Automated segmentation of basal ganglia and deep brain structures in MRI of Parkinson’s disease

Haegelen, Claire; Coupé, Pierrick; Fonov, Vladimir S.; Guizard, Nicolas; Jannin, Pierre; Morandi, Xavier; Collins, D. Louis

doi:10.1007/s11548-012-0675-8

Cited by 62 publications

(73 citation statements)

References 33 publications

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“…A threedimensional CT brain scan performed a few days later confirmed the position of the electrodes. The pre-and postoperative image segmentation and registration workflow, the anatomical ParkMedAtlis template used as an anatomical reference (Haegelen et al, 2013), and the electrode segmentation were performed by using pyDBS software (D'Albis et al, 2015).…”

Section: Patients and Surgery (Table 1)mentioning

confidence: 99%

Vocal emotion decoding in the subthalamic nucleus: An intracranial ERP study in Parkinson’s disease

et al. 2017

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a b s t r a c tUsing intracranial local field potential (LFP) recordings in patients with Parkinson's disease (PD) undergoing deep brain stimulation (DBS), we explored the electrophysiological activity of the subthalamic nucleus (STN) in response to emotional stimuli in the auditory modality. Previous studies focused on the influence of visual stimuli. To this end, we recorded LFPs within the STN in response to angry, happy, and neutral prosodies in 13 patients with PD who had just undergone implantation of DBS electrodes. We observed specific modulation of the right STN in response to anger and happiness, as opposed to neutral prosody, occurring at around 200-300 ms post-onset, and later at around 850-950 ms post-onset for anger and at around 3250-3350 ms post-onset for happiness. Taken together with previous reports of modulated STN activity in response to emotional visual stimuli, the present results appear to confirm that the STN is involved in emotion processing irrespective of stimulus valence and sensory modality.

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Section: Patients and Surgery (Table 1)mentioning

confidence: 99%

Vocal emotion decoding in the subthalamic nucleus: An intracranial ERP study in Parkinson’s disease

et al. 2017

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“…The patient's images were registered with the ParkMedAtlis anatomical atlas. 5 After a preliminary step of automatic electrode contact segmentation based on image processing, combinations of linear and nonlinear registrations allowed each stimulated contact to be warped into the ParkMedAtlis template. Both the contact segmentation and the registration algorithms had already been validated.…”

Section: Pyramidal Tract Side Effect Model Based On Artificial Neuralmentioning

confidence: 99%

“…Both the contact segmentation and the registration algorithms had already been validated. 5,19 The registration workflow was composed of a linear CT to MRI registration, a global affine MR-to-template transformation, a local affine MR-to-template transformation with a mask on deep structures, and a final nonlinear local registration. Using this procedure, the contact positions could be precisely warped into the ParkMedAtlis template.…”

Section: Pyramidal Tract Side Effect Model Based On Artificial Neuralmentioning

confidence: 99%

“…The procedure consists in implanting deep brain electrodes with an optimal placement and optimal electrical stimulation parameters that alleviate the symptoms while avoiding structures that trigger side effects. 2 To improve targeting, preoperative patient-specific models are built from multimodal medical images 3,4 such as preoperative magnetic resonance images (MRI) registered with an anatomical [5][6][7] or histological atlas. 8,9 A patient-specific model allows neurosurgeons to visualize on a three-dimensional (3-D) virtual representation of the cerebral structures of interest such as basal ganglia.…”

Section: Introductionmentioning

confidence: 99%

“…Our team has carried out an intensive modeling study to design a patient-specific model based on the ParkMedAtlis template, 5 an anatomical atlas specifically designed to fit the anatomy of patients with Parkinson's disease. This common anatomical space has been used to correlate the electrode locations with clinical data.…”

