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• Journal article
  Kousi E, Smith J, Ledger AE, Scurr E, Allen S, Wilson RM, O'Flynn E, Pope RJE, Leach MO, Schmidt MAet al., 2018,

  Quantitative evaluation of contrast agent uptake in standard fat-suppressed dynamic contrast-enhanced MRI examinations of the breast

  , MEDICAL PHYSICS, Vol: 45, Pages: 287-296, ISSN: 0094-2405
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  • Citations: 6
• Conference paper
  Soltaninejad M, Zhang L, Lambrou T, Yang G, Allinson N, Ye Xet al., 2018,

  MRI Brain Tumor Segmentation and Patient Survival Prediction Using Random Forests and Fully Convolutional Networks

  , 3rd International Workshop on Brain-Lesion (BrainLes) held jointly at the Conference on Medical Image Computing for Computer Assisted Intervention (MICCAI), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 204-215, ISSN: 0302-9743
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  • Citations: 40
• Journal article
  Grech-Sollars M, Vaqas B, Thompson G, Barwick T, Honeyfield L, O'Neill K, Waldman ADet al., 2017,

  An MRS- and PET-guided biopsy tool for intraoperative neuronavigational systems.

  , J Neurosurg, Vol: 127, Pages: 812-818

  OBJECTIVE Glioma heterogeneity and the limitations of conventional structural MRI for identifying aggressive tumor components can limit the reliability of stereotactic biopsy and, hence, tumor characterization, which is a hurdle for developing and selecting effective treatment strategies. In vivo MR spectroscopy (MRS) and PET enable noninvasive imaging of cellular metabolism relevant to proliferation and can detect regions of more highly active tumor. Here, the authors integrated presurgical PET and MRS with intraoperative neuronavigation to guide surgical biopsy and tumor sampling of brain gliomas with the aim of improving intraoperative tumor-tissue characterization and imaging biomarker validation. METHODS A novel intraoperative neuronavigation tool was developed as part of a study that aimed to sample high-choline tumor components identified by multivoxel MRS and 18F-methylcholine PET-CT. Spatially coregistered PET and MRS data were integrated into structural data sets and loaded onto an intraoperative neuronavigation system. High and low choline uptake/metabolite regions were represented as color-coded hollow spheres for targeted stereotactic biopsy and tumor sampling. RESULTS The neurosurgeons found the 3D spherical targets readily identifiable on the interactive neuronavigation system. In one case, areas of high mitotic activity were identified on the basis of high 18F-methylcholine uptake and elevated choline ratios found with MRS in an otherwise low-grade tumor, which revealed the possible use of this technique for tumor characterization. CONCLUSIONS These PET and MRI data can be combined and represented usefully for the surgeon in neuronavigation systems. This method enables neurosurgeons to sample tumor regions based on physiological and molecular imaging markers. The technique was applied for characterizing choline metabolism using MRS and 18F PET; however, this approach provides proof of principle for using different radionuclide tracers and other MRI m

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• Conference paper
  Yang G, Zhuang X, Khan H, Haldar S, Nyktari E, Ye X, Slabaugh G, Wong T, Mohiaddin R, Keegan J, Firmin Det al., 2017,

  Segmenting atrial fibrosis from late gadolinium-enhanced cardiac MRI by deep-learned features with stacked sparse auto-encoders

