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  • Journal article
    Liu Y, Yang G, Afshari Mirak S, Hosseiny M, Azadikhah A, Zhong X, Reiter RE, Lee Y, Raman SS, Sung Ket al., 2019,

    Automatic Prostate Zonal Segmentation Using Fully Convolutional Network With Feature Pyramid Attention

    , IEEE ACCESS, Vol: 7, Pages: 163626-163632, ISSN: 2169-3536
  • Conference paper
    Zhu J, Yang G, Lio P, 2019,


    , 16th IEEE International Symposium on Biomedical Imaging (ISBI), Publisher: IEEE, Pages: 1669-1673, ISSN: 1945-7928
  • Journal article
    Dooley D, van Timmeren MM, O'Reilly VP, Brady G, O'Brien EC, Fazekas B, Hickey FB, Leacy E, Pusey CD, Tam FWK, Mehrling T, Heeringa P, Little MAet al., 2018,

    Alkylating histone deacetylase inhibitors may have therapeutic value in experimental myeloperoxidase-ANCA vasculitis

    , Kidney International, Vol: 94, Pages: 926-936, ISSN: 0085-2538

    Current therapies for treating antineutrophil cytoplasm autoantibody (ANCA)–associated vasculitis include cyclophosphamide and corticosteroids. Unfortunately, these agents are associated with severe adverse effects, despite inducing remission in most patients. Histone deacetylase inhibitors are effective in rodent models of inflammation and act synergistically with many pharmacological agents, including alkylating agents like cyclophosphamide. EDO-S101 is an alkylating fusion histone deacetylase inhibitor molecule combining the DNA alkylating effect of Bendamustine with a pan-histone deacetylase inhibitor, Vorinostat. Here we studied the effects of EDO-S101 in two established rodent models of ANCA-associated vasculitis: a passive mouse model of anti-myeloperoxidase IgG-induced glomerulonephritis and an active rat model of myeloperoxidase-ANCA microscopic polyangiitis. Although pretreatment with EDO-S101 reduced circulating leukocytes, it did not prevent the development of passive IgG-induced glomerulonephritis in mice. On the other hand, treatment in rats significantly reduced glomerulonephritis and lung hemorrhage. EDO-S101 also significantly depleted rat B and T cells, and induced DNA damage and apoptosis in proliferating human B cells, suggesting a selective effect on the adaptive immune response. Thus, EDO-S101 may have a role in treatment of ANCA-associated vasculitis, operating primarily through its effects on the adaptive immune response to the autoantigen myeloperoxidase.

  • Journal article
    Tombetti E, Godi C, Ambrosi A, Doyle F, Jacobs A, Kiprianos AP, Youngstein T, Bechman K, Manfredi AA, Ariff B, Mason Jet al., 2018,

    Novel angiographic scores for evaluation of large vessel vasculitis

    , Scientific Reports, Vol: 8, ISSN: 2045-2322

    Arterial involvement is the cardinal feature of large-vessel vasculitis (LVV) and prevention of disease progression is the principal therapeutic goal. However, development of tools for its evaluation represents a major unmet need. To address this, a widely-applicable imaging tool for LVV, analysing arterial involvement in 17 arterial territories, has been developed and validated. Individual stenosis and dilation scores were generated and combined in a composite score. The methodology was validated cross-sectionally and longitudinally in 131 patients, 96 Takayasu arteritis (TA), 35 large-vessel giant-cell arteritis (LV-GCA). In total, 4420 arterial segments from 260 imaging studies were evaluated. The new scores allowed quantitative grading of LVV arterial involvement with high consistency, revealing inter-patient differences. TA had higher stenosis and composite scores and lower dilation scores than LV-GCA. Baseline stenotic and composite scores reflected arterial damage rather than disease-activity. Longitudinal changes in all three scores correlated with disease activity and mirrored arterial disease evolution, reflecting both progressive injury and lesion improvement. Increases ≥1 in any score were specific for arterial disease progression. The scores objectively quantify arterial involvement in LVV, providing precise definition of disease phenotype and evolution. We propose that they represent novel vascular outcome measures essential for future clinical trials.

