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

The putative RNase P motif in the DEAD box helicase Hera is dispensable for efficient interaction with RNA and helicase activity

DEAD box helicases use the energy of ATP hydrolysis to remodel RNA structures or RNA/protein complexes. They share a common helicase core with conserved signature motifs, and additional domains may confer substrate specificity. Identification of a specific substrate is crucial towards understanding the physiological role of a helicase. RNA binding and ATPase stimulation are necessary, but not sufficient criteria for a bona fide helicase substrate. Here, we report single molecule FRET experiments that identify fragments of the 23S rRNA comprising hairpin 92 and RNase P RNA as substrates for the Thermus thermophilus DEAD box helicase Hera. Both substrates induce a switch to the closed conformation of the helicase core and stimulate the intrinsic ATPase activity of Hera. Binding of these RNAs is mediated by the Hera C-terminal domain, but does not require a previously proposed putative RNase P motif within this domain. ATP-dependent unwinding of a short helix adjacent to hairpin 92 in the ribosomal RNA suggests a specific role for Hera in ribosome assembly, analogously to the Escherichia coli and Bacillus subtilis helicases DbpA and YxiN. In addition, the specificity of Hera for RNase P RNA may be required for RNase P RNA folding or RNase P assembly

Phylogenetic Distribution and Evolutionary History of Bacterial DEAD-Box Proteins

DEAD-box proteins are found in all domains of life and participate in almost all cellular processes that involve RNA. The presence of DEAD and Helicase_C conserved domains distinguish these proteins. DEAD-box proteins exhibit RNA-dependent ATPase activity in vitro, and several also show RNA helicase activity. In this study, we analyzed the distribution and architecture of DEAD-box proteins among bacterial genomes to gain insight into the evolutionary pathways that have shaped their history. We identified 1,848 unique DEAD-box proteins from 563 bacterial genomes. Bacterial genomes can possess a single copy DEAD-box gene, or up to 12 copies of the gene, such as in Shewanella. The alignment of 1,208 sequences allowed us to perform a robust analysis of the hallmark motifs of DEAD-box proteins and determine the residues that occur at high frequency, some of which were previously overlooked. Bacterial DEAD-box proteins do not generally contain a conserved C-terminal domain, with the exception of some members that possess a DbpA RNA-binding domain (RBD). Phylogenetic analysis showed a separation of DbpA-RBD-containing and DbpA-RBD-lacking sequences and revealed a group of DEAD-box protein genes that expanded mainly in the Proteobacteria. Analysis of DEAD-box proteins from Firmicutes and γ-Proteobacteria, was used to deduce orthologous relationships of the well-studied DEAD-box proteins from Escherichia coli and Bacillus subtilis. These analyses suggest that DbpA-RBD is an ancestral domain that most likely emerged as a specialized domain of the RNA-dependent ATPases. Moreover, these data revealed numerous events of gene family expansion and reduction following speciation

Genome-wide imputation study identifies novel HLA locus for pulmonary fibrosis and potential role for auto-immunity in fibrotic idiopathic interstitial pneumonia

Fibrotic idiopathic interstitial pneumonias (fIIP) are a group of fatal lung diseases with largely unknown etiology and without definitive treatment other than lung transplant to prolong life. There is strong evidence for the importance of both rare and common genetic risk alleles in familial and sporadic disease. We have previously used genome-wide single nucleotide polymorphism data to identify 10 risk loci for fIIP. Here we extend that work to imputed genome-wide genotypes and conduct new RNA sequencing studies of lung tissue to identify and characterize new fIIP risk loci. Results: We performed genome-wide genotype imputation association analyses in 1616 non-Hispanic white (NHW) cases and 4683 NHW controls followed by validation and replication (878 cases, 2017 controls) genotyping and targeted gene expression in lung tissue. Following meta-analysis of the discovery and replication populations, we identified a novel fIIP locus in the HLA region of chromosome 6 (rs7887 Pmeta = 3.7 × 10-09). Imputation of classic HLA alleles identified two in high linkage disequilibrium that are associated with fIIP (DRB1 15:01 P = 1.3 × 10-7 and DQB1 06:02 P = 6.1 × 10-8). Targeted RNA-sequencing of the HLA locus identified 21 genes differentially expressed between fibrotic and control lung tissue (Q < 0.001), many of which are involved in immune and inflammatory response regulation. In addition, the putative risk alleles, DRB1 15:01 and DQB1 06:02, are associated with expression of the DQB1 gene among fIIP cases (Q < 1 × 10-16)

Genome-wide imputation study identifies novel HLA locus for pulmonary fibrosis and potential role for auto-immunity in fibrotic idiopathic interstitial pneumonia.

