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  • Journal article
    Zhuang X, Li L, Payer C, Stern D, Urschler M, Heinrich M, Oster J, Wang C, Smedby O, Bian C, Yang X, Heng P-A, Mortazi A, Bagci U, Yang G, Sun C, Galisot G, Ramel J-Y, Brouard T, Tong Q, Si W, Liao X, Zeng G, Shi Z, Zheng G, Wang C, MacGillivray T, Newby D, Rhode K, Ourselin S, Mohiaddin R, Keegan J, Firmin D, Yang Get al., 2019,

    Evaluation of algorithms for multi-modality whole heart segmentation: An open-access grand challenge

    , Medical Image Analysis, Vol: 58, ISSN: 1361-8415

    Knowledge of whole heart anatomy is a prerequisite for many clinical applications. Whole heart segmentation (WHS),which delineates substructures of the heart, can be very valuable for modeling and analysis of the anatomy and functionsof the heart. However, automating this segmentation can be challenging due to the large variation of the heart shape,and different image qualities of the clinical data. To achieve this goal, an initial set of training data is generally neededfor constructing priors or for training. Furthermore, it is difficult to perform comparisons between different methods,largely due to differences in the datasets and evaluation metrics used. This manuscript presents the methodologiesand evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017. The challenge provided 120 three-dimensionalcardiac images covering the whole heart, including 60 CT and 60 MRI volumes, all acquired in clinical environmentswith manual delineation. Ten algorithms for CT data and eleven algorithms for MRI data, submitted from twelvegroups, have been evaluated. The results showed that the performance of CT WHS was generally better than thatof MRI WHS. The segmentation of the substructures for different categories of patients could present different levelsof challenge due to the difference in imaging and variations of heart shapes. The deep learning (DL)-based methodsdemonstrated great potential, though several of them reported poor results in the blinded evaluation. Their performance could vary greatly across different network structures and training strategies. The conventional algorithms,mainly based on multi-atlas segmentation, demonstrated good performance, though the accuracy and computationalefficiency could be limited. The challenge, including provision of the annotated training data and the blinded evaluation for submitted algorithms on the test data, conti

  • Journal article
    Peghaire C, Dufton N, Lang M, Salles I, Ahnstroem J, Kalna V, Raimondi C, Pericleous C, Inuabasi L, Kiseleva R, Muzykantov V, Mason J, Birdsey G, Randi Aet al., 2019,

    The transcription factor ERG regulates a low shear stress-induced anti-thrombotic pathway in the microvasculature

    , Nature Communications, Vol: 10, Pages: 1-17, ISSN: 2041-1723

    Endothelial cells actively maintain an anti-thrombotic environment; loss of this protective function may lead to thrombosis and systemic coagulopathy. The transcription factor ERG is essential to maintain endothelial homeostasis. Here we show that inducible endothelial ERG deletion (ErgiEC-KO) in mice is associated with spontaneous thrombosis, hemorrhages and systemic coagulopathy. We find that ERG drives transcription of the anti-coagulant thrombomodulin (TM), as shown by reporter assays and chromatin immunoprecipitation. TM expression is regulated by shear stress (SS) via Krüppel-like factor 2 (KLF2). In vitro, ERG regulates TM expression under low SS conditions, by facilitating KLF2 binding to the TM promoter. However, ERG is dispensable for TM expression in high SS conditions. In ErgiEC-KO mice, TM expression is decreased in liver and lung microvasculature exposed to low SS but not in blood vessels exposed to high SS. Our study identifies an endogenous, vascular bed- specific anti-coagulant pathway in microvasculature exposed to low SS.

  • Journal article
    Hultman K, Edsfeldt A, Björkbacka H, Dunér P, Sundius L, Nitulescu M, Persson A, Boyle JJ, Nilsson J, Hultgårdh-Nilsson A, Bengtsson E, Gonçalves Iet al., 2019,

    Cartilage oligomeric matrix protein associates with a vulnerable plaque phenotype in human atherosclerotic plaques