  • Conference paper
    Yang G, Chen J, Gao Z, Zhang H, Ni H, Angelini E, Mohiaddin R, Wong T, Keegan J, Firmin Det al., 2018,

    Multiview sequential learning and dilated residual learning for a fully automatic delineation of the left atrium and pulmonary veins from late gadolinium-enhanced cardiac MRI images

    , 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Publisher: IEEE, Pages: 1123-1127, ISSN: 1557-170X

    Accurate delineation of heart substructures is a prerequisite for abnormality detection, for making quantitative and functional measurements, and for computer-aided diagnosis and treatment planning. Late Gadolinium-Enhanced Cardiac MRI (LGE-CMRI) is an emerging imaging technology for myocardial infarction or scar detection based on the differences in the volume of residual gadolinium distribution between scar and healthy tissues. While LGE-CMRI is a well-established non-invasive tool for detecting myocardial scar tissues in the ventricles, its application to left atrium (LA) imaging is more challenging due to its very thin wall of the LA and poor quality images, which may be produced because of motion artefacts and low signal-to-noise ratio. As the LGE-CMRI scan is designed to highlight scar tissues by altering the gadolinium kinetics, the anatomy among different heart substructures has less distinguishable boundaries. An accurate, robust and reproducible method for LA segmentation is highly in demand because it can not only provide valuable information of the heart function but also be helpful for the further delineation of scar tissue and measuring the scar percentage. In this study, we proposed a novel deep learning framework working on LGE-CMRI images directly by combining sequential learning and dilated residual learning to delineate LA and pulmonary veins fully automatically. The achieved results showed accurate segmentation results compared to the state-of-the-art methods. The proposed framework leads to an automatic generation of a patient-specific model that can potentially enable an objective atrial scarring assessment for the atrial fibrillation patients.

  • Journal article
    Schlemper J, Yang G, Ferreira P, Scott A, McGill LA, Khalique Z, Gorodezky M, Roehl M, Keegan J, Pennell D, Firmin D, Rueckert Det al., 2018,

    Stochastic deep compressive sensing for the reconstruction of diffusion tensor cardiac MRI

    , Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol: 11070 LNCS, Pages: 295-303, ISSN: 0302-9743

    © Springer Nature Switzerland AG 2018. Understanding the structure of the heart at the microscopic scale of cardiomyocytes and their aggregates provides new insights into the mechanisms of heart disease and enables the investigation of effective therapeutics. Diffusion Tensor Cardiac Magnetic Resonance (DT-CMR) is a unique non-invasive technique that can resolve the microscopic structure, organisation, and integrity of the myocardium without the need for exogenous contrast agents. However, this technique suffers from relatively low signal-to-noise ratio (SNR) and frequent signal loss due to respiratory and cardiac motion. Current DT-CMR techniques rely on acquiring and averaging multiple signal acquisitions to improve the SNR. Moreover, in order to mitigate the influence of respiratory movement, patients are required to perform many breath holds which results in prolonged acquisition durations (e.g., ~ 30 min using the existing technology). In this study, we propose a novel cascaded Convolutional Neural Networks (CNN) based compressive sensing (CS) technique and explore its applicability to improve DT-CMR acquisitions. Our simulation based studies have achieved high reconstruction fidelity and good agreement between DT-CMR parameters obtained with the proposed reconstruction and fully sampled ground truth. When compared to other state-of-the-art methods, our proposed deep cascaded CNN method and its stochastic variation demonstrated significant improvements. To the best of our knowledge, this is the first study using deep CNN based CS for the DT-CMR reconstruction. In addition, with relatively straightforward modifications to the acquisition scheme, our method can easily be translated into a method for online, at-the-scanner reconstruction enabling the deployment of accelerated DT-CMR in various clinical applications.

  • Conference paper
    Chen J, Yang G, Gao Z, Ni H, Angelini E, Mohiaddin R, Wong T, Zhang Y, Du X, Zhang H, Keegan J, Firmin Det al., 2018,

    Multiview two-task recursive attention model for left atrium and atrial scars segmentation

    , Medical Image Computing and Computer Assisted Intervention – MICCAI 2018, Publisher: Springer, Pages: 455-463, ISSN: 0302-9743

