Publications by authors named "Suhuai Luo"

15 Publications

  • Page 1 of 1

Deep sequence modelling for Alzheimer's disease detection using MRI.

Comput Biol Med 2021 07 1;134:104537. Epub 2021 Jun 1.

School of Electrical Engineering and Computing, The University of Newcastle, NSW 2308, Australia.

Background: Alzheimer's disease (AD) is one of the deadliest diseases in developed countries. Treatments following early AD detection can significantly delay institutionalisation and extend patients' independence. There has been a growing focus on early AD detection using artificial intelligence. Convolutional neural networks (CNNs) have proven revolutionary for image-based applications and have been applied to brain scans. In recent years, studies have utilised two-dimensional (2D) CNNs on magnetic resonance imaging (MRI) scans for AD detection. To apply a 2D CNN on three-dimensional (3D) MRI volumes, each MRI scan is split into 2D image slices. A CNN is trained over the image slices by calculating a loss function between each subject's label and each image slice's predicted output. Although 2D CNNs can discover spatial dependencies in an image slice, they cannot understand the temporal dependencies among 2D image slices in a 3D MRI volume. This study aims to resolve this issue by modelling the sequence of MRI features produced by a CNN with deep sequence-based networks for AD detection.

Method: The CNN utilised in this paper was ResNet-18 pre-trained on an ImageNet dataset. The employed sequence-based models were the temporal convolutional network (TCN) and different types of recurrent neural networks. Several deep sequence-based models and configurations were implemented and compared for AD detection.

Results: Our proposed TCN model achieved the best classification performance with 91.78% accuracy, 91.56% sensitivity and 92% specificity.

Conclusion: Our results show that applying sequence-based models can improve the classification accuracy of 2D and 3D CNNs for AD detection by up to 10%.
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http://dx.doi.org/10.1016/j.compbiomed.2021.104537DOI Listing
July 2021

Convolutional neural networks for Alzheimer's disease detection on MRI images.

J Med Imaging (Bellingham) 2021 Mar 29;8(2):024503. Epub 2021 Apr 29.

The University of Newcastle, School of Electrical Engineering and Computing, Callaghan, New South Wales, Australia.

Detection of Alzheimer's disease (AD) on magnetic resonance imaging (MRI) using convolutional neural networks (CNNs), which is useful for detecting AD in its preliminary states. Our study implements and compares several deep models and configurations, including two-dimensional (2D) and three-dimensional (3D) CNNs and recurrent neural networks (RNNs). To use a 2D CNN on 3D MRI volumes, each MRI scan is split into 2D slices, neglecting the connection among 2D image slices in an MRI volume. Instead, a CNN model could be followed by an RNN in a way that the model of 2D CNN + RNN can understand the connection among sequences of 2D image slices for an MRI. The issue is that the feature extraction step in the 2D CNN is independent of classification in the RNN. To tackle this, 3D CNNs can be employed instead of 2D CNNs to make voxel-based decisions. Our study's main contribution is to introduce transfer learning from a dataset of 2D images to 3D CNNs. The results on our MRI dataset indicate that sequence-based decisions improve the accuracy of slice-based decisions by 2% in classifying AD patients from healthy subjects. Also the 3D voxel-based method with transfer learning outperforms the other methods with 96.88% accuracy, 100% sensitivity, and 94.12% specificity. Several implementations and experiments using CNNs on MRI scans for AD detection demonstrated that the voxel-based method with transfer learning from ImageNet to MRI datasets using 3D CNNs considerably improved the results compared with the others.
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http://dx.doi.org/10.1117/1.JMI.8.2.024503DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8083897PMC
March 2021

Automated detection of pneumoconiosis with multilevel deep features learned from chest X-Ray radiographs.

Comput Biol Med 2021 02 21;129:104125. Epub 2020 Nov 21.

Quantitative Imaging, CSIRO Data61, Marsfield, Sydney, NSW, 2122, Australia. Electronic address:

Early detection of pneumoconiosis in X-Rays has been a challenging task that leads to high inter- and intra-reader variability. Motivated by the success of deep learning in general and medical image classification, this paper proposes an approach to automatically detect pneumoconiosis using a deep feature based binary classifier. The features are extracted from X-rays using deep transfer learning, comprising both low and high-level feature sets. For this, a CNN model pre-trained with a transfer learning from a CheXNet model was initially used to extract deep features from the X-Ray images, then the deep features were mapped to higher-dimensional feature spaces for classification using Support Vector Machine (SVM) and CNN based feature aggregation methods. In order to cross validate the proposed method, the training and testing images were randomly split into three folds before each experiment. Nine evaluation metrics were employed to compare the performance of the proposed method and state-of-the-art methods from the literature that used the same datasets. The experimental results show that the proposed framework outperformed others, achieving an accuracy of 92.68% in the automated detection of pneumoconiosis.
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http://dx.doi.org/10.1016/j.compbiomed.2020.104125DOI Listing
February 2021

A comprehensive dataset for bibliometric analysis of SARS and coronavirus impact on social sciences.