    , Stroke, Vol: 50, ISSN: 0039-2499

    Background and Purpose- Extracellular matrix proteins are important in atherosclerotic disease by influencing plaque stability and cellular behavior but also by regulating inflammation. COMP (cartilage oligomeric matrix protein) is present in healthy human arteries and expressed by smooth muscle cells. A recent study showed that transplantation of COMP-deficient bone marrow to apoE-/- mice increased atherosclerotic plaque formation, indicating a role for COMP also in bone marrow-derived cells. Despite the evidence of a role for COMP in murine atherosclerosis, knowledge is lacking about the role of COMP in human atherosclerotic disease. Methods- In the present study, we investigated if COMP was associated with a stable or a vulnerable human atherosclerotic plaque phenotype by analyzing 211 carotid plaques for COMP expression using immunohistochemistry. Results- Plaque area that stained positive for COMP was significantly larger in atherosclerotic plaques associated with symptoms (n=110) compared with asymptomatic plaques (n=101; 9.7% [4.7-14.3] versus 5.6% [2.8-9.8]; P=0.0002). COMP was positively associated with plaque lipids (r=0.32; P=0.000002) and CD68 cells (r=0.15; P=0.036) but was negatively associated with collagen (r=-0.16; P=0.024), elastin (r=-0.14; P=0.041), and smooth muscle cells (r=-0.25; P=0.0002). COMP was positively associated with CD163 (r=0.37; P=0.00000006), a scavenger receptor for hemoglobin/haptoglobin and a marker of Mhem macrophages, and with intraplaque hemorrhage, measured as glycophorin A staining (r=0.28; P=0.00006). Conclusions- The present study shows that COMP is associated to symptomatic carotid atherosclerosis, CD163-expressing cells, and a vulnerable atherosclerotic plaque phenotype in humans.

  • Conference paper
    Wang C, Papanastasiou G, Tsaftaris S, Yang G, Gray C, Newby D, Macnaught G, MacGillivray Tet al., 2019,

    TPSDicyc: Improved deformation invariant cross-domain medical image synthesis

    , Second International Workshop, MLMIR 2019, Held in Conjunction with MICCAI 2019, Publisher: Springer International Publishing, Pages: 245-254, ISSN: 0302-9743

    Cycle-consistent generative adversarial network (CycleGAN) has been widely used for cross-domain medical image systhesis tasks particularly due to its ability to deal with unpaired data. However, most CycleGAN-based synthesis methods can not achieve good alignment between the synthesized images and data from the source domain, even with additional image alignment losses. This is because the CycleGAN generator network can encode the relative deformations and noises associated to different domains. This can be detrimental for the downstream applications that rely on the synthesized images, such as generating pseudo-CT for PET-MR attenuation correction. In this paper, we present a deformation invariant model based on the deformation-invariant CycleGAN (DicycleGAN) architecture and the spatial transformation network (STN) using thin-plate-spline (TPS). The proposed method can be trained with unpaired and unaligned data, and generate synthesised images aligned with the source data. Robustness to the presence of relative deformations between data from the source and target domain has been evaluated through experiments on multi-sequence brain MR data and multi-modality abdominal CT and MR data. Experiment results demonstrated that our method can achieve better alignment between the source and target data while maintaining superior image quality of signal compared to several state-of-the-art CycleGAN-based methods.

  • Conference paper
    Chen J, Zhang H, Zhang Y, Zhao S, Mohiaddin R, Wong T, Firmin D, Yang G, Keegan Jet al., 2019,

    Discriminative consistent domain generation for semi-supervised learning

    , International Conference on Medical Image Computing and Computer-Assisted Intervention, Publisher: Springer International Publishing, Pages: 595-604, ISSN: 0302-9743

    Deep learning based task systems normally rely on a large amount of manually labeled training data, which is expensive to obtain and subject to operator variations. Moreover, it does not always hold that the manually labeled data and the unlabeled data are sitting in the same distribution. In this paper, we alleviate these problems by proposing a discriminative consistent domain generation (DCDG) approach to achieve a semi-supervised learning. The discriminative consistent domain is achieved by a double-sided domain adaptation. The double-sided domain adaptation aims to make a fusion of the feature spaces of labeled data and unlabeled data. In this way, we can fit the differences of various distributions between labeled data and unlabeled data. In order to keep the discriminativeness of generated consistent domain for the task learning, we apply an indirect learning for the double-sided domain adaptation. Based on the generated discriminative consistent domain, we can use the unlabeled data to learn the task model along with the labeled data via a consistent image generation. We demonstrate the performance of our proposed DCDG on the late gadolinium enhancement cardiac MRI (LGE-CMRI) images acquired from patients with atrial fibrillation in two clinical centers for the segmentation of the left atrium anatomy (LA) and proximal pulmonary veins (PVs). The experiments show that our semi-supervised approach achieves compelling segmentation results, which can prove the robustness of DCDG for the semi-supervised learning using the unlabeled data along with labeled data acquired from a single center or multicenter studies.

