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  • Journal article
    Zhou X, Ye Q, Jiang Y, Wang M, Niu Z, Menpes-Smith W, Fang EF, Liu Z, Xia J, Yang Get al., 2020,

    Systematic and comprehensive automated ventricle segmentation on ventricle images of the elderly patients: A retrospective study

    , Frontiers in Aging Neuroscience, Vol: 12, ISSN: 1663-4365

    Background and Objective: Ventricle volume is closely related to hydrocephalus, brain atrophy, Alzheimer's, Parkinson's syndrome, and other diseases. To accurately measure the volume of the ventricles for elderly patients, we use deep learning to establish a systematic and comprehensive automated ventricle segmentation framework.Methods: The study participation included 20 normal elderly people, 20 patients with cerebral atrophy, 64 patients with normal pressure hydrocephalus, and 51 patients with acquired hydrocephalus. Second, get their imaging data through the picture archiving and communication systems (PACS) system. Then use ITK software to manually label participants' ventricular structures. Finally, extract imaging features through machine learning.Results: This automated ventricle segmentation method can be applied not only to CT and MRI images but also to images with different scan slice thicknesses. More importantly, it produces excellent segmentation results (Dice > 0.9).Conclusion: This automated ventricle segmentation method has wide applicability and clinical practicability. It can help clinicians find early disease, diagnose disease, understand the patient's disease progression, and evaluate the patient's treatment effect.

  • Journal article
    Yuan Z, Jiang M, Wang Y, Wei B, Li Y, Wang P, Menpes-Smith W, Niu Z, Yang Get al., 2020,

    SARA-GAN: self-attention and relative average discriminator based generative adversarial networks for fast compressed sensing MRI reconstruction

    , Frontiers in Neuroinformatics, Vol: 14, Pages: 1-12, ISSN: 1662-5196

    Research on undersampled magnetic resonance image (MRI) reconstruction can increase the speed of MRI imaging and reduce patient suffering. In this paper, an undersampled MRI reconstruction method based on Generative Adversarial Networks with the Self-Attention mechanism and the Relative Average discriminator (SARA-GAN) is proposed. In our SARA-GAN, the relative average discriminator theory is applied to make full use of the prior knowledge, in which half of the input data of the discriminator is true and half is fake. At the same time, a self-attention mechanism is incorporated into the high-layer of the generator to build long-range dependence of the image, which can overcome the problem of limited convolution kernel size. Besides, spectral normalization is employed to stabilize the training process. Compared with three widely used GAN-based MRI reconstruction methods, i.e., DAGAN, DAWGAN, and DAWGAN-GP, the proposed method can obtain a higher peak signal-to-noise ratio (PSNR) and structural similarity index measure(SSIM), and the details of the reconstructed image are more abundant and more realistic for further clinical scrutinization and diagnostic tasks.

  • Journal article
    Ferreira PF, Martin RR, Scott AD, Khalique Z, Yang G, Nielles-Vallespin S, Pennell DJ, Firmin DNet al., 2020,

    Automating in vivo cardiac diffusion tensor postprocessing with deep learning-based segmentation

    , Magnetic Resonance in Medicine, Vol: 84, Pages: 2801-2814, ISSN: 0740-3194

    PurposeIn this work we develop and validate a fully automated postprocessing framework for in vivo diffusion tensor cardiac magnetic resonance (DT‐CMR) data powered by deep learning.MethodsA U‐Net based convolutional neural network was developed and trained to segment the heart in short‐axis DT‐CMR images. This was used as the basis to automate and enhance several stages of the DT‐CMR tensor calculation workflow, including image registration and removal of data corrupted with artifacts, and to segment the left ventricle. Previously collected and analyzed scans (348 healthy scans and 144 cardiomyopathy patient scans) were used to train and validate the U‐Net. All data were acquired at 3 T with a STEAM‐EPI sequence. The DT‐CMR postprocessing and U‐Net training/testing were performed with MATLAB and Python TensorFlow, respectively.ResultsThe U‐Net achieved a median Dice coefficient of 0.93 [0.92, 0.94] for the segmentation of the left‐ventricular myocardial region. The image registration of diffusion images improved with the U‐Net segmentation (P < .0001), and the identification of corrupted images achieved an F1 score of 0.70 when compared with an experienced user. Finally, the resulting tensor measures showed good agreement between an experienced user and the fully automated method.ConclusionThe trained U‐Net successfully automated the DT‐CMR postprocessing, supporting real‐time results and reducing human workload. The automatic segmentation of the heart improved image registration, resulting in improvements of the calculated DT parameters.

