Regions-of-interest based automated diagnosis of Parkinson’s disease using T1-weighted MRI

Rana, Bharti; Juneja, Akanksha; Saxena, Mohit; Gudwani, Sunita; Kumaran, Senthil; Agrawal, R. K.; Behari, Madhuri

doi:10.1016/j.eswa.2015.01.062

Cited by 44 publications

(21 citation statements)

References 32 publications

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“…In literature, not enough work has been suggested for fully CAD of PD from controls using sMRI except (Focke, et al, ; Babu, Suresh, & Mahanand, ; Salvatore, et al, ; Rana, et al, ). The demographic details of controls and PD patients used in the existing methods along with the proposed method, i.e., VV‐GBSFS are mentioned in Table .…”

Section: Resultsmentioning

confidence: 99%

“…In this research work, a reliable computer-aided diagnosis (CAD) of PD using T1-weighted brain MRI is proposed. The proposed method selects a minimal subset of most discriminating features using (Salvatore, et al, 2014) Self acquired a Controls 28 (54%M) 67.50 6 7.10 PD 28 (54%M) 68.20 6 5.00 (Rana, et al, 2015) Self spectral feature selection method based on the concept of graph theory. Support vector machine, a well-known machine learning algorithm, is used to learn a decision model from selected features.…”

Section: Discussionmentioning

confidence: 99%

“…Thus, the extracted features may degrade the performance of a decision model. Recently, the research work (Rana, et al, 2015) also carried out classification of PD patients from controls using SVM to learn a decision model for GM, WM, and cerebrospinal fluid (CSF) separately. In their study, features were constructed from five ROIs individually and in their different combinations.…”

Section: Introductionmentioning

confidence: 99%

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Graph-theory-based spectral feature selection for computer aided diagnosis of Parkinson's disease using T1-weighted MRI

Rana

Juneja

Saxena

et al. 2015

Int. J. Imaging Syst. Technol.

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Introduction: Parkinson's disease (PD) is a neurological disorder, which is diagnosed on the basis of clinical history and examination alone as there are no diagnostic tests available. However, the current diagnosis highly depends on the knowledge and experience of clinicians and hence subjective in nature. Thus, the focus of this study is to develop a computer-aided diagnosis (CAD) method using T1-weighted magnetic resonance imaging (MRI) to differentiate PD from controls. Method: The proposed method utilizes graph-theory-based spectral feature selection method to select a set of discriminating features from whole brain volume. A decision model is built using support vector machine as a classifier with leave-one-out cross-validation scheme. The performance measures, namely, sensitivity, specificity, and classification accuracy, are utilized to evaluate the performance of the decision model. The efficacy of the proposed method is checked on volumetric 3D T1-weighted (1 mm iso-voxel) MRI dataset of 30 PD patients and 30 age and gender matched controls acquired with 1.5T MRI scanner. Results: Experimental results demonstrate that the proposed method is able to differentiate PD from controls with an accuracy of 86.67%, which encourages the use of CAD. The performance of the proposed method outperforms the existing methods except one. In addition, it is observed that the maximum number of selected features belong to caudate region followed by cuneus region. Thus, these regions may be considered as potential biomarkers in diagnosis of PD. Conclusion: The proposed method may be utilized by the clinicians for diagnosis of PD.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Graph-theory-based spectral feature selection for computer aided diagnosis of Parkinson's disease using T1-weighted MRI

Rana

Juneja

Saxena

et al. 2015

Int. J. Imaging Syst. Technol.

