HeMIS: Hetero-Modal Image Segmentation

We introduce a deep learning image segmentation framework that is extremely robust to missing imaging modalities. Instead of attempting to impute or synthesize missing data, the proposed approach learns, for each modality, an embedding of the input image into a single latent vector space for which arithmetic operations (such as taking the mean) are well defined. Points in that space, which are averaged over modalities available at inference time, can then be further processed to yield the desired segmentation. As such, any combinatorial subset of available modalities can be provided as input, without having to learn a combinatorial number of imputation models. Evaluated on two neurological MRI datasets (brain tumors and MS lesions), the approach yields state-of-the-art segmentation results when provided with all modalities; moreover, its performance degrades remarkably gracefully when modalities are removed, significantly more so than alternative mean-filling or other synthesis approaches.Comment: Accepted as an oral presentation at MICCAI 201

3D deep convolutional neural network-based ventilated lung segmentation using multi-nuclear hyperpolarized gas MRI

Hyperpolarized gas MRI enables visualization of regional lung ventilation with high spatial resolution. Segmentation of the ventilated lung is required to calculate clinically relevant biomarkers. Recent research in deep learning (DL) has shown promising results for numerous segmentation problems. In this work, we evaluate a 3D V-Net to segment ventilated lung regions on hyperpolarized gas MRI scans. The dataset consists of 743 helium-3 (3He) or xenon-129 (129Xe) volumetric scans and corresponding expert segmentations from 326 healthy subjects and patients with a wide range of pathologies. We evaluated segmentation performance for several DL experimental methods via overlap, distance and error metrics and compared them to conventional segmentation methods, namely, spatial fuzzy c-means (SFCM) and K-means clustering. We observed that training on combined 3He and 129Xe MRI scans outperformed other DL methods, achieving a mean ± SD Dice of 0.958 ± 0.022, average boundary Hausdorff distance of 2.22 ± 2.16 mm, Hausdorff 95th percentile of 8.53 ± 12.98 mm and relative error of 0.087 ± 0.049. Moreover, no difference in performance was observed between 129Xe and 3He scans in the testing set. Combined training on 129Xe and 3He yielded statistically significant improvements over the conventional methods (p < 0.0001). The DL approach evaluated provides accurate, robust and rapid segmentations of ventilated lung regions and successfully excludes non-lung regions such as the airways and noise artifacts and is expected to eliminate the need for, or significantly reduce, subsequent time-consuming manual editing

Automatic Segmentation of Muscle Tissue and Inter-muscular Fat in Thigh and Calf MRI Images

Magnetic resonance imaging (MRI) of thigh and calf muscles is one of the most effective techniques for estimating fat infiltration into muscular dystrophies. The infiltration of adipose tissue into the diseased muscle region varies in its severity across, and within, patients. In order to efficiently quantify the infiltration of fat, accurate segmentation of muscle and fat is needed. An estimation of the amount of infiltrated fat is typically done visually by experts. Several algorithmic solutions have been proposed for automatic segmentation. While these methods may work well in mild cases, they struggle in moderate and severe cases due to the high variability in the intensity of infiltration, and the tissue's heterogeneous nature. To address these challenges, we propose a deep-learning approach, producing robust results with high Dice Similarity Coefficient (DSC) of 0.964, 0.917 and 0.933 for muscle-region, healthy muscle and inter-muscular adipose tissue (IMAT) segmentation, respectively.Comment: 9 pages, 4 figures, 2 tables, MICCAI 2019, the 22nd International Conference on Medical Image Computing and Computer Assisted Interventio

Retinal blood vessels extraction using probabilistic modelling

© 2014 Kaba et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.This article has been made available through the Brunel Open Access Publishing Fund.The analysis of retinal blood vessels plays an important role in detecting and treating retinal diseases. In this review, we present an automated method to segment blood vessels of fundus retinal image. The proposed method could be used to support a non-intrusive diagnosis in modern ophthalmology for early detection of retinal diseases, treatment evaluation or clinical study. This study combines the bias correction and an adaptive histogram equalisation to enhance the appearance of the blood vessels. Then the blood vessels are extracted using probabilistic modelling that is optimised by the expectation maximisation algorithm. The method is evaluated on fundus retinal images of STARE and DRIVE datasets. The experimental results are compared with some recently published methods of retinal blood vessels segmentation. The experimental results show that our method achieved the best overall performance and it is comparable to the performance of human experts.The Department of Information Systems, Computing and Mathematics, Brunel University

Brain Tumor Segmentation from Multi-Spectral MR Image Data Using Random Forest Classifier