  • Journal article
    He W-J, Zhou X, Long J, Xu Q-Z, Huang X-J, Jiang J, Xia J, Yang Get al., 2020,

    Idiopathic normal pressure hydrocephalus and elderly acquired hydrocephalus: evaluation with cerebrospinal fluid flow and ventricular volume parameters

    , Frontiers in Aging Neuroscience, Vol: 12, ISSN: 1663-4365

    Purpose: To investigate differences in cerebrospinal fluid (CSF) flow through the aqueduct and to determine whether there is a relationship between CSF flow and ventricular volume parameters in idiopathic normal pressure hydrocephalus (iNPH) patients, elderly acquired hydrocephalus patients and age-matched healthy volunteers by phase-contrast MR (PC-MR).Methods: A total of 40 iNPH patients and 41 elderly acquired hydrocephalus patients and 26 age-matched healthy volunteers in the normal control (NC) group were included between November 2017 and October 2019 in this retrospective study. The following CSF flow parameters were measured with PC-MR: peak velocity, average velocity (AV), aqueductal stroke volume (ASV), net ASV, and net flow. The following ventricular volume parameters were measured: ventricular volume (VV), brain volume, total intracranial volume, and relative VV. Differences between the iNPH and acquired hydrocephalus groups were compared Mann–Whitney U test and correlations between CSF flow and ventricular volume parameters were assessed using the Spearman correlation coefficient.Results: Aqueductal stroke volume was significantly higher in the iNPH and acquired hydrocephalus groups than in the NC group, but did not differ significantly between the iNPH group and acquired hydrocephalus group. The AV, net ASV, and net flow in the iNPH and acquired hydrocephalus groups were significantly higher than those in the NC group (P < 0.0001), and those in the acquired hydrocephalus group were significantly higher than those in the iNPH group (P = 0.01, P = 0.007, P = 0.002, respectively). The direction of the AV and net ASV significantly differed among the three groups. There were no associations between the volume parameters and CSF flow according to PC-MR among the three groups.Conclusion: Compared with iNPH, elderly acquired hydrocephalus demonstrated higher CSF hyperdynamic flow. Although increased CSF flow may contribute to further changes in ventri

  • Journal article
    Long J, Sun D, Zhou X, Huang X, Hu J, Xia J, Yang Get al., 2020,

    A mathematical model for predicting intracranial pressure based on noninvasively acquired PC-MRI parameters in communicating hydrocephalus

    , JOURNAL OF CLINICAL MONITORING AND COMPUTING, Vol: 35, Pages: 1325-1332, ISSN: 1387-1307
  • Conference paper
    Guo Y, Wang C, Zhang H, Yang Get al., 2020,

    Deep attentive wasserstein generative adversarial networks for MRI reconstruction with recurrent context-awareness

    , International Conference on Medical Image Computing and Computer-Assisted Intervention, Publisher: Springer International Publishing, Pages: 167-177, ISSN: 0302-9743

    The performance of traditional compressive sensing-based MRI (CS-MRI) reconstruction is affected by its slow iterative procedure and noise-induced artefacts. Although many deep learning-based CS-MRI methods have been proposed to mitigate the problems of traditional methods, they have not been able to achieve more robust results at higher acceleration factors. Most of the deep learning-based CS-MRI methods still can not fully mine the information from the k-space, which leads to unsatisfactory results in the MRI reconstruction. In this study, we propose a new deep learning-based CS-MRI reconstruction method to fully utilise the relationship among sequential MRI slices by coupling Wasserstein Generative Adversarial Networks (WGAN) with Recurrent Neural Networks. Further development of an attentive unit enables our model to reconstruct more accurate anatomical structures for the MRI data. By experimenting on different MRI datasets, we have demonstrated that our method can not only achieve better results compared to the state-of-the-arts but can also effectively reduce residual noise generated during the reconstruction process.