To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked Files. This article is open access.Fibrotic idiopathic interstitial pneumonias (fIIP) are a group of fatal lung diseases with largely unknown etiology and without definitive treatment other than lung transplant to prolong life. There is strong evidence for the importance of both rare and common genetic risk alleles in familial and sporadic disease. We have previously used genome-wide single nucleotide polymorphism data to identify 10 risk loci for fIIP. Here we extend that work to imputed genome-wide genotypes and conduct new RNA sequencing studies of lung tissue to identify and characterize new fIIP risk loci.We performed genome-wide genotype imputation association analyses in 1616 non-Hispanic white (NHW) cases and 4683 NHW controls followed by validation and replication (878 cases, 2017 controls) genotyping and targeted gene expression in lung tissue. Following meta-analysis of the discovery and replication populations, we identified a novel fIIP locus in the HLA region of chromosome 6 (rs7887 P meta  = 3.7 × 10(-09)). Imputation of classic HLA alleles identified two in high linkage disequilibrium that are associated with fIIP (DRB1*15:01 P = 1.3 × 10(-7) and DQB1*06:02 P = 6.1 × 10(-8)). Targeted RNA-sequencing of the HLA locus identified 21 genes differentially expressed between fibrotic and control lung tissue (Q < 0.001), many of which are involved in immune and inflammatory response regulation. In addition, the putative risk alleles, DRB1*15:01 and DQB1*06:02, are associated with expression of the DQB1 gene among fIIP cases (Q < 1 × 10(-16)).We have identified a genome-wide significant association between the HLA region and fIIP. Two HLA alleles are associated with fIIP and affect expression of HLA genes in lung tissue, indicating that the potential genetic risk due to HLA alleles may involve gene regulation in addition to altered protein structure. These studies reveal the importance of the HLA region for risk of fIIP and a basis for the potential etiologic role of auto-immunity in fIIP.National Heart, Lung and Blood Institute R01-HL095393 R01-HL097163 P01-HL092870 RC2-HL101715 U01-HL089897 U01-HL089856 U01-HL108642 P50-HL089493

Antifibrotic activities of pirfenidone in animal models

Pirfenidone is an orally active small molecule that has recently been evaluated in large clinical trials for the treatment of idiopathic pulmonary fibrosis, a fatal disease in which the uncontrolled deposition of extracellular matrix leads to progressive loss of lung function. This review describes the activity of pirfenidone in several well-characterised animal models of fibrosis in the lung, liver, heart and kidney. In these studies, treatment-related reductions in fibrosis are associated with modulation of cytokines and growth factors, with the most commonly reported effect being reduction of transforming growth factor-β. The consistent antifibrotic activity of pirfenidone in a broad array of animal models provides a strong preclinical rationale for the clinical characterisation of pirfenidone in pulmonary fibrosis and, potentially, other conditions with a significant fibrotic component

Evaluation of a Deep Learning Algorithm for Automated Spleen Segmentation in Patients with Conditions Directly or Indirectly Affecting the Spleen

The aim of this study was to develop a deep learning-based algorithm for fully automated spleen segmentation using CT images and to evaluate the performance in conditions directly or indirectly affecting the spleen (e.g., splenomegaly, ascites). For this, a 3D U-Net was trained on an in-house dataset (n = 61) including diseases with and without splenic involvement (in-house U-Net), and an open-source dataset from the Medical Segmentation Decathlon (open dataset, n = 61) without splenic abnormalities (open U-Net). Both datasets were split into a training (n = 32.52%), a validation (n = 9.15%) and a testing dataset (n = 20.33%). The segmentation performances of the two models were measured using four established metrics, including the Dice Similarity Coefficient (DSC). On the open test dataset, the in-house and open U-Net achieved a mean DSC of 0.906 and 0.897 respectively (p = 0.526). On the in-house test dataset, the in-house U-Net achieved a mean DSC of 0.941, whereas the open U-Net obtained a mean DSC of 0.648 (p < 0.001), showing very poor segmentation results in patients with abnormalities in or surrounding the spleen. Thus, for reliable, fully automated spleen segmentation in clinical routine, the training dataset of a deep learning-based algorithm should include conditions that directly or indirectly affect the spleen