Multi-View Separable Pyramid Network for AD Prediction at MCI Stage by <sup>18</sup>F-FDG Brain PET Imaging

Pan, Xiaoxi; Phan, Trong-Le; Adel, Mouloud; Fossati, Caroline; Gaidon, Thierry; Wojak, Julien; Guedj, Éric

doi:10.1109/tmi.2020.3022591

Cited by 78 publications

(27 citation statements)

References 49 publications

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“…As an emerging technique for image quantitative analysis, the DLR method represents a combination and development of DL and radiomics. The DLR method can automatically learn a large number of features including a neural network's hidden layers according to input images, and this process do not require object segmentation and hard-coded feature extraction (Lu et al, 2018b ; Basaia et al, 2019 ; Spasov et al, 2019a ; Roy et al, 2020 ; Yee et al, 2020 ; Pan et al, 2021 ). This has been successfully applied to oncology and cancer diagnosis at the present (Han et al, 2017 ; Deepak and Ameer, 2019 ; Jeyaraj and Samuel Nadar, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

“…In this study, a small number of subjects were collected only from the ADNI database. Although the final DLR+C model performed excellent diagnostic performance, there is still potential to improve the representation of our Base DL model, where the accuracy only reached 74.12% in the independent test group and did not exceed those of Pan et al (2021) and Lu et al (2018a). Therefore, it is possible to improve the performance of our DLR+C method when comprehensive and homogeneous databases are developed and become available.…”

Section: Limitationsmentioning

confidence: 99%

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Deep-Learning Radiomics for Discrimination Conversion of Alzheimer's Disease in Patients With Mild Cognitive Impairment: A Study Based on 18F-FDG PET Imaging

Zhou

Zeng

Lun

et al. 2021

Front. Aging Neurosci.

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Objectives: Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder and the most common form of dementia in the older people. Some types of mild cognitive impairment (MCI) are the clinical precursors of AD, while other MCI forms tend to remain stable over time and do not progress to AD. To discriminate MCI patients at risk of AD from stable MCI, we propose a novel deep-learning radiomics (DLR) model based on 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) images and combine DLR features with clinical parameters (DLR+C) to improve diagnostic performance.Methods:18F-fluorodeoxyglucose positron emission tomography (PET) data from the Alzheimer's disease Neuroimaging Initiative database (ADNI) were collected, including 168 patients with MCI who converted to AD within 3 years and 187 patients with MCI without conversion within 3 years. These subjects were randomly partitioned into 90 % for the training/validation group and 10 % for the independent test group. The proposed DLR approach consists of three steps: base DL model pre-training, network features extraction, and integration of DLR+C, where a convolution network serves as a feature encoder, and a support vector machine (SVM) operated as the classifier. In comparative experiments, we compared our DLR+C method with four other methods: the standard uptake value ratio (SUVR) method, Radiomics-ROI method, Clinical method, and SUVR + Clinical method. To guarantee the robustness, 10-fold cross-validation was processed 100 times.Results: Under the DLR model, our proposed DLR+C was advantageous and yielded the best classification performance in the diagnosis of conversion with the accuracy, sensitivity, and specificity of 90.62 ± 1.16, 87.50 ± 0.00, and 93.39 ± 2.19%, respectively. In contrast, the respective accuracy of the other four methods reached 68.38 ± 1.27, 73.31 ± 6.93, 81.09 ± 1.97, and 85.35 ± 0.72 %. These results suggested the DLR approach could be used successfully in the prediction of conversion to AD, and that our proposed DLR-combined clinical information was effective.Conclusions: This study showed DLR+C could provide a novel and valuable method for the computer-assisted diagnosis of conversion to AD from MCI. This DLR+C method provided a quantitative biomarker which could predict conversion to AD in MCI patients.

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Section: Discussionmentioning

confidence: 99%

Section: Limitationsmentioning

confidence: 99%

Deep-Learning Radiomics for Discrimination Conversion of Alzheimer's Disease in Patients With Mild Cognitive Impairment: A Study Based on 18F-FDG PET Imaging

Zhou

Zeng

Lun

et al. 2021

Front. Aging Neurosci.

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show abstract

“…To better evaluate the performance of the proposed method and state-of-the-art methods, 4 technical indexes [ 20 ] are employed for evaluation, including accuracy (ACC), sensitivity (SEN), speciﬁcity (SPE), and AUC (area under ROC curve). The ACC, SEN, and SPE are the proportion of correct predictions among all samples, positive samples, and negative samples, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…Yee et al [ 19 ] designed a 3D CNN-based network with residual connections for AD diagnosis, and class activation maps implicate many known regions affected by AD. Pan et al [ 20 ] developed a multiview separable pyramid network-based classification model for AD prediction, in which the features are extracted from axial, coronal, and sagittal views of PET scans with the 3D CNN framework.…”

Section: Introductionmentioning

confidence: 99%

Automatic Diagnosis of Alzheimer’s Disease and Mild Cognitive Impairment Based on CNN + SVM Networks with End‐to‐End Training