  • Journal article
    Seneviratne A, Han Y, Wong E, Walter E, Jiang L, Cave L, Long NJ, Carling D, Mason JC, Haskard DO, Boyle Jet al., 2020,

    Hematoma resolution in vivo is directed by Activating Transcription Factor 1

    , Circulation Research, Vol: 127, Pages: 928-944, ISSN: 0009-7330

    Rationale: The efficient resolution of tissue hemorrhage is an important homeostatic function. In human macrophages in vitro, heme activates an adenosine monophosphate activated protein kinase / activating transcription factor 1 (AMPK/ATF1) pathway that directs Mhem macrophages through coregulation of heme oxygenase 1 (HMOX1, HO-1) and lipid homeostasis genes.Objective: We asked whether this pathway had an in vivo role in mice.Methods and Results: Perifemoral hematomas were used as a model of hematoma resolution. In mouse bone marrow derived macrophages (mBMM), heme induced HO-1, lipid regulatory genes including LXR, the growth factor IGF1, and the splenic red pulp macrophage gene Spic. This response was lost in mBMM from mice deficient in AMPK (Prkab1-/-) or ATF1 (Atf1-/-). In vivo, femoral hematomas resolved completely between day 8 and day 9 in littermate control mice (n=12), but were still present at day 9 in mice deficient in either AMPK (Prkab1-/-) or ATF1 (Atf1-/-) (n=6 each). Residual hematomas were accompanied by increased macrophage infiltration, inflammatory activation and oxidative stress. We also found that fluorescent lipids and a fluorescent iron-analog were trafficked to lipid-laden and iron-laden macrophages respectively. Moreover erythrocyte iron and lipid abnormally colocalized in the same macrophages in Atf1-/- mice. Therefore, iron-lipid separation was Atf1-dependent.Conclusions: Taken together, these data demonstrate that both AMPK and ATF1 are required for normal hematoma resolution.

  • Journal article
    Smith RM, Jones RB, Specks U, Bond S, Nodale M, Aljayyousi R, Andrews J, Bruchfeld A, Camilleri B, Carette S, Cheung CK, Derebail V, Doulton T, Forbess L, Fujimoto S, Furuta S, Gewurz-Singer O, Harper L, Ito-Ihara T, Khalidi N, Klocke R, Koening C, Komagata Y, Langford C, Lanyon P, Luqmani RA, Makino H, McAlear C, Monach P, Moreland LW, Mynard K, Nachman P, Pagnoux C, Pearce F, Peh CA, Pusey C, Ranganathan D, Rhee RL, Spiera R, Sreih AG, Tesar V, Walters G, Weisman MH, Wroe C, Merkel P, Jayne Det al., 2020,

    Rituximab as therapy to induce remission after relapse in ANCA-associated vasculitis

    , ANNALS OF THE RHEUMATIC DISEASES, Vol: 79, Pages: 1243-1249, ISSN: 0003-4967
  • Journal article
    Hao J, Wang C, Zhang H, Yang Get al., 2020,

    Annealing Genetic GAN for Minority Oversampling

    The key to overcome class imbalance problems is to capture the distributionof minority class accurately. Generative Adversarial Networks (GANs) have shownsome potentials to tackle class imbalance problems due to their capability ofreproducing data distributions given ample training data samples. However, thescarce samples of one or more classes still pose a great challenge for GANs tolearn accurate distributions for the minority classes. In this work, we proposean Annealing Genetic GAN (AGGAN) method, which aims to reproduce thedistributions closest to the ones of the minority classes using only limiteddata samples. Our AGGAN renovates the training of GANs as an evolutionaryprocess that incorporates the mechanism of simulated annealing. In particular,the generator uses different training strategies to generate multiple offspringand retain the best. Then, we use the Metropolis criterion in the simulatedannealing to decide whether we should update the best offspring for thegenerator. As the Metropolis criterion allows a certain chance to accept theworse solutions, it enables our AGGAN steering away from the local optimum.According to both theoretical analysis and experimental studies on multipleimbalanced image datasets, we prove that the proposed training strategy canenable our AGGAN to reproduce the distributions of minority classes from scarcesamples and provide an effective and robust solution for the class imbalanceproblem.

  • Journal article
    Yang M, Xiao X, Liu Z, Sun L, Guo W, Cui L, Sun D, Zhang P, Yang Get al., 2020,

    Deep RetinaNet for Dynamic Left Ventricle Detection in Multiview Echocardiography Classification

    , SCIENTIFIC PROGRAMMING, Vol: 2020, ISSN: 1058-9244

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