individual to visual stimuli. This work [20] is the first application of deep learning for systematic structural discovery of macromolecular complexes among large amount (millions) of structurally highly heterogeneous particles captured by Cryo-ET. It represents an important step towards large scale systematic detection of native structures and spatial organizations of large macromolecular complexes inside single cells.…”

Section: A Cryo-electron Tomography (Cryo-et)mentioning

confidence: 99%

Deep Learning-Based Strategy For Macromolecules Classification with Imbalanced Data from Cellular Electron Cryotomography

Luo

Zeng

Bao

et al. 2019

2019 International Joint Conference on Neural Networks (IJCNN)

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Deep learning model trained by imbalanced data may not work satisfactorily since it could be determined by major classes and thus may ignore the classes with small amount of data. In this paper, we apply deep learning based imbalanced data classification for the first time to cellular macromolecular complexes captured by Cryo-electron tomography (Cryo-ET). We adopt a range of strategies to cope with imbalanced data, including data sampling, bagging, boosting, Genetic Programming based method and. Particularly, inspired from Inception 3D network, we propose a multi-path CNN model combining focal loss and mixup on the Cryo-ET dataset to expand the dataset, where each path had its best performance corresponding to each type of data and let the network learn the combinations of the paths to improve the classification performance. In addition, extensive experiments have been conducted to show our proposed method is flexible enough to cope with different number of classes by adjusting the number of paths in our multi-path model. To our knowledge, this work is the first application of deep learning methods of dealing with imbalanced data to the internal tissue classification of cell macromolecular complexes, which opened up a new path for cell classification in the field of computational biology.

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Deep Learning Based Supervised Semantic Segmentation of Electron Cryo-Subtomograms

Cited by 12 publications

References 25 publications

A unified framework for packing deformable and non-deformable subcellular structures in crowded cryo-electron tomogram simulation

A unified framework for packing deformable and non-deformable subcellular structures in crowded cryo-electron tomogram simulation

Template-Based and Template-Free Approaches in Cellular Cryo-Electron Tomography Structural Pattern Mining

Deep Learning-Based Strategy For Macromolecules Classification with Imbalanced Data from Cellular Electron Cryotomography

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