Data Brief 2020 Dec 14;33:106520. Epub 2020 Nov 14.

Department of Computer Science, University of Engineering and Technology, Lahore 54890, Pakistan.

The year 2020 has changed the living style of people all around the world. Corona pandemic has affected the people in all fields of life economically, physically, and mentally. This dataset is a collection of published articles discussing the effect of COVID and SARS on the social sciences from 2003 to 2020. This dataset collection and analysis highlight the significance and influential aspects, research streams, and themes in this domain. The analysis provides top journals, highly cited articles, mostly used keywords, top affiliation institutes, leading countries based on the citation, potential research streams, a thematic map, and future directions in this area of research. In the future, this dataset will be helpful for every researcher and policymakers to proceed as a starting point to identify the relevant research based on the analysis of 18 years of research in this domain.
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http://dx.doi.org/10.1016/j.dib.2020.106520DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7691717PMC
December 2020

The emerging role of artificial intelligence in multiple sclerosis imaging.

Mult Scler 2020 Oct 28:1352458520966298. Epub 2020 Oct 28.

Hunter Medical Research Institute, New Lambton Heights, NSW, Australia/School of Medicine and Public Health, Faculty of Health and Medicine, The University of Newcastle, Callaghan, NSW, Australia/Department of Neurology, John Hunter Hospital, New Lambton Heights, NSW, Australia.

Background: Computer-aided diagnosis can facilitate the early detection and diagnosis of multiple sclerosis (MS) thus enabling earlier interventions and a reduction in long-term MS-related disability. Recent advancements in the field of artificial intelligence (AI) have led to the improvements in the classification, quantification and identification of diagnostic patterns in medical images for a range of diseases, in particular, for MS. Importantly, data generated using AI techniques are analyzed automatically, which compares favourably with labour-intensive and time-consuming manual methods.

Objective: The aim of this review is to assist MS researchers to understand current and future developments in the AI-based diagnosis and prognosis of MS.

Methods: We will investigate a variety of AI approaches and various classifiers and compare the current state-of-the-art techniques in relation to lesion segmentation/detection and prognosis of disease. After briefly describing the magnetic resonance imaging (MRI) techniques commonly used, we will describe AI techniques used for the detection of lesions and MS prognosis.

Results: We then evaluate the clinical maturity of these AI techniques in relation to MS.

Conclusion: Finally, future research challenges are identified in a bid to encourage further improvements of the methods.
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http://dx.doi.org/10.1177/1352458520966298DOI Listing
October 2020

Deep learning to detect Alzheimer's disease from neuroimaging: A systematic literature review.

Comput Methods Programs Biomed 2020 Apr 27;187:105242. Epub 2019 Nov 27.

The University of Newcastle, University Drive, Callaghan 2308, Australia. Electronic address:

Alzheimer's Disease (AD) is one of the leading causes of death in developed countries. From a research point of view, impressive results have been reported using computer-aided algorithms, but clinically no practical diagnostic method is available. In recent years, deep models have become popular, especially in dealing with images. Since 2013, deep learning has begun to gain considerable attention in AD detection research, with the number of published papers in this area increasing drastically since 2017. Deep models have been reported to be more accurate for AD detection compared to general machine learning techniques. Nevertheless, AD detection is still challenging, and for classification, it requires a highly discriminative feature representation to separate similar brain patterns. This paper reviews the current state of AD detection using deep learning. Through a systematic literature review of over 100 articles, we set out the most recent findings and trends. Specifically, we review useful biomarkers and features (personal information, genetic data, and brain scans), the necessary pre-processing steps, and different ways of dealing with neuroimaging data originating from single-modality and multi-modality studies. Deep models and their performance are described in detail. Although deep learning has achieved notable performance in detecting AD, there are several limitations, especially regarding the availability of datasets and training procedures.
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http://dx.doi.org/10.1016/j.cmpb.2019.105242DOI Listing
April 2020

A Solitary Feature-Based Lung Nodule Detection Approach for Chest X-Ray Radiographs.