    Late Gadolinium Enhanced Cardiac MRI (LGE-CMRI) for detecting atrial scars in atrial fibrillation (AF) patients has recently emerged as a promising technique to stratify patients, guide ablation therapy and predict treatment success. Visualisation and quantification of scar tissues require a segmentation of both the left atrium (LA) and the high intensity scar regions from LGE-CMRI images. These two segmentation tasks are challenging due to the cancelling of healthy tissue signal, low signal-to-noise ratio and often limited image quality in these patients. Most approaches require manual supervision and/or a second bright-blood MRI acquisition for anatomical segmentation. Segmenting both the LA anatomy and the scar tissues automatically from a single LGE-CMRI acquisition is highly in demand. In this study, we proposed a novel fully automated multiview two-task (MVTT) recursive attention model working directly on LGE-CMRI images that combines a sequential learning and a dilated residual learning to segment the LA (including attached pulmonary veins) and delineate the atrial scars simultaneously via an innovative attention model. Compared to other state-of-the-art methods, the proposed MVTT achieves compelling improvement, enabling to generate a patient-specific anatomical and atrial scar assessment model.

  • Conference paper
    Shi Z, Zeng G, Zhang L, Zhuang X, Li L, Yang G, Zheng Get al., 2018,

    Bayesian VoxDRN: A Probabilistic Deep Voxelwise Dilated Residual Network for Whole Heart Segmentation from 3D MR Images

    , 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI2018), Pages: 569-577, ISSN: 0302-9743

    © 2018, Springer Nature Switzerland AG. In this paper, we propose a probabilistic deep voxelwise dilated residual network, referred as Bayesian VoxDRN, to segment the whole heart from 3D MR images. Bayesian VoxDRN can predict voxelwise class labels with a measure of model uncertainty, which is achieved by a dropout-based Monte Carlo sampling during testing to generate a posterior distribution of the voxel class labels. Our method has three compelling advantages. First, the dropout mechanism encourages the model to learn a distribution of weights with better data-explanation ability and prevents over-fitting. Second, focal loss and Dice loss are well encapsulated into a complementary learning objective to segment both hard and easy classes. Third, an iterative switch training strategy is introduced to alternatively optimize a binary segmentation task and a multi-class segmentation task for a further accuracy improvement. Experiments on the MICCAI 2017 multi-modality whole heart segmentation challenge data corroborate the effectiveness of the proposed method.

  • Conference paper
    Wu F, Li L, Yang G, Wong T, Mohiaddin R, Firmin D, Keegan J, Xu L, Zhuang Xet al., 2018,

    Atrial Fibrosis Quantification Based on Maximum Likelihood Estimator of Multivariate Images

    , 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI2018), Pages: 604-612, ISSN: 0302-9743

    © 2018, Springer Nature Switzerland AG. We present a fully-automated segmentation and quantification of the left atrial (LA) fibrosis and scars combining two cardiac MRIs, one is the target late gadolinium-enhanced (LGE) image, and the other is an anatomical MRI from the same acquisition session. We formulate the joint distribution of images using a multivariate mixture model (MvMM), and employ the maximum likelihood estimator (MLE) for texture classification of the images simultaneously. The MvMM can also embed transformations assigned to the images to correct the misregistration. The iterated conditional mode algorithm is adopted for optimization. This method first extracts the anatomical shape of the LA, and then estimates a prior probability map. It projects the resulting segmentation onto the LA surface, for quantification and analysis of scarring. We applied the proposed method to 36 clinical data sets and obtained promising results (Accuracy: 0.809±150, Dice: 0.556±187). We compared the method with the conventional algorithms and showed an evidently and statistically better performance (p < 0.03).

  • Conference paper
    Mo Y, Liu F, McIlwraith D, Yang G, Zhang J, He T, Guo Yet al., 2018,

    The Deep Poincaré Map: A Novel Approach for Left Ventricle Segmentation

    , 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI2018), Pages: 561-568, ISSN: 0302-9743

    © 2018, Springer Nature Switzerland AG. Precise segmentation of the left ventricle (LV) within cardiac MRI images is a prerequisite for the quantitative measurement of heart function. However, this task is challenging due to the limited availability of labeled data and motion artifacts from cardiac imaging. In this work, we present an iterative segmentation algorithm for LV delineation. By coupling deep learning with a novel dynamic-based labeling scheme, we present a new methodology where a policy model is learned to guide an agent to travel over the image, tracing out a boundary of the ROI – using the magnitude difference of the Poincaré map as a stopping criterion. Our method is evaluated on two datasets, namely the Sunnybrook Cardiac Dataset (SCD) and data from the STACOM 2011 LV segmentation challenge. Our method outperforms the previous research over many metrics. In order to demonstrate the transferability of our method we present encouraging results over the STACOM 2011 data, when using a model trained on the SCD dataset.

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