Patch-based label fusion segmentation of brainstem structures with dual-contrast MRI for Parkinson’s disease

Xiao, Yiming; Fonov, Vladimir S.; Bériault, Silvain; Gerard, Ian J.; Sadikot, Abbas F.; Pike, G. Bruce; Collins, D. Louis

doi:10.1007/s11548-014-1119-4

Cited by 19 publications

(13 citation statements)

References 50 publications

(83 reference statements)

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“…Recent multiple atlases-based approaches showed promising results for the localization of the STN (Haegelen et al, 2013; Xiao, Fonov, et al, 2014; Xiao, Jannin, et al, 2014). In these studies, manual labeling was performed, based on the appearance of 3 T MRI, and automatic segmentation of the subcortical structures on the query patient was done by registration between the 3 T MRIs atlas templates and patient.…”

Section: Discussionmentioning

confidence: 99%

“…Some studies show promising results for the visualization of the STN on 3 Tesla (T) MR datasets. Patch based label fusion methods were used for segmenting the STN and its adjacent structures using multimodal 3 T MRIs (Haegelen et al, 2013; Xiao, Fonov, et al, 2014). Xiao, Jannin, et al (2014) analyzed the morphometric variability of the STN obtained by majority voting of labels that were augmented on 3 T MRIs of advanced PD patients.…”

Section: Introductionmentioning

confidence: 99%

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Automatic localization of the subthalamic nucleus on patient‐specific clinical MRI by incorporating 7 T MRI and machine learning: Application in deep brain stimulation

Kim¹,

Duchin²,

Shamir³

et al. 2018

Human Brain Mapping

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Deep brain stimulation (DBS) of the subthalamic nucleus (STN) has shown clinical potential for relieving the motor symptoms of advanced Parkinson’s disease. While accurate localization of the STN is critical for consistent across-patients effective DBS, clear visualization of the STN under standard clinical MR protocols is still challenging. Therefore, intraoperative microelectrode recordings (MER) are incorporated to accurately localize the STN. However, MER require significant neurosurgical expertise and lengthen the surgery time. Recent advances in 7 T MR technology facilitate the ability to clearly visualize the STN. The vast majority of centers, however, still do not have 7 T MRI systems, and fewer have the ability to collect and analyze the data. This work introduces an automatic STN localization framework based on standard clinical MRIs without additional cost in the current DBS planning protocol. Our approach benefits from a large database of 7 T MRI and its clinical MRI pairs. We first model in the 7 T database, using efficient machine learning algorithms, the spatial and geometric dependency between the STN and its adjacent structures (predictors). Given a standard clinical MRI, our method automatically computes the predictors and uses the learned information to predict the patient-specific STN. We validate our proposed method on clinical T2W MRI of 80 subjects, comparing with experts-segmented STNs from the corresponding 7 T MRI pairs. The experimental results show that our framework provides more accurate and robust patient-specific STN localization than using state-of-the-art atlases. We also demonstrate the clinical feasibility of the proposed technique assessing the post-operative electrode active contact locations.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic localization of the subthalamic nucleus on patient‐specific clinical MRI by incorporating 7 T MRI and machine learning: Application in deep brain stimulation

Kim¹,

Duchin²,

Shamir³

et al. 2018

Human Brain Mapping

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“…This technique has recently become more sophisticated. For example, Xiao et al (2014a) and Wang et al (2014d) used this approach to compute local label fusion weights using multi-channel MRI data. Wang et al (2014f) proposed to use the anatomical context to improve the quality of the patch matches.…”

Section: Survey Of Methodological Developmentsmentioning

confidence: 99%

“…Many methods have been developed to parcellate the whole brain, segmenting it into a large number of regions (Aljabar et al, 2008; Babalola et al, 2009; Fonov et al, 2012; Han et al, 2009; Heckemann et al, 2010, 2011; Keihaninejad et al, 2010; Kotrotsou et al, 2014; Svarer et al, 2005; Wang et al, 2012; Ledig et al, 2014), while other studies have focused on small sets or individual ROIs, such as the caudate nucleus (van Rikxoort et al, 2007b); the cerebellum (Park et al, 2014; Van Der Lijn et al, 2012; Weier et al, 2014); the amygdala (Hanson et al, 2012; Klein-Koerkamp et al, 2014); the corpus callosum (Ardekani et al, 2014; Gao et al, 2014; Meyer, 2014); the striatum (Janes et al, 2014); the subthalamic nucleus, red nucleus and substantia nigra (Xiao et al, 2014b,a); the ventricles (Chou et al, 2008; Raamana et al, 2014); and, most notably, the hippocampus, which has attracted much attention due to its association with dementia and Alzheimer’s disease (Akhondi-Asl et al, 2010; Bishop et al, 2010; Clerx et al, 2013; Hammers et al, 2007; Iglesias et al, 2010; Kim et al, 2012; Leung et al, 2010; Pipitone et al, 2014; Pluta et al, 2012; Raamana et al, 2014; van der Lijn et al, 2008; Van Der Lijn et al, 2012; Winston et al, 2013; Wolz et al, 2010b; Yushkevich et al, 2010; Platero et al, 2014; Ta et al, 2014). …”