IEEE J Biomed Health Inform 2018 03 31;22(2):516-524. Epub 2017 Jan 31.

Lung cancer is one of the most deadly diseases. It has a high death rate and its incidence rate has been increasing all over the world. Lung cancer appears as a solitary nodule in chest x-ray radiograph (CXR). Therefore, lung nodule detection in CXR could have a significant impact on early detection of lung cancer. Radiologists define a lung nodule in CXR as "solitary white nodule-like blob." However, the solitary feature has not been employed for lung nodule detection before. In this paper, a solitary feature-based lung nodule detection method was proposed. We employed stationary wavelet transform and convergence index filter to extract the texture features and used AdaBoost to generate white nodule-likeness map. A solitary feature was defined to evaluate the isolation degree of candidates. Both the isolation degree and the white nodule likeness were used as final evaluation of lung nodule candidates. The proposed method shows better performance and robustness than those reported in previous research. More than 80% and 93% of lung nodules in the lung field in the Japanese Society of Radiological Technology (JSRT) database were detected when the false positives per image were two and five, respectively. The proposed approach has the potential of being used in clinical practice.
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http://dx.doi.org/10.1109/JBHI.2017.2661805DOI Listing
March 2018

A marker-based watershed method for X-ray image segmentation.

Comput Methods Programs Biomed 2014 Mar 7;113(3):894-903. Epub 2014 Jan 7.

Research Lab for Medical Imaging and Digital Surgery, Shenzhen Institutes of Advanced Technology, 1068 Xueyuan Boulevard, University Town of Shenzhen, Shenzhen 518055, PR China. Electronic address:

Digital X-ray images are the most frequent modality for both screening and diagnosis in hospitals. To facilitate subsequent analysis such as quantification and computer aided diagnosis (CAD), it is desirable to exclude image background. A marker-based watershed segmentation method was proposed to segment background of X-ray images. The method consisted of six modules: image preprocessing, gradient computation, marker extraction, watershed segmentation from markers, region merging and background extraction. One hundred clinical direct radiograph X-ray images were used to validate the method. Manual thresholding and multiscale gradient based watershed method were implemented for comparison. The proposed method yielded a dice coefficient of 0.964±0.069, which was better than that of the manual thresholding (0.937±0.119) and that of multiscale gradient based watershed method (0.942±0.098). Special means were adopted to decrease the computational cost, including getting rid of few pixels with highest grayscale via percentile, calculation of gradient magnitude through simple operations, decreasing the number of markers by appropriate thresholding, and merging regions based on simple grayscale statistics. As a result, the processing time was at most 6s even for a 3072×3072 image on a Pentium 4 PC with 2.4GHz CPU (4 cores) and 2G RAM, which was more than one time faster than that of the multiscale gradient based watershed method. The proposed method could be a potential tool for diagnosis and quantification of X-ray images.
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http://dx.doi.org/10.1016/j.cmpb.2013.12.025DOI Listing
March 2014

Predicting the development of mild cognitive impairment: a new use of pattern recognition.

Neuroimage 2012 Apr 25;60(2):894-901. Epub 2012 Jan 25.

School of Design, Communication & Information Technology, University of Newcastle, Callaghan, NSW, Australia.

While the conversion from mild cognitive impairment to Alzheimer's disease has received much recent attention, the transition from normal cognition to mild cognitive impairment is largely unexplored. The present pattern recognition study addressed this by using neuropsychological test scores and neuroimaging morphological measures to predict the later development of mild cognitive impairment in cognitively normal community-dwelling individuals aged 70-90years. A feature selection algorithm chose a subset of neuropsychological and FreeSurfer-derived morphometric features that optimally differentiated between individuals who developed mild cognitive impairment and individuals who remained cognitively normal. Support vector machines were used to train classifiers and test prediction performance, which was evaluated via 10-fold cross-validation to reduce variability. Prediction performance was greater when using a combination of neuropsychological scores and morphological measures than when using either of these alone. Results for the combined method were: accuracy 78.51%, sensitivity 73.33%, specificity 79.75%, and an area under the receiver operating characteristic curve of 0.841. Of all the features investigated, memory performance and measures of the prefrontal cortex and parietal lobe were the most discriminative. Our prediction method offers the potential to detect elderly individuals with apparently normal cognition at risk of imminent cognitive decline. Identification at this stage will facilitate the early start of interventions designed to prevent or slow the development of Alzheimer's disease and other dementias.
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http://dx.doi.org/10.1016/j.neuroimage.2012.01.084DOI Listing
April 2012

Automated detection of amnestic mild cognitive impairment in community-dwelling elderly adults: a combined spatial atrophy and white matter alteration approach.

