Generating 3D TOF-MRA volumes and segmentation labels using generative adversarial networks

Deep learning requires large labeled datasets that are difficult to gather in medical imaging due to data privacy issues and time-consuming manual labeling. Generative Adversarial Networks (GANs) can alleviate these challenges enabling synthesis of shareable data. While 2D GANs have been used to generate 2D images with their corresponding labels, they cannot capture the volumetric information of 3D medical imaging. 3D GANs are more suitable for this and have been used to generate 3D volumes but not their corresponding labels. One reason might be that synthesizing 3D volumes is challenging owing to computational limitations. In this work, we present 3D GANs for the generation of 3D medical image volumes with corresponding labels applying mixed precision to alleviate computational constraints. We generated 3D Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) patches with their corresponding brain blood vessel segmentation labels. We used four variants of 3D Wasserstein GAN (WGAN) with: 1) gradient penalty (GP), 2) GP with spectral normalization (SN), 3) SN with mixed precision (SN-MP), and 4) SN-MP with double filters per layer (c-SN-MP). The generated patches were quantitatively evaluated using the Fréchet Inception Distance (FID) and Precision and Recall of Distributions (PRD). Further, 3D U-Nets were trained with patch-label pairs from different WGAN models and their performance was compared to the performance of a benchmark U-Net trained on real data. The segmentation performance of all U-Net models was assessed using Dice Similarity Coefficient (DSC) and balanced Average Hausdorff Distance (bAVD) for a) all vessels, and b) intracranial vessels only. Our results show that patches generated with WGAN models using mixed precision (SN-MP and c-SN-MP) yielded the lowest FID scores and the best PRD curves. Among the 3D U-Nets trained with synthetic patch-label pairs, c-SN-MP pairs achieved the highest DSC (0.841) and lowest bAVD (0.508) compared to the benchmark U-Net trained on real data (DSC 0.901; bAVD 0.294) for intracranial vessels. In conclusion, our solution generates realistic 3D TOF-MRA patches and labels for brain vessel segmentation. We demonstrate the benefit of using mixed precision for computational efficiency resulting in the best-performing GAN-architecture. Our work paves the way towards sharing of labeled 3D medical data which would increase generalizability of deep learning models for clinical use

Toward sharing brain images: Differentially private TOF-MRA images with segmentation labels using generative adversarial networks

Sharing labeled data is crucial to acquire large datasets for various Deep Learning applications. In medical imaging, this is often not feasible due to privacy regulations. Whereas anonymization would be a solution, standard techniques have been shown to be partially reversible. Here, synthetic data using a Generative Adversarial Network (GAN) with differential privacy guarantees could be a solution to ensure the patient's privacy while maintaining the predictive properties of the data. In this study, we implemented a Wasserstein GAN (WGAN) with and without differential privacy guarantees to generate privacy-preserving labeled Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) image patches for brain vessel segmentation. The synthesized image-label pairs were used to train a U-net which was evaluated in terms of the segmentation performance on real patient images from two different datasets. Additionally, the Fréchet Inception Distance (FID) was calculated between the generated images and the real images to assess their similarity. During the evaluation using the U-Net and the FID, we explored the effect of different levels of privacy which was represented by the parameter ϵ. With stricter privacy guarantees, the segmentation performance and the similarity to the real patient images in terms of FID decreased. Our best segmentation model, trained on synthetic and private data, achieved a Dice Similarity Coefficient (DSC) of 0.75 for ϵ = 7.4 compared to 0.84 for ϵ = ∞ in a brain vessel segmentation paradigm (DSC of 0.69 and 0.88 on the second test set, respectively). We identified a threshold of ϵ <5 for which the performance (DSC <0.61) became unstable and not usable. Our synthesized labeled TOF-MRA images with strict privacy guarantees retained predictive properties necessary for segmenting the brain vessels. Although further research is warranted regarding generalizability to other imaging modalities and performance improvement, our results mark an encouraging first step for privacy-preserving data sharing in medical imaging

A dominant negative mutant of the E. coli RNA helicase DbpA blocks assembly of the 50S ribosomal subunit

