Automatic classification of dopamine transporter SPECT: deep convolutional neural networks can be trained to be robust with respect to variable image characteristics

Wenzel, Markus; Milletarì, Fausto; Krüger, Julia; Lange, Catharina; Schenk, Michael; Apostolova, Ivayla; Klutmann, Susanne; Ehrenburg, Marcus; Buchert, Ralph

doi:10.1007/s00259-019-04502-5

Cited by 40 publications

(26 citation statements)

References 52 publications

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“…SBR analysis is sensitive to site-and/or camera-specific variability of SPECT image characteristics caused by differences in acquisition and reconstruction protocols, which limits sharing of normal databases and SBR cutoff values between sites and/or cameras [5,[11][12][13][14][15][16][17][18]. In prospective studies, this problem can be addressed by harmonization of acquisition protocols and centralized image reconstruction in an imaging core lab [15][16][17]19].…”

Section: Introductionmentioning

confidence: 99%

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Impact of the size of the normal database on the performance of the specific binding ratio in dopamine transporter SPECT

et al. 2020

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Background: This study investigated the impact of the size of the normal database on the classification performance of the specific binding ratio (SBR) in dopamine transporter (DAT) SPECT with [ 123 I]FP-CIT in different settings. Methods: The first subject sample comprised 645 subjects from the Parkinson's Progression Marker Initiative (PPMI), 207 healthy controls (HC), and 438 Parkinson's disease (PD) patients. The second sample comprised 372 patients from clinical routine patient care, 186 with non-neurodegenerative parkinsonian syndrome (PS) and 186 with neurodegenerative PS. Single-photon emission computed tomography (SPECT) images of the clinical sample were reconstructed with two different reconstruction algorithms (filtered backprojection, iterative ordered subsets expectation maximization (OSEM) reconstruction with resolution recovery). The putaminal specific binding ratio (SBR) was computed using an anatomical region of interest (ROI) predefined in standard (MNI) space in the Automated Anatomic Labeling (AAL) atlas or using hottest voxels (HV) analysis in large predefined ROIs. SBR values were transformed to z-scores using mean and standard deviation of the SBR in a normal database of varying sizes (n = 5, 10, 15,…, 50) randomly selected from the HC subjects (PPMI sample) or the patients with non-neurodegenerative PS (clinical sample). Accuracy, sensitivity, and specificity for identifying patients with PD or neurodegenerative PS were determined as performance measures using a predefined fixed cutoff on the z-score. This was repeated for 10,000 randomly selected normal databases, separately for each size of the normal database. Mean and 5th percentile of the performance measures over the 10,000 realizations were computed. Accuracy, sensitivity, and specificity when using the whole set of HC or non-neurodegenerative PS subjects as normal database were used as benchmark.

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Section: Introductionmentioning

confidence: 99%

“…More complex methods including convolutional neural networks have been proposed for automatic classification of FP-CIT SPECT [18,20,21]. However, conventional SBR analysis is still widely used because it is easy to understand (no black box) and achieves high accuracy provided that an appropriate normal database is used.…”

Section: Introductionmentioning

confidence: 99%

Impact of the size of the normal database on the performance of the specific binding ratio in dopamine transporter SPECT

et al. 2020

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“…These regions were used as an input for the Fig. 4 CNN architectures applied for PD diagnosis using PET and SPECT images [33,119] IET Image Process. © The Institution of Engineering and Technology 2020 CNN algorithms.…”

Section: From Hand-crafted ML Methods To Dlmentioning

confidence: 99%

“…Fig. 4 shows two CNN examples applied for PD diagnosis [33, 119]. Choi et al [33] developed in 2017, a CNN architecture (PD Net) to identify PD patients using FP‐CIT SPECT images.…”

Section: Ml/dl In Pd Diagnosismentioning

confidence: 99%

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Machine learning and deep learning for clinical data and PET/SPECT imaging in Parkinson's disease: a review

2020

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Machine Learning (ML) is a subfield of Artificial Intelligence (AI) that is increasingly applied to several medical diagnosis tasks, including a wide range of diseases. Importantly, various ML models were developed to address the complexity of Parkinson's Disease (PD) diagnosis. PD is a neurodegenerative disease characterized by motor and non-motor disorders where its syndromes affect the daily lives of patients. Several Computer Aided Diagnosis and Detection (CADD) systems based on hand-crafted ML algorithms achieved promising results in distinguishing PD patients from Healthy Control (HC) subjects and other Parkinsonian syndrome categories using clinical data (e.g., speech and gait impairments) and medical imaging [e.g., Position Emission Tomography (PET) and Single Photon Emission Computed Tomography (SPECT)]. Despite the good performance of hand-crafted ML algorithms, there is still a problem linked to the features' extraction and selection. In fact, Deep Learning DL has provided an ultimate solution for the features' extraction and selection related issue. An important number of studies on the diagnosis of PD using DL algorithms were developed recently. This study provides an overview of the application of hand-crafted ML algorithms and DL techniques for PD diagnosis. It also introduces key concepts for understanding the application of ML methods to diagnose PD.

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Computer-Aided Diagnosis of Parkinson’s Disease, Based on SPECT Scans of the Dopamine Transporter

Oliveira

Costa

2020

PET and SPECT in Neurology

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Automatic classification of dopamine transporter SPECT: deep convolutional neural networks can be trained to be robust with respect to variable image characteristics

Cited by 40 publications

References 52 publications

Impact of the size of the normal database on the performance of the specific binding ratio in dopamine transporter SPECT

Impact of the size of the normal database on the performance of the specific binding ratio in dopamine transporter SPECT

Machine learning and deep learning for clinical data and PET/SPECT imaging in Parkinson's disease: a review

Computer-Aided Diagnosis of Parkinson’s Disease, Based on SPECT Scans of the Dopamine Transporter

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