The development of brain tumor segmentation techniques based on multi-spectral MR image data has relevant impact on the clinical practice via better diagnosis, radiotherapy planning and follow-up studies. This task is also very challenging due to the great variety of tumor appearances, the presence of several noise effects, and the differences in scanner sensitivity. This paper proposes an automatic procedure trained to distinguish gliomas from normal brain tissues in multi-spectral MRI data. The procedure is based on a random forest (RF) classifier, which uses 80 computed features beside the four observed ones, including morphological ones, gradients, and Gabor wavelet features. The intermediary segmentation outcome provided by the RF is fed to a twofold post-processing, which regularizes the shape of detected tumors and enhances the segmentation accuracy. The performance of the procedure was evaluated using the 274 records of the BraTS 2015 train data set. The achieved overall Dice scores between 85-86% represent highly accurate segmentation

Realistic Adversarial Data Augmentation for MR Image Segmentation

Neural network-based approaches can achieve high accuracy in various medical image segmentation tasks. However, they generally require large labelled datasets for supervised learning. Acquiring and manually labelling a large medical dataset is expensive and sometimes impractical due to data sharing and privacy issues. In this work, we propose an adversarial data augmentation method for training neural networks for medical image segmentation. Instead of generating pixel-wise adversarial attacks, our model generates plausible and realistic signal corruptions, which models the intensity inhomogeneities caused by a common type of artefacts in MR imaging: bias field. The proposed method does not rely on generative networks, and can be used as a plug-in module for general segmentation networks in both supervised and semi-supervised learning. Using cardiac MR imaging we show that such an approach can improve the generalization ability and robustness of models as well as provide significant improvements in low-data scenarios

Geodesically smoothed tensor features for pulmonary hypertension prognosis using the heart and surrounding tissues

Cardiac magnetic resonance imaging (CMRI) provides non-invasive characterization of the heart and surrounding tissues. It is an important tool for the prognosis of pulmonary arterial hypertension (PAH), a disease with heterogeneous presentation that makes survival likelihood prediction a challenging task. In this paper, we propose a Geodesically Smooothed Tensor feature learning method (GST) that utilizes not only the heart but also its surrounding tissues to characterize disease severity for improving prognosis. Specifically, GST includes structures surrounding the heart by geodesic rings which were incrementally smoothed with Gaussian filters. This provides additive insight while modulating for patient positional differences for a subsequent tensor-based feature learning pipeline. We performed evaluation on Four Chamber and Short Axis CMRI from 150 individuals with confirmed PAH and 1-year mortality census (27 deceased, 123 alive). The proposed GST method improved AUC and Cox difference at 4-years post-imaging (Cox4YD) over the standardized measurement of right ventricular end systolic volume index (RVESVi: AUC: 0.58; Cox4YD: 0.18) on the Four Chamber protocol (AUC: 0.77; Cox4YD: 0.35). Only AUC was improved over RVESVi in the Short Axis scans (AUC: 0.77; Cox4YD: 0.16)

Automated Glioblastoma Segmentation Based on a Multiparametric Structured Unsupervised Classification