  • Journal article
    Poo SX, Tham CSW, Smith C, Lee J, Cairns T, Galliford J, Hamdulay S, Jacyna M, Levy JB, McAdoo S, Roufosse C, Wernig F, Mason J, Pusey C, Tam F, Tomlinson Jet al., 2019,

    IgG4-related disease in a multi-ethnic community: Clinical characteristics and association with malignancy

    , QJM: An International Journal of Medicine, Vol: 112, Pages: 763-769, ISSN: 1460-2393

    BackgroundImmunoglobulin-G4-related disease (IgG4-RD) is a recently recognised fibro-inflammatory condition that can affect multiple organs. Despite growing interest in this condition, the natural history and management of IgG4-RD remain poorly understood.AimTo describe the clinical characteristics, treatment and outcomes of IgG4-RD in a multi-ethnic UK cohort, and investigate its possible association with malignancy.DesignRetrospective analysis of case-note and electronic data.MethodsCases were identified from sub-specialty cohorts and a systematic search of an NHS trust histopathology database using ‘IgG4’ or ‘inflammatory pseudotumour’ as search terms. Electronic records, imaging and histopathology reports were reviewed.Results66 identified cases of IgG4-RD showed a similar multi-ethnic spread to the local population of North West London. The median age was 59 years and 71% of patients were male. Presenting symptoms relating to mass effect of a lesion were present in 48% of cases and the mean number of organs involved was 2.4. 10 patients had reported malignancies with 6 of these being haematological. 83% of those treated with steroids had good initial response, however 50% had relapsing-remitting disease. Rituximab was administered in 11 cases and all achieved an initial serological response. Despite this, 7 patients subsequently relapsed after a mean duration of 11 months and 4 progressed despite treatment.ConclusionsWe report a large UK-based cohort of IgG4-RD that shows no clear ethnic predisposition and a wide range of affected organs. We discuss the use of serum IgG4 concentrations as a disease marker in IgG4-RD, the association with malignant disease and outcomes according to differing treatment regimens.

  • Journal article
    Zhang N, Yang G, Gao Z, Xu C, Zhang Y, Shi R, Keegan J, Xu L, Zhang H, Fan Z, Firmin Det al., 2019,

    Deep learning for diagnosis of chronic myocardial infarction on nonenhanced cardiac cine MRI

    , Radiology, Vol: 294, Pages: 52-60, ISSN: 0033-8419

    BackgroundRenal impairment is common in patients with coronary artery disease and, if severe, late gadolinium enhancement (LGE) imaging for myocardial infarction (MI) evaluation cannot be performed.PurposeTo develop a fully automatic framework for chronic MI delineation via deep learning on non–contrast material–enhanced cardiac cine MRI.Materials and MethodsIn this retrospective single-center study, a deep learning model was developed to extract motion features from the left ventricle and delineate MI regions on nonenhanced cardiac cine MRI collected between October 2015 and March 2017. Patients with chronic MI, as well as healthy control patients, had both nonenhanced cardiac cine (25 phases per cardiac cycle) and LGE MRI examinations. Eighty percent of MRI examinations were used for the training data set and 20% for the independent testing data set. Chronic MI regions on LGE MRI were defined as ground truth. Diagnostic performance was assessed by analysis of the area under the receiver operating characteristic curve (AUC). MI area and MI area percentage from nonenhanced cardiac cine and LGE MRI were compared by using the Pearson correlation, paired t test, and Bland-Altman analysis.ResultsStudy participants included 212 patients with chronic MI (men, 171; age, 57.2 years ± 12.5) and 87 healthy control patients (men, 42; age, 43.3 years ± 15.5). Using the full cardiac cine MRI, the per-segment sensitivity and specificity for detecting chronic MI in the independent test set was 89.8% and 99.1%, respectively, with an AUC of 0.94. There were no differences between nonenhanced cardiac cine and LGE MRI analyses in number of MI segments (114 vs 127, respectively; P = .38), per-patient MI area (6.2 cm2 ± 2.8 vs 5.5 cm2 ± 2.3, respectively; P = .27; correlation coefficient, r = 0.88), and MI area percentage (21.5% ± 17.3 vs 18.5% ± 15.4; P = .17; correlation coefficient, r = 0.89).ConclusionThe proposed deep learning f

  • Journal article
    Kalna V, Yang Y, Peghaire C, Frudd K, hannah R, Shah A, Osuna Almagro L, Boyle J, gottgens B, Ferrer J, Randi A, Birdsey Get al., 2019,