Neuroimage 2012 Jan 16;59(2):1209-17. Epub 2011 Aug 16.

School of Design, Communication & Information Technology, University of Newcastle, Callaghan, NSW, Australia.

Amnestic mild cognitive impairment (aMCI) is a syndrome widely considered to be prodromal Alzheimer's disease. Accurate diagnosis of aMCI would enable earlier treatment, and could thus help minimize the prevalence of Alzheimer's disease. The aim of the present study was to evaluate a magnetic resonance imaging-based automated classification schema for identifying aMCI. This was carried out in a sample of community-dwelling adults aged 70-90 years old: 79 with a clinical diagnosis of aMCI and 204 who were cognitively normal. Our schema was novel in using measures of both spatial atrophy, derived from T1-weighted images, and white matter alterations, assessed with diffusion tensor imaging (DTI) tract-based spatial statistics (TBSS). Subcortical volumetric features were extracted using a FreeSurfer-initialized Large Deformation Diffeomorphic Metric Mapping (FS+LDDMM) segmentation approach, and fractional anisotropy (FA) values obtained for white matter regions of interest. Features were ranked by their ability to discriminate between aMCI and normal cognition, and a support vector machine (SVM) selected an optimal feature subset that was used to train SVM classifiers. As evaluated via 10-fold cross-validation, the classification performance characteristics achieved by our schema were: accuracy, 71.09%; sensitivity, 51.96%; specificity, 78.40%; and area under the curve, 0.7003. Additionally, we identified numerous socio-demographic, lifestyle, health and other factors potentially implicated in the misclassification of individuals by our schema and those previously used by others. Given its high level of performance, our classification schema could facilitate the early detection of aMCI in community-dwelling elderly adults.
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http://dx.doi.org/10.1016/j.neuroimage.2011.08.013DOI Listing
January 2012

Identification of conversion from mild cognitive impairment to Alzheimer's disease using multivariate predictors.

PLoS One 2011 21;6(7):e21896. Epub 2011 Jul 21.

School of Design, Communication and Information Technology, University of Newcastle, Newcastle, New South Wales, Australia.

Prediction of conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is of major interest in AD research. A large number of potential predictors have been proposed, with most investigations tending to examine one or a set of related predictors. In this study, we simultaneously examined multiple features from different modalities of data, including structural magnetic resonance imaging (MRI) morphometry, cerebrospinal fluid (CSF) biomarkers and neuropsychological and functional measures (NMs), to explore an optimal set of predictors of conversion from MCI to AD in an Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. After FreeSurfer-derived MRI feature extraction, CSF and NM feature collection, feature selection was employed to choose optimal subsets of features from each modality. Support vector machine (SVM) classifiers were then trained on normal control (NC) and AD participants. Testing was conducted on MCIc (MCI individuals who have converted to AD within 24 months) and MCInc (MCI individuals who have not converted to AD within 24 months) groups. Classification results demonstrated that NMs outperformed CSF and MRI features. The combination of selected NM, MRI and CSF features attained an accuracy of 67.13%, a sensitivity of 96.43%, a specificity of 48.28%, and an AUC (area under curve) of 0.796. Analysis of the predictive values of MCIc who converted at different follow-up evaluations showed that the predictive values were significantly different between individuals who converted within 12 months and after 12 months. This study establishes meaningful multivariate predictors composed of selected NM, MRI and CSF measures which may be useful and practical for clinical diagnosis.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0021896PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3140993PMC
December 2011

The relationship between cortical sulcal variability and cognitive performance in the elderly.

Neuroimage 2011 Jun 21;56(3):865-73. Epub 2011 Mar 21.

School of Design, Communication & IT, The University of Newcastle, Callaghan, NSW, Australia.

