Computer-aided extraction of select MRI markers of cerebral small vessel disease: A systematic review

Cerebral small vessel disease (CSVD) is a major vascular contributor to cognitive impairment in ageing, including dementias. Imaging remains the most promising method for in vivo studies of CSVD. To replace the subjective and laborious visual rating approaches, emerging studies have applied state-of-the-art artificial intelligence to extract imaging biomarkers of CSVD from MRI scans. We aimed to summarise published computer-aided methods for the examination of three imaging biomarkers of CSVD, namely cerebral microbleeds (CMB), dilated perivascular spaces (PVS), and lacunes of presumed vascular origin. Seventy classical image processing, classical machine learning, and deep learning studies were identified. Transfer learning and weak supervision techniques have been applied to accommodate the limitations in the training data. While good performance metrics were achieved in local datasets, there have not been generalisable pipelines validated in different research and/or clinical cohorts. Future studies could consider pooling data from multiple sources to increase data size and diversity, and evaluating performance using both image processing metrics and associations with clinical measures

Brain tumor MRI medical images classification with data augmentation by transfer learning of VGG16

The ability to estimate conclusions without direct human input in healthcare systems via computer algorithms is known as Artificial intelligence (AI) in healthcare. Deep learning (DL) approaches are already being employed or exploited for healthcare purposes, and in the case of medical images analysis, DL paradigms opened a world of opportunities. This paper describes creating a DL model based on transfer learning of VGG16 that can correctly classify MRI images as either (tumorous) or (non-tumorous). In addition, the model employed data augmentation in order to balance the dataset and increase the number of images. The dataset comes from the brain tumour classification project, which contains publicly available tumorous and non-tumorous images. The result showed that the model performed better with the augmented dataset, with its validation accuracy reaching ~100 %

A New Residual Dense Network for Dance Action Recognition From Heterogeneous View Perception

At present, part of people's body is in the state of sub-health, and more people pay attention to physical exercise. Dance is a relatively simple and popular activity, it has been widely concerned. The traditional action recognition method is easily affected by the action speed, illumination, occlusion and complex background, which leads to the poor robustness of the recognition results. In order to solve the above problems, an improved residual dense neural network method is used to study the automatic recognition of dance action images. Firstly, based on the residual model, the features of dance action are extracted by using the convolution layer and pooling layer. Then, the exponential linear element (ELU) activation function, batch normalization (BN) and Dropout technology are used to improve and optimize the model to mitigate the gradient disappearance, prevent over-fitting, accelerate convergence and enhance the model generalization ability. Finally, the dense connection network (DenseNet) is introduced to make the extracted dance action features more rich and effective. Comparison experiments are carried out on two public databases and one self-built database. The results show that the recognition rate of the proposed method on three databases are 99.98, 97.95, and 0.97.96%, respectively. It can be seen that this new method can effectively improve the performance of dance action recognition

Deteção e Quantificação de Microhemorragias Cerebrais com Base em Imagens de Ressonância Magnética Ponderadas por Suscetibilidade Magnética