    The transcription factor ERG regulates super-enhancers associated with an endothelial-specific gene expression program

    , Circulation Research, Vol: 124, Pages: 1337-1349, ISSN: 0009-7330

    Rationale:The ETS (E-26 transformation-specific) transcription factor ERG (ETS-related gene) is essential for endothelial homeostasis, driving expression of lineage genes and repressing proinflammatory genes. Loss of ERG expression is associated with diseases including atherosclerosis. ERG’s homeostatic function is lineage-specific, because aberrant ERG expression in cancer is oncogenic. The molecular basis for ERG lineage-specific activity is unknown. Transcriptional regulation of lineage specificity is linked to enhancer clusters (super-enhancers).Objective:To investigate whether ERG regulates endothelial-specific gene expression via super-enhancers.Methods and Results:Chromatin immunoprecipitation with high-throughput sequencing in human umbilical vein endothelial cells showed that ERG binds 93% of super-enhancers ranked according to H3K27ac, a mark of active chromatin. These were associated with endothelial genes such as DLL4 (Delta-like protein 4), CLDN5 (claudin-5), VWF (von Willebrand factor), and CDH5 (VE-cadherin). Comparison between human umbilical vein endothelial cell and prostate cancer TMPRSS2 (transmembrane protease, serine-2):ERG fusion-positive human prostate epithelial cancer cell line (VCaP) cells revealed distinctive lineage-specific transcriptome and super-enhancer profiles. At a subset of endothelial super-enhancers (including DLL4 and CLDN5), loss of ERG results in significant reduction in gene expression which correlates with decreased enrichment of H3K27ac and MED (Mediator complex subunit)-1, and reduced recruitment of acetyltransferase p300. At these super-enhancers, co-occupancy of GATA2 (GATA-binding protein 2) and AP-1 (activator protein 1) is significantly lower compared with super-enhancers that remained constant following ERG inhibition. These data suggest distinct mechanisms of super-enhancer regulation in endothelial cells and highlight the unique role of ERG in controlling a core subset of super-enhancers. Most disease-assoc

  • Journal article
    Zhang L, Yang G, Ye X, 2019,

    Automatic skin lesion segmentation by coupling deep fully convolutional networks and shallow network with textons

    , Journal of Medical Imaging, Vol: 6, ISSN: 2329-4302

    Segmentation of skin lesions is an important step in computer-aided diagnosis of melanoma; it is also a very challenging task due to fuzzy lesion boundaries and heterogeneous lesion textures. We present a fully automatic method for skin lesion segmentation based on deep fully convolutional networks (FCNs). We investigate a shallow encoding network to model clinically valuable prior knowledge, in which spatial filters simulating simple cell receptive fields function in the primary visual cortex (V1) is considered. An effective fusing strategy using skip connections and convolution operators is then leveraged to couple prior knowledge encoded via shallow network with hierarchical data-driven features learned from the FCNs for detailed segmentation of the skin lesions. To our best knowledge, this is the first time the domain-specific hand craft features have been built into a deep network trained in an end-to-end manner for skin lesion segmentation. The method has been evaluated on both ISBI 2016 and ISBI 2017 skin lesion challenge datasets. We provide comparative evidence to demonstrate that our newly designed network can gain accuracy for lesion segmentation by coupling the prior knowledge encoded by the shallow network with the deep FCNs. Our method is robust without the need for data augmentation or comprehensive parameter tuning, and the experimental results show great promise of the method with effective model generalization compared to other state-of-the-art-methods.

  • Conference paper
    Wang C, MacGillivray T, Macnaught G, Yang G, Newby Det al., 2019,

    A Two-Stage U-Net Model for 3D Multi-class Segmentation on Full-Resolution Cardiac Data

    , Pages: 191-199, ISSN: 0302-9743

    Deep convolutional neural networks (CNNs) have achieved state-of-the-art performances for multi-class segmentation of medical images. However, a common problem when dealing with large, high resolution 3D data is that the volumes input into the deep CNNs has to be either cropped or downsampled due to limited memory capacity of computing devices. These operations can lead to loss of resolution and class imbalance in the input data batches, thus downgrade the performances of segmentation algorithms. Inspired by the architecture of image super-resolution CNN (SRCNN), we propose a two-stage modified U-Net framework that simultaneously learns to detect a ROI within the full volume and to classify voxels without losing the original resolution. Experiments on a variety of multi-modal 3D cardiac images have demonstrated that this framework shows better segmentation performances than state-of-the-art Deep CNNs with trained with the same similarity metrics.

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