The relationship between cognitive functions and brain structure has been of long-standing research interest. Most previous research has attempted to relate cognition to volumes of specific brain structures or thickness of cortical regions, with relatively few studies examining other features such as cortical surface anatomy. In this study, we examine the relationship between cortical sulcal features and cognitive function in a sample (N=316) of community-dwelling subjects aged between 70 and 90 years (mean=78.06±4.75; male/female=130/186) who had detailed neuropsychological assessments and brain MRI scans. Using automated methods on 3D T1-weighted brain scans, we computed global sulcal indices (g-SIs) of the whole brain and average sulcal spans of five prominent sulci. The g-SI, which reflects the complexity of sulcal folds across the cerebral hemispheres, showed a significant positive correlation with performance in most cognitive domains including attention/processing speed, memory, language and executive function. Regionally, a negative correlation was found between some cognitive functions and sulcal spans, i.e. poorer cognitive performance was associated with a wider sulcal span. Of the five cognitive domains examined, the performance of processing speed was found to be correlated with the spans of most sulci, with the strongest correlation being with the superior temporal sulcus. Memory did not show a significant correlation with any individual sulcal index, after correcting for age and sex. Of the five sulci measured, the left superior temporal sulcus showed the highest sensitivity, with significant correlations with performances in all cognitive domains except memory, after controlling for age, sex, years of education and brain size. The results suggest that regionally specific sulcal morphology is associated with cognitive function in elderly individuals.
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http://dx.doi.org/10.1016/j.neuroimage.2011.03.015DOI Listing
June 2011

The effects of age and sex on cortical sulci in the elderly.

Neuroimage 2010 May 13;51(1):19-27. Epub 2010 Feb 13.

School of Design, Communication & I.T., the University of Newcastle, and Neuropsychiatric Institute, Prince of Wales Hospital, Randwick, NSW, Australia.

A large number of structural brain studies using magnetic resonance imaging (MRI) have reported age-related cortical changes and sex difference in brain morphology. Most studies have focused on cortical thickness or density, with relatively few studies of cortical sulcal features, especially in the elderly. In this paper, we report global sulcal indices (g-SIs) of both cerebral hemispheres and the average sulcal span in six prominent sulci, as observed in T1-weighted scans obtained from a large community cohort of 319 non-demented individuals aged between 70 and 90 years (mean=78.06+/-4.75; male/female=149/170), using automated methods. Our results showed that for both hemispheres, g-SIs had significant negative correlations with age in both men and women. Using an interactive effect analysis, we found that g-SIs for men declined faster with age than that for women. The widths of all six sulcal spans increased significantly with age, with largest span increase occurring in the superior frontal sulcus. Compared to women, men had significantly wider sulcal spans for all sulci that were examined. Our findings suggest that both age and sex contribute to significant cortical gyrification differences and variations in the elderly. This study establishes a reference for future studies of age-related brain changes and neurodegenerative diseases in the elderly.
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http://dx.doi.org/10.1016/j.neuroimage.2010.02.016DOI Listing
May 2010

Sensor data fusion for accurate cloud presence prediction using Dempster-Shafer evidence theory.

Sensors (Basel) 2010 18;10(10):9384-96. Epub 2010 Oct 18.

CSIRO ICT Centre, Corner of Vimiera and Pembroke Roads, Marsfield, NSW 2122; Australia.

Sensor data fusion technology can be used to best extract useful information from multiple sensor observations. It has been widely applied in various applications such as target tracking, surveillance, robot navigation, signal and image processing. This paper introduces a novel data fusion approach in a multiple radiation sensor environment using Dempster-Shafer evidence theory. The methodology is used to predict cloud presence based on the inputs of radiation sensors. Different radiation data have been used for the cloud prediction. The potential application areas of the algorithm include renewable power for virtual power station where the prediction of cloud presence is the most challenging issue for its photovoltaic output. The algorithm is validated by comparing the predicted cloud presence with the corresponding sunshine occurrence data that were recorded as the benchmark. Our experiments have indicated that comparing to the approaches using individual sensors, the proposed data fusion approach can increase correct rate of cloud prediction by ten percent, and decrease unknown rate of cloud prediction by twenty three percent.
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http://dx.doi.org/10.3390/s101009384DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3230941PMC
August 2012

Extraction of brain vessels from magnetic resonance angiographic images: concise literature review, challenges, and proposals.

Conf Proc IEEE Eng Med Biol Soc 2005;2005:1422-5

The School of Design, Communication & I.T., The University of Newcastle.

The automated extraction of brain vessels from magnetic resonance angiography (MRA) has found its applications in vascular disease diagnosis, endovascular operation and neurosurgical planning. In this paper we first present a concise technical review on cerebral vasculature extraction from MRA. It reveals the latest development in the area of vessel extraction. Then we detail the main challenges to the researchers working in the vessel extraction and segmentation area. Based on the review and our experience in the area, we finally present our proposals on ways of developing robust vessel extracting algorithm. Examples of brain vasculature extracted with advanced hybrid approach are shown. Twenty one references are given.
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http://dx.doi.org/10.1109/IEMBS.2005.1616697DOI Listing
October 2012
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