Automatic substantia nigra segmentation in neuromelanin-sensitive MRI by deep neural network in patients with prodromal and manifest synucleinopathy

Krupička, Radim; Mareček, S; Mala, C.; Lang, M.; Klempíř, Ondřej; Duspivová, Tereza; Široká, R; Jarošíková, T; Keller, Jiří; Šonka, Karel; Růžička, Evžen; Dušek, Petr

doi:10.33549/physiolres.934380

Cited by 17 publications

(22 citation statements)

References 18 publications

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“…Automated methods may improve the reproducibility of segmentation techniques. 74 , 75 , 76 , 77 Manual segmentation has the advantage of allowing careful quality control of images and removal of areas containing artifacts from measurements. Experienced raters can achieve good reproducibility of measurements, as it was in our case, in line with those reported in previous studies.…”

Section: Discussionmentioning

confidence: 99%

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Longitudinal Changes in Neuromelanin MRI Signal in Parkinson's Disease: A Progression Marker

et al. 2021

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A BS TRACT: Background: Development of reliable and accurate imaging biomarkers of dopaminergic cell neurodegeneration is necessary to facilitate therapeutic drug trials in Parkinson's disease (PD). Neuromelanin-sensitive MRI techniques have been effective in detecting neurodegeneration in the substantia nigra pars compacta (SNpc). The objective of the current study was to investigate longitudinal neuromelanin signal changes in the SNpc in PD patients. Methods: In this prospective, longitudinal, observational case-control study, we included 140 PD patients and 64 healthy volunteers divided into 2 cohorts. Cohort I included 99 early PD patients (disease duration, 1.5 AE 1.0 years) and 41 healthy volunteers analyzed at baseline (V1), where 79 PD patients and 32 healthy volunteers were rescanned after 2.0 AE 0.2 years of follow-up (V2). Cohort II included 41 progressing PD patients (disease duration, 9.3 AE 3.7 years) and 23 healthy volunteers at V1, where 30 PD patients were rescanned after 2.4 AE 0.5 years of follow-up. Subjects were scanned at 3 T MRI using 3-dimensional T1-weighted and neuromelanin-sensitive imaging. Regions of interest were delineated manually to calculate SN volumes, volumes corrected by total intracranial volume, signal-to-noise ratio, and contrast-to-noise ratio. Results: Results showed (1) significant reduction in volume and volume corrected by total intracranial volume between visits, greater in progressing PD than nonsignificant changes in healthy volunteers; (2) no significant effects of visit for signal intensity (signal-to-noise ratio);(3) significant interaction in volume between group and visit; (4) greater volume corrected by total intracranial volume at baseline in female patients and greater decrease in volume and increase in the contrast-to-noise ratio in progressing female PD patients compared with male patients; and (5) correlations between neuromelanin SN changes and disease severity and duration. Conclusions: We observed a progressive and measurable decrease in neuromelanin-based SN signal and volume in PD, which might allow a direct noninvasive

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Section: Discussionmentioning

confidence: 99%

“…First, we used manual segmentation to delineate the SN. Automated methods may improve the reproducibility of segmentation techniques 74‐77 . Manual segmentation has the advantage of allowing careful quality control of images and removal of areas containing artifacts from measurements.…”

Section: Discussionmentioning

confidence: 99%

Longitudinal Changes in Neuromelanin MRI Signal in Parkinson's Disease: A Progression Marker

et al. 2021

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“…17,[27][28][29] Despite this breakthrough, the potential has been limited because the medical datasets were relatively small and training any AI model is a difficult task with a relatively small size of datasets. [27][28][29] Here, the Dice coefficient between the automatic and manual method was high, similar to previous studies. 28 We also obtained similar SNc volume decrease between HVs and PD of 26.4% in line with a previous study using U-net model.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning‐Based Neuromelanin MRI Changes of Isolated REM Sleep Behavior Disorder

et al. 2022

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Background: Isolated REM sleep behavior disorder (iRBD) is considered a prodromal stage of parkinsonism. Neurodegenerative changes in the substantia nigra pars compacta (SNc) in parkinsonism can be detected using neuromelanin-sensitive MRI. Objective: To investigate SNc neuromelanin changes in iRBD patients using fully automatic segmentation. Methods: We included 47 iRBD patients, 134 early Parkinson's disease (PD) patients and 55 healthy volunteers (HVs) scanned at 3 Tesla. SNc regions-of-interest were delineated automatically using convolutional neural network. SNc volumes, volumes corrected by total intracranial volume, signal-to-noise ratio (SNR) and contrastto-noise ratio were computed. One-way general linear models (GLM) analysis of covariance (ANCOVA) was conducted while adjusting for age and sex. Results: All SNc measurements differed significantly between the three groups (except SNR in iRBD). Changes in iRBD were intermediate between those in PD and HVs. Conclusions: Using fully automated SNc segmentation method and neuromelanin-sensitive imaging, iRBD patients showed neurodegenerative changes in the SNc at a lower level than in PD patients.

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“…Another application of SN segmentation via CNN has been reported by Krupička et al [99]. Artificial neural networks were also used to validate a dynamic, atlas-based segmentation process of the SN and to quantify NM-rich brainstem structures in PD [100].…”

Section: Radiomics Artificial Intelligence and Future Perspectivesmentioning

confidence: 99%

Imaging of Substantia Nigra in Parkinson’s Disease: A Narrative Review

et al. 2021

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Parkinson’s disease (PD) is a progressive neurodegenerative disorder, characterized by motor and non-motor symptoms due to the degeneration of the pars compacta of the substantia nigra (SNc) with dopaminergic denervation of the striatum. Although the diagnosis of PD is principally based on a clinical assessment, great efforts have been expended over the past two decades to evaluate reliable biomarkers for PD. Among these biomarkers, magnetic resonance imaging (MRI)-based biomarkers may play a key role. Conventional MRI sequences are considered by many in the field to have low sensitivity, while advanced pulse sequences and ultra-high-field MRI techniques have brought many advantages, particularly regarding the study of brainstem and subcortical structures. Nowadays, nigrosome imaging, neuromelanine-sensitive sequences, iron-sensitive sequences, and advanced diffusion weighted imaging techniques afford new insights to the non-invasive study of the SNc. The use of these imaging methods, alone or in combination, may also help to discriminate PD patients from control patients, in addition to discriminating atypical parkinsonian syndromes (PS). A total of 92 articles were identified from an extensive review of the literature on PubMed in order to ascertain the-state-of-the-art of MRI techniques, as applied to the study of SNc in PD patients, as well as their potential future applications as imaging biomarkers of disease. Whilst none of these MRI-imaging biomarkers could be successfully validated for routine clinical practice, in achieving high levels of accuracy and reproducibility in the diagnosis of PD, a multimodal MRI-PD protocol may assist neuroradiologists and clinicians in the early and differential diagnosis of a wide spectrum of neurodegenerative disorders.

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Automatic substantia nigra segmentation in neuromelanin-sensitive MRI by deep neural network in patients with prodromal and manifest synucleinopathy

Cited by 17 publications

References 18 publications

Longitudinal Changes in Neuromelanin MRI Signal in Parkinson's Disease: A Progression Marker

Longitudinal Changes in Neuromelanin MRI Signal in Parkinson's Disease: A Progression Marker

Deep Learning‐Based Neuromelanin MRI Changes of Isolated REM Sleep Behavior Disorder

Imaging of Substantia Nigra in Parkinson’s Disease: A Narrative Review

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