Evaluation of Enhanced Learning Techniques for Segmenting Ischaemic Stroke Lesions in Brain Magnetic Resonance Perfusion Images Using a Convolutional Neural Network Scheme

Magnetic resonance (MR) perfusion imaging non-invasively measures cerebral perfusion, which describes the blood's passage through the brain's vascular network. Therefore, it is widely used to assess cerebral ischaemia. Convolutional Neural Networks (CNN) constitute the state-of-the-art method in automatic pattern recognition and hence, in segmentation tasks. But none of the CNN architectures developed to date have achieved high accuracy when segmenting ischaemic stroke lesions, being the main reasons their heterogeneity in location, shape, size, image intensity and texture, especially in this imaging modality. We use a freely available CNN framework, developed for MR imaging lesion segmentation, as core algorithm to evaluate the impact of enhanced machine learning techniques, namely data augmentation, transfer learning and post-processing, in the segmentation of stroke lesions using the ISLES 2017 dataset, which contains expert annotated diffusion-weighted perfusion and diffusion brain MRI of 43 stroke patients. Of all the techniques evaluated, data augmentation with binary closing achieved the best results, improving the mean Dice score in 17% over the baseline model. Consistent with previous works, better performance was obtained in the presence of large lesions

A Review on Computer Aided Diagnosis of Acute Brain Stroke.

Amongst the most common causes of death globally, stroke is one of top three affecting over 100 million people worldwide annually. There are two classes of stroke, namely ischemic stroke (due to impairment of blood supply, accounting for ~70% of all strokes) and hemorrhagic stroke (due to bleeding), both of which can result, if untreated, in permanently damaged brain tissue. The discovery that the affected brain tissue (i.e., 'ischemic penumbra') can be salvaged from permanent damage and the bourgeoning growth in computer aided diagnosis has led to major advances in stroke management. Abiding to the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines, we have surveyed a total of 177 research papers published between 2010 and 2021 to highlight the current status and challenges faced by computer aided diagnosis (CAD), machine learning (ML) and deep learning (DL) based techniques for CT and MRI as prime modalities for stroke detection and lesion region segmentation. This work concludes by showcasing the current requirement of this domain, the preferred modality, and prospective research areas

Editorial: Deep Learning in Aging Neuroscience

MINECO/FEDER TEC2015-64718-R RTI2018-098913-B-100 PGC2018-098813-B-C32General Secretariat for Universities, Research and Technology of the Junta de Andalucia under FEDER Andalucia project A-TIC-117-UGR1

AIFNet: Automatic Vascular Function Estimation for Perfusion Analysis Using Deep Learning

Perfusion imaging is crucial in acute ischemic stroke for quantifying the salvageable penumbra and irreversibly damaged core lesions. As such, it helps clinicians to decide on the optimal reperfusion treatment. In perfusion CT imaging, deconvolution methods are used to obtain clinically interpretable perfusion parameters that allow identifying brain tissue abnormalities. Deconvolution methods require the selection of two reference vascular functions as inputs to the model: the arterial input function (AIF) and the venous output function, with the AIF as the most critical model input. When manually performed, the vascular function selection is time demanding, suffers from poor reproducibility and is subject to the professionals' experience. This leads to potentially unreliable quantification of the penumbra and core lesions and, hence, might harm the treatment decision process. In this work we automatize the perfusion analysis with AIFNet, a fully automatic and end-to-end trainable deep learning approach for estimating the vascular functions. Unlike previous methods using clustering or segmentation techniques to select vascular voxels, AIFNet is directly optimized at the vascular function estimation, which allows to better recognise the time-curve profiles. Validation on the public ISLES18 stroke database shows that AIFNet reaches inter-rater performance for the vascular function estimation and, subsequently, for the parameter maps and core lesion quantification obtained through deconvolution. We conclude that AIFNet has potential for clinical transfer and could be incorporated in perfusion deconvolution software.Comment: Preprint submitted to Elsevie

Deep learning-based brain tumour image segmentation and its extension to stroke lesion segmentation