As microhemorragias cerebrais (CMBs) têm uma função importante no desenvolvimento de hemorragias intracerebrais (ICH) e doenças cerebrovasculares. Estas microestruturas surgem devido ao sangramento perivascular dos pequenos vasos, principalmente afetados por vasculopatia hipertensiva e angiopatia amilóide cerebral, que consistem na forma esporádica da doença dos pequenos vasos (SVD) cerebrais. Esta patologia consiste na segunda maior causa de demência, que por sua vez constituí uma das preocupações a nível global que afeta sobretudo a população idosa. Para que seja possível o diagnóstico precoce, bem como a monitorização da progressão da SVD existe a necessidade do desenvolvimento de um protocolo na prática clínica que agilize o processo de deteção e quantificação de forma automática, rápida e eficiente destes biomarcadores imagiológicos (CMBs) em pacientes com SVD. O processo de inspeção visual de CMBs é na maioria das vezes impraticável em exames de rotina, dado que é bastante demorado. Uma das modalidades de ressonância magnética (RM) com grande potencial para a deteção de CMBs é a imagem ponderada por suscetibilidade magnética (SWI), cuja influência de diversos fatores na quantificação de CMBs ainda necessitam de ser explorados. Assim sendo, nesta tese propôs-se averiguar o potencial de técnicas avançadas de RM, a fim de detetar as CMBs presentes na SVD, incluindo o estudo sistemático de várias opções de préprocessamento das imagens SWI, através da manipulação das máscaras de fase positiva, negativa e sigmóide. Para além da apreciação visual das máscaras de fase procedeu-se à avaliação de algoritmos de aprendizagem automática para a deteção das CMBs. Deste estudo, conclui-se que as imagens SWI pertencentes ao conjunto de dados previamente adquirido podem surgir devido à multiplicação da máscara positiva com a imagem de magnitude quatro vezes. A máscara de fase que proporciona o aumento da sensibilidade na deteção de CMBs é a máscara positiva através de oito multiplicações

Cerebrovascular dysfunction in cerebral small vessel disease

INTRODUCTION: Cerebral small vessel disease (SVD) is the cause of a quarter of all ischaemic strokes and is postulated to have a role in up to half of all dementias. SVD pathophysiology remains unclear but cerebrovascular dysfunction may be important. If confirmed many licensed medications have mechanisms of action targeting vascular function, potentially enabling new treatments via drug repurposing. Knowledge is limited however, as most studies assessing cerebrovascular dysfunction are small, single centre, single imaging modality studies due to the complexities in measuring cerebrovascular dysfunctions in humans. This thesis describes the development and application of imaging techniques measuring several cerebrovascular dysfunctions to investigate SVD pathophysiology and trial medications that may improve small blood vessel function in SVD. METHODS: Participants with minor ischaemic strokes were recruited to a series of studies utilising advanced MRI techniques to measure cerebrovascular dysfunction. Specifically MRI scans measured the ability of different tissues in the brain to change blood flow in response to breathing carbon dioxide (cerebrovascular reactivity; CVR) and the flow and pulsatility through the cerebral arteries, venous sinuses and CSF spaces. A single centre observational study optimised and established feasibility of the techniques and tested associations of cerebrovascular dysfunctions with clinical and imaging phenotypes. Then a randomised pilot clinical trial tested two medications’ (cilostazol and isosorbide mononitrate) ability to improve CVR and pulsatility over a period of eight weeks. The techniques were then expanded to include imaging of blood brain barrier permeability and utilised in multi-centre studies investigating cerebrovascular dysfunction in both sporadic and monogenetic SVDs. RESULTS: Imaging protocols were feasible, consistently being completed with usable data in over 85% of participants. After correcting for the effects of age, sex and systolic blood pressure, lower CVR was associated with higher white matter hyperintensity volume, Fazekas score and perivascular space counts. Lower CVR was associated with higher pulsatility of blood flow in the superior sagittal sinus and lower CSF flow stroke volume at the foramen magnum. Cilostazol and isosorbide mononitrate increased CVR in white matter. The CVR, intra-cranial flow and pulsatility techniques, alongside blood brain barrier permeability and microstructural integrity imaging were successfully employed in a multi-centre observational study. A clinical trial assessing the effects of drugs targeting blood pressure variability is nearing completion. DISCUSSION: Cerebrovascular dysfunction in SVD has been confirmed and may play a more direct role in disease pathogenesis than previously established risk factors. Advanced imaging measures assessing cerebrovascular dysfunction are feasible in multi-centre studies and trials. Identifying drugs that improve cerebrovascular dysfunction using these techniques may be useful in selecting candidates for definitive clinical trials which require large sample sizes and long follow up periods to show improvement against outcomes of stroke and dementia incidence and cognitive function