Escherichia coli DbpA is an ATP-dependent RNA helicase with specificity for hairpin 92 of 23S ribosomal RNA, an important part of the peptidyl transferase center. The R331A active site mutant of DbpA confers a dominant slow growth and cold sensitive phenotype when overexpressed in E. coli containing endogenous DbpA. Ribosome profiles from cells overexpressing DbpA R331A display increased levels of 50S and 30S subunits and decreased levels 70S ribosomes. Profiles run at low Mg2+ exhibit fewer 50S subunits and accumulate a 45S particle that contains incompletely processed and undermodified 23S rRNA in addition to reduced levels of several ribosomal proteins that bind late in the assembly pathway. Unlike mature 50S subunits, these 45S particles can stimulate the ATPase activity of DbpA, indicating that hairpin 92 has not yet been sequestered within the 50S subunit. Overexpression of the inactive DbpA R331A mutant appears to block assembly at a late stage when the peptidyl transferase center is formed, indicating a possible role for DbpA promoting this conformational change

Synthesizing anonymized and labeled TOF-MRA patches for brain vessel segmentation using generative adversarial networks

Anonymization and data sharing are crucial for privacy protection and acquisition of large datasets for medical image analysis. This is a big challenge, especially for neuroimaging. Here, the brain's unique structure allows for re-identification and thus requires non-conventional anonymization. Generative adversarial networks (GANs) have the potential to provide anonymous images while preserving predictive properties. Analyzing brain vessel segmentation, we trained 3 GANs on time-of-flight (TOF) magnetic resonance angiography (MRA) patches for image-label generation: 1) Deep convolutional GAN, 2) Wasserstein-GAN with gradient penalty (WGAN-GP) and 3) WGAN-GP with spectral normalization (WGAN-GP-SN). The generated image-labels from each GAN were used to train a U-net for segmentation and tested on real data. Moreover, we applied our synthetic patches using transfer learning on a second dataset. For an increasing number of up to 15 patients we evaluated the model performance on real data with and without pre-training. The performance for all models was assessed by the Dice Similarity Coefficient (DSC) and the 95th percentile of the Hausdorff Distance (95HD). Comparing the 3 GANs, the U-net trained on synthetic data generated by the WGAN-GP-SN showed the highest performance to predict vessels (DSC/95HD 0.85/30.00) benchmarked by the U-net trained on real data (0.89/26.57). The transfer learning approach showed superior performance for the same GAN compared to no pre-training, especially for one patient only (0.91/24.66 vs. 0.84/27.36). In this work, synthetic image-label pairs retained generalizable information and showed good performance for vessel segmentation. Besides, we showed that synthetic patches can be used in a transfer learning approach with independent data. This paves the way to overcome the challenges of scarce data and anonymization in medical imaging

High prevalence of adverse prognostic genetic aberrations and unmutated IGHV genes in small lymphocytic lymphoma as compared to chronic lymphocytic leukemia.

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DEAD-Box RNA Helicases in Bacillus subtilis Have Multiple Functions and Act Independently from Each Other

<p>DEAD-box RNA helicases play important roles in remodeling RNA molecules and in facilitating a variety of RNA-protein interactions that are key to many essential cellular processes. In spite of the importance of RNA, our knowledge about RNA helicases is limited. In this study, we investigated the role of the four DEAD-box RNA helicases in the Gram-positive model organism Bacillus subtilis. A strain deleted of all RNA helicases is able to grow at 37 degrees C but not at lower temperatures. The deletion of cshA, cshB, or yfmL in particular leads to cold-sensitive phenotypes. Moreover, these mutant strains exhibit unique defects in ribosome biogenesis, suggesting distinct functions for the individual enzymes in this process. Based on protein accumulation, severity of the cold-sensitive phenotype, and the interaction with components of the RNA degradosome, CshA is the major RNA helicase of B. subtilis. To unravel the functions of CshA in addition to ribosome biogenesis, we conducted microarray analysis and identified the ysbAB and frlBONMD mRNAs as targets that are strongly affected by the deletion of the cshA gene. Our findings suggest that the different helicases make distinct contributions to the physiology of B. subtilis. Ribosome biogenesis and RNA degradation are two of their major tasks in B. subtilis.</p>