Self Cite

View full text Add to dashboard Cite

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“…Recent advances in the areas of machine learning and data-driven analysis have demonstrated the utility of different brain imaging modalities for automated diagnosis of PD. These studies have utilized a host of techniques that include supervised predictive models such as support vector machines (SVMs) (Abos et al, 2017;Amoroso, La Rocca, Monaco, Bellotti, & Tangaro, 2018;Cherubini, Morelli, et al, 2014;Cherubini, Nistico, et al, 2014;Huppertz et al, 2016;Rana et al, 2015;Salvatore et al, 2014) as well as unsupervised models such as self-organizing maps (Peran et al, 2018;Singh & Samavedham, 2015) on data acquired from morphological T1 weighted MRI, functional MRI, diffusion tensor imaging, SPECT, etc. (Adeli et al, 2016;Ariz et al, 2018) and have reported high but variable accuracies.…”

Section: Introductionmentioning

confidence: 99%

Predictive markers for Parkinson’s disease using deep neural nets on neuromelanin sensitive MRI

Shinde

Prasad

Saboo

et al. 2019

Preprint

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Neuromelanin sensitive magnetic resonance imaging (NMS-MRI) has been crucial in identifying abnormalities in the substantia nigra pars compacta (SNc) in Parkinson's disease (PD). PD is characterized by loss of dopaminergic neurons in SNc and current techniques employ estimation of contrast ratios of the SNc visualized on NMS-MRI to discern PD patients from the healthy controls. However, the extraction of these features is time-consuming and laborious and moreover provides lower prediction accuracies. Furthermore, these do not account for patterns of subtle changes in PD in the SNc. To mitigate this, our work establishes a computer-based analysis technique based on convolutional neural networks (CNNs) to create prognostic and diagnostic biomarkers of PD from NMS-MRI. The technique not only performs with a superior cross-validation accuracy (83.7%) as well as testing accuracy (80%) as compared to contrast ratio-based classification (52.7% cross-validation and 56.5% testing accuracy) and radiomics based classifier (81.1% cross-validation and 60.3% testing accuracy); but also locates the most discriminative regions on the neuromelanin contrast images. These discriminative activations demonstrate that the left SNc plays a key role in the classification in comparison to the right SNc, and are in agreement with the concept of asymmetry in PD. Overall, the proposed technique has the potential to support radiological diagnosis of PD while facilitating deeper understanding into the abnormalities in SNc.

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“…In Towey et al (2011) and Segovia et al (2012) two methods based, respectively, on Principal Component Analysis and Partial Least Squares were proposed to extract relevant features from DaTSCAN data. Structural data were also used to assist the diagnosis of PD (Rana et al, 2015), including the separation of PD and APS (Salvatore et al, 2014). However, the validity of DMFP data to feed statistical classification procedures is still poorly covered.…”

Section: Introductionmentioning

confidence: 99%

Multivariate Analysis of 18F-DMFP PET Data to Assist the Diagnosis of Parkinsonism

Segovia

Górriz

Ramı́rez

et al. 2017

Front. Neuroinform.

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An early and differential diagnosis of parkinsonian syndromes still remains a challenge mainly due to the similarity of their symptoms during the onset of the disease. Recently, 18F-Desmethoxyfallypride (DMFP) has been suggested to increase the diagnostic precision as it is an effective radioligand that allows us to analyze post-synaptic dopamine D2/3 receptors. Nevertheless, the analysis of these data is still poorly covered and its use limited. In order to address this challenge, this paper shows a novel model to automatically distinguish idiopathic parkinsonism from non-idiopathic variants using DMFP data. The proposed method is based on a multiple kernel support vector machine and uses the linear version of this classifier to identify some regions of interest: the olfactory bulb, thalamus, and supplementary motor area. We evaluated the proposed model for both, the binary separation of idiopathic and non-idiopathic parkinsonism and the multigroup separation of parkinsonian variants. These systems achieved accuracy rates higher than 70%, outperforming DaTSCAN neuroimages for this purpose. In addition, a system that combined DaTSCAN and DMFP data was assessed.

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Regions-of-interest based automated diagnosis of Parkinson’s disease using T1-weighted MRI

Cited by 44 publications

References 32 publications

Graph-theory-based spectral feature selection for computer aided diagnosis of Parkinson's disease using T1-weighted MRI

Graph-theory-based spectral feature selection for computer aided diagnosis of Parkinson's disease using T1-weighted MRI

Predictive markers for Parkinson’s disease using deep neural nets on neuromelanin sensitive MRI

Multivariate Analysis of 18F-DMFP PET Data to Assist the Diagnosis of Parkinsonism

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