Section: Introductionmentioning

confidence: 99%

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Image-guided preoperative prediction of pyramidal tract side effect in deep brain stimulation: proof of concept and application to the pyramidal tract side effect induced by pallidal stimulation

et al. 2016

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, "Imageguided preoperative prediction of pyramidal tract side effect in deep brain stimulation: proof of concept and application to the pyramidal tract side effect induced by pallidal stimulation," J. Abstract. Deep brain stimulation of the medial globus pallidus (GPm) is a surgical procedure for treating patients suffering from Parkinson's disease. Its therapeutic effect may be limited by the presence of pyramidal tract side effect (PTSE). PTSE is a contraction time-locked to the stimulation when the current spreading reaches the motor fibers of the pyramidal tract within the internal capsule. The objective of the study was to propose a preoperative predictive model of PTSE. A machine learning-based method called PyMAN (PTSE model based on artificial neural network) accounting for the current used in stimulation, the three-dimensional electrode coordinates and the angle of the trajectory, was designed to predict the occurrence of PTSE. Ten patients implanted in the GPm have been tested by a clinician to create a labeled dataset of the stimulation parameters that trigger PTSE. The kappa index value between the data predicted by PyMAN and the labeled data was 0.78. Further evaluation studies are desirable to confirm whether PyMAN could be a reliable tool for assisting the surgeon to prevent PTSE during the preoperative planning.

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Impact of CT reconstruction algorithm on auto‐segmentation performance

Miller

Mittelstaedt²,

Black

et al. 2019

J Applied Clin Med Phys

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Model‐based iterative reconstruction (MBIR) reduces CT imaging dose while maintaining image quality. However, MBIR reduces noise while preserving edges which may impact intensity‐based tasks such as auto‐segmentation. This work evaluates the sensitivity of an auto‐contouring prostate atlas across multiple MBIR reconstruction protocols and benchmarks the results against filtered back projection (FBP). Images were created from raw projection data for 11 prostate cancer cases using FBP and nine different MBIR reconstructions (3 protocols/3 noise reduction levels) yielding 10 reconstructions/patient. Five bony structures, bladder, rectum, prostate, and seminal vesicles (SVs) were segmented using an auto‐segmentation pipeline that renders 3D binary masks for analysis. Performance was evaluated for volume percent difference (VPD) and Dice similarity coefficient (DSC), using FBP as the gold standard. Nonparametric Friedman tests plus post hoc all pairwise comparisons were employed to test for significant differences (P < 0.05) for soft tissue organs and protocol/level combinations. A physician performed qualitative grading of 396 MBIR contours across the prostate, bladder, SVs, and rectum in comparison to FBP using a six‐point scale. MBIR contours agreed with FBP for bony anatomy (DSC ≥ 0.98), bladder (DSC ≥ 0.94, VPD < 8.5%), and prostate (DSC = 0.94 ± 0.03, VPD = 4.50 ± 4.77% (range: 0.07–26.39%). Increased variability was observed for rectum (VPD = 7.50 ± 7.56% and DSC = 0.90 ± 0.08) and SVs (VPD and DSC of 8.23 ± 9.86% range (0.00–35.80%) and 0.87 ± 0.11, respectively). Over the all protocol/level comparisons, a significant difference was observed for the prostate VPD between BSPL1 and BSTL2 (adjusted P‐value = 0.039). Nevertheless, 300 of 396 (75.8%) of the four soft tissue structures using MBIR were graded as equivalent or better than FBP, suggesting that MBIR offered potential improvements in auto‐segmentation performance when compared to FBP. Future work may involve tuning organ‐specific MBIR parameters to further improve auto‐segmentation performance. Running title: Impact of CT Reconstruction Algorithm on Auto‐segmentation Performance.

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Automated segmentation of basal ganglia and deep brain structures in MRI of Parkinson’s disease

Cited by 62 publications

References 33 publications

Vocal emotion decoding in the subthalamic nucleus: An intracranial ERP study in Parkinson’s disease

Vocal emotion decoding in the subthalamic nucleus: An intracranial ERP study in Parkinson’s disease

Image-guided preoperative prediction of pyramidal tract side effect in deep brain stimulation: proof of concept and application to the pyramidal tract side effect induced by pallidal stimulation

Impact of CT reconstruction algorithm on auto‐segmentation performance

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