Medical imaging plays a very important role in clinical methods of treating cancer, as well as treatment selection, diagnosis, an evaluating the response to therapy. One of the best-known acquisition modalities is magnetic resonance imaging (MRI), which is used widely in the analysis of brain tumours by means of acquisition protocols (e.g. conventional and advanced). Due to the wide variation in the shape, location and appearance of tumours, automated segmentation in MRI is a difficult task. Although many studies have been conducted, automated segmentation is difficult and work to improve the accuracy of tumour segmentation is still ongoing. This research aims to develop fully automated methods for segmenting the abnormal tissues associated with brain tumours (i.e. those subject to oedema, necrosis and enhanced) from the multimodal MRI images that help radiologists to diagnose conditions and plan treatment. In this thesis the machine-learned features from the deep learning convolutional neural network (CIFAR) are investigated and joined with hand-crafted histogram texture features to encode global information and local dependencies in the representation of features. The combined features are then applied in a decision tree (DT) classifier to group individual pixels into normal brain tissues and the various parts of a tumour. These features are good point view for the clinicians to accurately visualize the texture tissue of tumour and sub-tumour regions. To further improve the segmentation of tumour and sub-tumour tissues, 3D datasets of the four MRI modalities (i.e. FLAIR, T1, T1ce and T2) are used and fully convolutional neural networks, called SegNet, are constructed for each of these four modalities of images. The outputs of these four SegNet models are then fused by choosing the one with the highest scores to construct feature maps, with the pixel intensities as an input to a DT classifier to further classify each pixel as either a normal brain tissue or the component parts of a tumour. To achieve a high-performance accuracy in the segmentation as a whole, deep learning (the IV SegNet network) and the hand-crafted features are combined, particularly in the grey-level co-occurrence matrix (GLCM) in the region of interest (ROI) that is initially detected from FLAIR modality images using the SegNet network. The methods that have been developed in this thesis (i.e. CIFAR _PI_HIS _DT, SegNet_Max_DT and SegNet_GLCM_DT) are evaluated on two datasets: the first is the publicly available Multimodal Brain Tumour Image Segmentation Benchmark (BRATS) 2017 dataset, and the second is a clinical dataset. In brain tumour segmentation methods, the F-measure performance of more than 0.83 is accepted, or at least useful from a clinical point of view, for segmenting the whole tumour structure which represents the brain tumour boundaries. Thanks to it, our proposed methods show promising results in the segmentation of brain tumour structures and they provide a close match to expert delineation across all grades of glioma. To further detect brain injury, these three methods were adopted and exploited for ischemic stroke lesion segmentation. In the steps of training and evaluation, the publicly available Ischemic Stroke Lesion (ISLES 2015) dataset and a clinical dataset were used. The performance accuracies of the three developed methods in ischemic stroke lesion segmentation were assessed. The third segmentation method (SegNet_GLCM_DT) was found to be more accurate than the other two methods (SegNet_Max_DT and SegNet_GLCM_DT) because it exploits GLCM as a set of hand-crafted features with machine features, which increases the accuracy of segmentation with ischemic stroke lesion

ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI

Ischemic stroke is the most common cerebrovascular disease, and its diagnosis, treatment, and study relies on non-invasive imaging. Algorithms for stroke lesion segmentation from magnetic resonance imaging (MRI) volumes are intensely researched, but the reported results are largely incomparable due to different datasets and evaluation schemes. We approached this urgent problem of comparability with the Ischemic Stroke Lesion Segmentation (ISLES) challenge organized in conjunction with the MICCAI 2015 conference. In this paper we propose a common evaluation framework, describe the publicly available datasets, and present the results of the two sub-challenges: Sub-Acute Stroke Lesion Segmentation (SISS) and Stroke Perfusion Estimation (SPES). A total of 16 research groups participated with a wide range of state-of-the-art automatic segmentation algorithms. A thorough analysis of the obtained data enables a critical evaluation of the current state-of-the-art, recommendations for further developments, and the identification of remaining challenges. The segmentation of acute perfusion lesions addressed in SPES was found to be feasible. However, algorithms applied to sub-acute lesion segmentation in SISS still lack accuracy. Overall, no algorithmic characteristic of any method was found to perform superior to the others. Instead, the characteristics of stroke lesion appearances, their evolution, and the observed challenges should be studied in detail. The annotated ISLES image datasets continue to be publicly available through an online evaluation system to serve as an ongoing benchmarking resource (www.isles-challenge.org).Peer reviewe

Development of machine learning schemes for segmentation, characterisation, and evolution prediction of white matter hyperintensities in structural brain MRI

White matter hyperintensities (WMH) are neuroradiological features seen in T2 Fluid-Attenuated Inversion Recovery (T2-FLAIR) brain magnetic resonance imaging (MRI) and have been commonly associated with stroke, ageing, dementia, and Alzheimer’s disease (AD) progression. As a marker of neuro-degenerative disease, WMH may change over time and follow the clinical condition of the patient. In contrast to the early longitudinal studies of WMH, recent studies have suggested that the progression of WMH may be a dynamic, non-linear process where different clusters of WMH may shrink, stay unchanged, or grow. In this thesis, these changes are referred to as the “evolution of WMH”. The main objective of this thesis is to develop machine learning methods for prediction of WMH evolution in structural brain MRI from one-time (baseline) assessment. Predicting the evolution of WMH is challenging because the rate and direction of WMH evolution varies greatly across previous studies. Furthermore, the evolution of WMH is a non-deterministic problem because some clinical factors that possibly influence it are still not known. In this thesis, different learning schemes of deep learning algorithm and data modalities are proposed to produce the best estimation of WMH evolution. Furthermore, a scheme to simulate the non-deterministic nature of WMH evolution, named auxiliary input, was also proposed. In addition to the development of prediction model for WMH evolution, machine learning methods for segmentation of early WMH, characterisation of WMH, and simulation of WMH progression and regression are also developed as parts of this thesis