Huang

Sun

Guo

2021

Computational Intelligence and Neuroscience

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Alzheimer’s disease (AD) is an irreversible neurodegenerative disease, and, at present, once it has been diagnosed, there is no effective curative treatment. Accurate and early diagnosis of Alzheimer’s disease is crucial for improving the condition of patients since effective preventive measures can be taken in advance to delay the onset time of the disease. 18F-Fluorodeoxyglucose positron emission tomography (18F-FDG PET : PET) is an effective biomarker of the symptom of AD and has been used as medical imaging data for diagnosing AD. Mild cognitive impairment (MCI) is regarded as an early symptom of AD, and it has been shown that MCI also has a certain biomedical correlation with PET. In this paper, we explore how to use 3D PET images to realize the effective recognition of MCI and thus achieve the early prediction of AD. This problem is then taken as the classification of three categories of PET images, including MCI, AD, and NC (normal controls). In order to get better classification performance, a novel network model is proposed in the paper based on 3D convolution neural networks (CNN) and support vector machines (SVM) by utilizing both the excellent abilities of CNN in feature extraction and SVM in classification. In order to make full use of the optimal property of SVM in solving binary classification problems, the three-category classification problem is divided into three binary classifications, and each binary classification is being realized with a CNN + SVM network. Then, the outputs of the three CNN + SVM networks are fused into a final three-category classification result. An end-to-end learning algorithm is developed to train the CNN + SVM networks, and a decision fusion algorithm is exploited to realize the fusion of the outputs of three CNN + SVM networks. Experimental results obtained in the work with comparative analyses confirm the effectiveness of the proposed method.

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“…To better evaluate the performance of the proposed method and state-of-the-art methods, 4 technical indexes [20] are employed for evaluation, including accuracy (ACC), sensitivity (SEN), specificity (SPE), and AUC (area under ROC curve). e ACC, SEN, and SPE are the proportion of correct predictions among all samples, positive samples, and negative samples, respectively.…”

Section: Implementation Settings and Evaluation Indexesmentioning

confidence: 99%

Automatic Diagnosis of Alzheimer’s Disease and Mild Cognitive Impairment Based on CNN+SVM Networks with End-to-end Training

Sun

Huang

Guo

2021

2021 13th International Conference on Advanced Computational Intelligence (ICACI)

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Alzheimer's disease (AD) is an irreversible neurodegenerative disease, and, at present, once it has been diagnosed, there is no effective curative treatment. Accurate and early diagnosis of Alzheimer's disease is crucial for improving the condition of patients since effective preventive measures can be taken in advance to delay the onset time of the disease. 18 F-Fluorodeoxyglucose positron emission tomography ( 18 F-FDG PET : PET) is an effective biomarker of the symptom of AD and has been used as medical imaging data for diagnosing AD. Mild cognitive impairment (MCI) is regarded as an early symptom of AD, and it has been shown that MCI also has a certain biomedical correlation with PET. In this paper, we explore how to use 3D PET images to realize the effective recognition of MCI and thus achieve the early prediction of AD. is problem is then taken as the classification of three categories of PET images, including MCI, AD, and NC (normal controls). In order to get better classification performance, a novel network model is proposed in the paper based on 3D convolution neural networks (CNN) and support vector machines (SVM) by utilizing both the excellent abilities of CNN in feature extraction and SVM in classification. In order to make full use of the optimal property of SVM in solving binary classification problems, the three-category classification problem is divided into three binary classifications, and each binary classification is being realized with a CNN + SVM network. en, the outputs of the three CNN + SVM networks are fused into a final three-category classification result. An end-to-end learning algorithm is developed to train the CNN + SVM networks, and a decision fusion algorithm is exploited to realize the fusion of the outputs of three CNN + SVM networks. Experimental results obtained in the work with comparative analyses confirm the effectiveness of the proposed method.

show abstract

Multi-View Separable Pyramid Network for AD Prediction at MCI Stage by ¹⁸F-FDG Brain PET Imaging

Cited by 78 publications

References 49 publications

Deep-Learning Radiomics for Discrimination Conversion of Alzheimer's Disease in Patients With Mild Cognitive Impairment: A Study Based on 18F-FDG PET Imaging

Deep-Learning Radiomics for Discrimination Conversion of Alzheimer's Disease in Patients With Mild Cognitive Impairment: A Study Based on 18F-FDG PET Imaging

Automatic Diagnosis of Alzheimer’s Disease and Mild Cognitive Impairment Based on CNN + SVM Networks with End‐to‐End Training

Automatic Diagnosis of Alzheimer’s Disease and Mild Cognitive Impairment Based on CNN+SVM Networks with End-to-end Training

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Multi-View Separable Pyramid Network for AD Prediction at MCI Stage by 18F-FDG Brain PET Imaging

Cited by 78 publications

References 49 publications

Deep-Learning Radiomics for Discrimination Conversion of Alzheimer's Disease in Patients With Mild Cognitive Impairment: A Study Based on 18F-FDG PET Imaging

Deep-Learning Radiomics for Discrimination Conversion of Alzheimer's Disease in Patients With Mild Cognitive Impairment: A Study Based on 18F-FDG PET Imaging

Automatic Diagnosis of Alzheimer’s Disease and Mild Cognitive Impairment Based on CNN + SVM Networks with End‐to‐End Training

Automatic Diagnosis of Alzheimer’s Disease and Mild Cognitive Impairment Based on CNN+SVM Networks with End-to-end Training

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Multi-View Separable Pyramid Network for AD Prediction at MCI Stage by ¹⁸F-FDG Brain PET Imaging