Automatic brain tumour segmentation has become a key component for the future of brain tumour treatment. Currently, most of brain tumour segmentation approaches arise from the supervised learning standpoint, which requires a labelled training dataset from which to infer the models of the classes. The performance of these models is directly determined by the size and quality of the training corpus, whose retrieval becomes a tedious and time-consuming task. On the other hand, unsupervised approaches avoid these limitations but often do not reach comparable results than the supervised methods. In this sense, we propose an automated unsupervised method for brain tumour segmentation based on anatomical Magnetic Resonance (MR) images. Four unsupervised classification algorithms, grouped by their structured or non-structured condition, were evaluated within our pipeline. Considering the non-structured algorithms, we evaluated K-means, Fuzzy K-means and Gaussian Mixture Model (GMM), whereas as structured classification algorithms we evaluated Gaussian Hidden Markov Random Field (GHMRF). An automated postprocess based on a statistical approach supported by tissue probability maps is proposed to automatically identify the tumour classes after the segmentations. We evaluated our brain tumour segmentation method with the public BRAin Tumor Segmentation (BRATS) 2013 Test and Leaderboard datasets. Our approach based on the GMM model improves the results obtained by most of the supervised methods evaluated with the Leaderboard set and reaches the second position in the ranking. Our variant based on the GHMRF achieves the first position in the Test ranking of the unsupervised approaches and the seventh position in the general Test ranking, which confirms the method as a viable alternative for brain tumour segmentation.EFG was supported by Programa Torres Quevedo, Ministerio de Educacion y Ciencia, co-funded by the European Social Fund (PTQ-1205693). EFG, JMGG, and JVM were supported by Red Tematica de Investigacion Cooperativa en Cancer, (RTICC) 2013-2016 (RD12/0036/0020). JMGG was supported by Project TIN2013-43457-R: Caracterizacion de firmas biologicas de glioblastomas mediante modelos no-supervisados de prediccion estructurada basados en biomarcadores de imagen, co-funded by the Ministerio de Economia y Competitividad of Spain; CON2014001 UPV-IISLaFe: Unsupervised glioblastoma tumor components segmentation based on perfusion multiparametric MRI and spatio/temporal constraints; and CON2014002 UPV-IISLaFe: Empleo de segmentacion no supervisada multiparametrica basada en perfusion RM para la caracterizacion del edema peritumoral de gliomas y metastasis cerebrales unicas, funded by Instituto de Investigacion Sanitaria H. Universitario y Politecnico La Fe. This work was partially supported by the Instituto de Aplicaciones de las Tecnologias de la Informacion y las Comunicaciones Avanzadas (ITACA). Veratech for Health S.L. provided support in the form of salaries for author EF-G, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of this author is articulated in the "author contributions" section. This does not alter the authors' adherence to PLOS ONE policies on sharing data and materials.Juan Albarracín, J.; Fuster García, E.; Manjón Herrera, JV.; Robles Viejo, M.; Aparici, F.; Marti-Bonmati, L.; García Gómez, JM. (2015). Automated Glioblastoma Segmentation Based on a Multiparametric Structured Unsupervised Classification. PLoS ONE. 10(5):1-20. https://doi.org/10.1371/journal.pone.0125143S120105Wen, P. Y., Macdonald, D. R., Reardon, D. A., Cloughesy, T. F., Sorensen, A. G., Galanis, E., … Chang, S. M. (2010). Updated Response Assessment Criteria for High-Grade Gliomas: Response Assessment in Neuro-Oncology Working Group. 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Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease

Identifying accurate biomarkers of cognitive decline is essential for advancing early diagnosis and prevention therapies in Alzheimer's disease. The Alzheimer's disease DREAM Challenge was designed as a computational crowdsourced project to benchmark the current state-of-the-art in predicting cognitive outcomes in Alzheimer's disease based on high dimensional, publicly available genetic and structural imaging data. This meta-analysis failed to identify a meaningful predictor developed from either data modality, suggesting that alternate approaches should be considered for prediction of cognitive performance

Multicentre evaluation of MRI variability in the quantification of infarct size in experimental focal cerebral ischaemia

Ischaemic stroke is a leading cause of death and disability in the developed world. Despite that considerable advances in experimental research enabled understanding of the pathophysiology of the disease and identified hundreds of potential neuroprotective drugs for treatment, no such drug has shown efficacy in humans. The failure in the translation from bench to bedside has been partially attributed to the poor quality and rigour of animal studies. Recently, it has been suggested that multicentre animal studies imitating the design of randomised clinical trials could improve the translation of experimental research. Magnetic resonance imaging (MRI) could be pivotal in such studies due to its non-invasive nature and its high sensitivity to ischaemic lesions, but its accuracy and concordance across centres has not yet been evaluated. This thesis focussed on the use of MRI for the assessment of late infarct size, the primary outcome used in stroke models. Initially, a systematic review revealed that a plethora of imaging protocols and data analysis methods are used for this purpose. Using meta-analysis techniques, it was determined that T2-weighted imaging (T2WI) was best correlated with gold standard histology for the measurement of infarctbased treatment effects. Then, geometric accuracy in six different preclinical MRI scanners was assessed using structural phantoms and automated data analysis tools developed in-house. It was found that geometric accuracy varies between scanners, particularly when centre-specific T2WI protocols are used instead of a standardised protocol, though longitudinal stability over six months is high. Finally, a simulation study suggested that the measured geometric errors and the different protocols are sufficient to render infarct volumes and related group comparisons across centres incomparable. The variability increases when both factors are taken into account and when infarct volume is expressed as a relative estimate. Data in this study were analysed using a custom-made semi-automated tool that was faster and more reliable in repeated analyses than manual analysis. Findings of this thesis support the implementation of standardised methods for the assessment and optimisation of geometric accuracy in MRI scanners, as well as image acquisition and analysis of in vivo data for the measurement of infarct size in multicentre animal studies. Tools and techniques developed as part of the thesis show great promise in the analysis of phantom and in vivo data and could be a step towards this endeavour