  , MIUA 2017, Publisher: Springer, Pages: 195-206, ISSN: 1865-0929

  The late gadolinium-enhanced (LGE) MRI technique is a well-validated method for fibrosis detection in the myocardium. With this technique, the altered wash-in and wash-out contrast agent kinetics in fibrotic and healthy myocardium results in scar tissue being seen with high or enhanced signal relative to normal tissue which is ‘nulled’. Recently, great progress on LGE MRI has resulted in improved visualization of fibrosis in the left atrium (LA). This provides valuable information for treatment planning, image-based procedure guidance and clinical management in patients with atrial fibrillation (AF). Nevertheless, precise and objective atrial fibrosis segmentation (AFS) is required for accurate assessment of AF patients using LGE MRI. This is a very challenging task, not only because of the limited quality and resolution of the LGE MRI images acquired in AF but also due to the thinner wall and unpredictable morphology of the LA. Accurate and reliable segmentation of the anatomical structure of the LA myocardium is a prerequisite for accurate AFS. Most current studies rely on manual segmentation of the anatomical structures, which is very labor-intensive and subject to inter- and intra-observer variability. The subsequent AFS is normally based on unsupervised learning methods, e.g., using thresholding, histogram analysis, clustering and graph-cut based approaches, which have variable accuracy. In this study, we present a fully-automated multi-atlas propagation based whole heart segmentation method to derive the anatomical structure of the LA myocardium and pulmonary veins. This is followed by a supervised deep learning method for AFS. Twenty clinical LGE MRI scans from longstanding persistent AF patients were entered into this study retrospectively. We have demonstrated that our fully automatic method can achieve accurate and reliable AFS compared to manual delineated ground truth.

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• Conference paper
  Yang G, Zhuang X, Khan H, Haldar S, Nyktari E, Ye X, Slabaugh G, Wong T, Mohiaddin R, Keegan J, Firmin Det al., 2017,

  A fully automatic deep learning method for atrial scarring segmentation from late gadolinium-enhanced MRI images

  , 2017 IEEE 14th International Symposium on Biomedical Imaging, Publisher: IEEE, Pages: 844-848, ISSN: 1945-7928

  Precise and objective segmentation of atrial scarring (SAS) is a prerequisite for quantitative assessment of atrial fibrillation using non-invasive late gadolinium-enhanced (LGE) MRI. This also requires accurate delineation of the left atrium (LA) and pulmonary veins (PVs) geometry. Most previous studies have relied on manual segmentation of LA wall and PVs, which is a tedious and error-prone procedure with limited reproducibility. There are many attempts on automatic SAS using simple thresholding, histogram analysis, clustering and graph-cut based approaches; however, in general, these methods are considered as unsupervised learning thus subject to limited segmentation accuracy. In this study, we present a fully-automated multi-atlas based whole heart segmentation method to derive the LA and PVs geometry objectively that is followed by a fully automatic deep learning method for SAS. Our deep learning method consists of a feature extraction step via super-pixel over-segmentation and a supervised classification step via stacked sparse auto-encoders. We demonstrate the efficacy of our method on 20 clinical LGE MRI scans acquired from a longstanding persistent atrial fibrillation cohort. Both quantitative and qualitative results show that our fully automatic method obtained accurate segmentation results compared to the manual segmentation based ground truths.

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• Journal article
  Sun Y, Reynolds H, Wraith D, Williams S, Finnegan ME, Mitchell C, Murphy D, Ebert MA, Haworth Aet al., 2017,

  Predicting prostate tumour location from multiparametric MRI using Gaussian kernel support vector machines: a preliminary study

  , AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE, Vol: 40, Pages: 39-49, ISSN: 0158-9938
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  • Citations: 22
• Conference paper
  Yang G, Zhuang X, Khan H, Haldar S, Nyktari E, Li L, Ye X, Slabaugh G, Mohiaddin R, Keegan J, otherset al., 2017,

  Differentiation of Pre-ablation and Post-ablation Late Gadolinium-enhanced Cardiac MRI Scans of Longstanding Persistent Atrial Fibrillation Patients

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• Conference paper
  Olliverre N, Asad M, Yang G, Howe F, Slabaugh Get al., 2017,

  Pairwise Mixture Model for Unmixing Partial Volume Effect in Multi-voxel MR Spectroscopy of Brain Tumour Patients

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• Conference paper
  Dong H, Yang G, Liu F, Mo Y, Guo Yet al., 2017,

  Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks

  , 21st Annual Conference on Medical Image Understanding and Analysis (MIUA), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 506-517, ISSN: 1865-0929
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  • Citations: 358
• Conference paper
  Yang G, Zhuang X, Khan H, Haldar S, Nyktari E, Li L, Ye X, Slabaugh G, Mohiaddin R, Keegan J, otherset al., 2017,

  Multi-atlas Propagation based Left Atrium Segmentation Coupled with Super-voxel based Pulmonary Veins Delineation in Late Gadolinium-enhanced Cardiac MRI

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