Section: Survey Of Applicationsmentioning

confidence: 99%

Multi-atlas segmentation of biomedical images: A survey

Iglesias

Sabuncu

2015

Medical Image Analysis

604

457

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Multi-atlas segmentation (MAS), first introduced and popularized by the pioneering work of Rohlfing, Brandt, Menzel and Maurer Jr (2004), Klein, Mensh, Ghosh, Tourville and Hirsch (2005), and Heckemann, Hajnal, Aljabar, Rueckert and Hammers (2006), is becoming one of the most widely-used and successful image segmentation techniques in biomedical applications. By manipulating and utilizing the entire dataset of “atlases” (training images that have been previously labeled, e.g., manually by an expert), rather than some model-based average representation, MAS has the flexibility to better capture anatomical variation, thus offering superior segmentation accuracy. This benefit, however, typically comes at a high computational cost. Recent advancements in computer hardware and image processing software have been instrumental in addressing this challenge and facilitated the wide adoption of MAS. Today, MAS has come a long way and the approach includes a wide array of sophisticated algorithms that employ ideas from machine learning, probabilistic modeling, optimization, and computer vision, among other fields. This paper presents a survey of published MAS algorithms and studies that have applied these methods to various biomedical problems. In writing this survey, we have three distinct aims. Our primary goal is to document how MAS was originally conceived, later evolved, and now relates to alternative methods. Second, this paper is intended to be a detailed reference of past research activity in MAS, which now spans over a decade (2003 – 2014) and entails novel methodological developments and application-specific solutions. Finally, our goal is to also present a perspective on the future of MAS, which, we believe, will be one of the dominant approaches in biomedical image segmentation.

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“…Some studies show promising results for the visualization of the STN for DBS surgery on 3 Tesla (T) MR datasets. Patch based label fusion methods were used for segmenting the STN and its adjacent structures using multimodal 3T MRIs [Haegelen et al, 2013;Xiao et al, 2014a]. Xiao et al [2014b] analyzed the morphometric variability of the STN obtained by a majority voting label that was augmented on 3T MRIs of advanced PD patients.…”

Section: Introductionmentioning

confidence: 99%

Automatic Localization of the Subthalamic Nucleus on Patient-Specific Clinical MRI by Incorporating 7T MRI and Machine Learning: Application in Deep Brain Stimulation

Kim

Duchin

Shamir

et al. 2018

Preprint

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. CC-BY-NC-ND 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/322230 doi: bioRxiv preprint first posted online May. 15, 2018; -3 - Our approach benefits from a large database of 7T MRI and its clinical MRI pairs. We first model in the 7T database, using efficient machine learning algorithms, the spatial and geometric dependency between the STN and its adjacent structures (predictors). Given a standard clinical MRI, our method automatically computes the predictors and uses the learned information to predict the patient-specific STN. We validate our proposed method on clinical T 2 W MRI of 80 subjects, comparing with experts-segmented STNs from the corresponding 7T MRI pairs. The experimental results show that our framework provides more accurate and robust patient-specific STN localization than using state-of-the-art atlases. We also demonstrate the clinical feasibility of the proposed technique assessing the post-operative electrode active contact locations.

show abstract

Patch-based label fusion segmentation of brainstem structures with dual-contrast MRI for Parkinson’s disease

Cited by 19 publications

References 50 publications

Automatic localization of the subthalamic nucleus on patient‐specific clinical MRI by incorporating 7 T MRI and machine learning: Application in deep brain stimulation

Automatic localization of the subthalamic nucleus on patient‐specific clinical MRI by incorporating 7 T MRI and machine learning: Application in deep brain stimulation

Multi-atlas segmentation of biomedical images: A survey

Automatic Localization of the Subthalamic Nucleus on Patient-Specific Clinical MRI by Incorporating 7T MRI and Machine Learning: Application in Deep Brain Stimulation

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