Stereotactic and Functional Neurosurgery

Miller C.P.K.a,e· Muller J.a,b· Noecker A.M.c· Matias C.a· Alizadeh M.a,b· McIntyre C.c,d· Wu C.a,b

Author affiliations

aDepartment of Neurological Surgery, Vickie and Jack Farber Institute for Neuroscience, Thomas Jefferson University, Philadelphia, PA, USA
bJefferson Integrated Magnetic Resonance Imaging Center, Department of Radiology, Thomas Jefferson University, Philadelphia, PA, USA
cDepartment of Biomedical Engineering, Pratt School of Engineering, Duke University, Durham, NC, USA
dDepartment of Neurosurgery, School of Medicine, Duke University, Durham, NC, USA
eDepartment of Neurosurgery, The University of Kansas School of Medicine, Kansas City, KS, USA

Stereotact Funct Neurosurg

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Article / Publication Details

First-Page Preview

Received: May 10, 2022
Accepted: August 13, 2022
Published online: March 07, 2023

Number of Print Pages: 12
Number of Figures: 4
Number of Tables: 3

ISSN: 1011-6125 (Print)
eISSN: 1423-0372 (Online)

For additional information: https://www.karger.com/SFN

Abstract

Introduction: Accurate and precise delineation of the globus pallidus pars interna (GPi) and subthalamic nucleus (STN) is critical for the clinical treatment and research of Parkinson’s disease (PD). Automated segmentation is a developing technology which addresses limitations of visualizing deep nuclei on MR imaging and standardizing their definition in research applications. We sought to compare manual segmentation with three workflows for template-to-patient nonlinear registration providing atlas-based automatic segmentation of deep nuclei. Methods: Bilateral GPi, STN, and red nucleus (RN) were segmented for 20 PD and 20 healthy control (HC) subjects using 3T MRIs acquired for clinical purposes. The automated workflows used were an option available in clinical practice and two common research protocols. Quality control (QC) was performed on registered templates via visual inspection of readily discernible brain structures. Manual segmentation using T1, proton density, and T2 sequences was used as “ground truth” data for comparison. Dice similarity coefficient (DSC) was used to assess agreement between segmented nuclei. Further analysis was done to compare the influences of disease state and QC classifications on DSC. Results: Automated segmentation workflows (CIT-S, CRV-AB, and DIST-S) had the highest DSC for the RN and lowest for the STN. Manual segmentations outperformed automated segmentation for all workflows and nuclei; however, for 3/9 workflows (CIT-S STN, CRV-AB STN, and CRV-AB GPi) the differences were not statically significant. HC and PD only showed significant differences in 1/9 comparisons (DIST-S GPi). QC classification only demonstrated significantly higher DSC in 2/9 comparisons (CRV-AB RN and GPi). Conclusion: Manual segmentations generally performed better than automated segmentations. Disease state does not appear to have a significant effect on the quality of automated segmentations via nonlinear template-to-patient registration. Notably, visual inspection of template registration is a poor indicator of the accuracy of deep nuclei segmentation. As automatic segmentation methods continue to evolve, efficient and reliable QC methods will be necessary to support safe and effective integration into clinical workflows.

© 2023 S. Karger AG, Basel

References Garonzik IM, Hua SE, Ohara S, Lenz FA. Intraoperative microelectrode and semi-microelectrode recording during the physiological localization of the thalamic nucleus ventral intermediate. Mov Disord. 2002;17(Suppl 3):S135–44. Gross RE, Krack P, Rodriguez-Oroz MC, Rezai AR, Benabid AL. Electrophysiological mapping for the implantation of deep brain stimulators for Parkinson’s disease and tremor. Mov Disord. 2006;21(Suppl 14):S259–83. Rezai AR, Kopell BH, Gross RE, Vitek JL, Sharan AD, Limousin P, et al. Deep brain stimulation for Parkinson’s disease: surgical issues. Mov Disord. 2006;21(Suppl 14):S197–218. Zeiler FA, Wilkinson M, Krcek JP. Subthalamic nucleus deep brain stimulation: an invaluable role for MER. Can J Neurol Sci. 2013;40(4):572–5. Foltynie T, Zrinzo L, Martinez-Torres I, Tripoliti E, Petersen E, Holl E, et al. MRI-guided STN DBS in Parkinson’s disease without microelectrode recording: efficacy and safety. J Neurol Neurosurg Psychiatry. 2011;82(4):358–63. Aviles-Olmos I, Kefalopoulou Z, Tripoliti E, Candelario J, Akram H, Martinez-Torres I, et al. Long-term outcome of subthalamic nucleus deep brain stimulation for Parkinson’s disease using an MRI-guided and MRI-verified approach. J Neurol Neurosurg Psychiatry. 2014;85(12):1419–25. Brodsky MA, Anderson S, Murchison C, Seier M, Wilhelm J, Vederman A, et al. Clinical outcomes of asleep vs awake deep brain stimulation for Parkinson disease. Neurology. 2017;89(19):1944–50. Chen T, Mirzadeh Z, Chapple KM, Lambert M, Shill HA, Moguel-Cobos G, et al. Clinical outcomes following awake and asleep deep brain stimulation for Parkinson disease. J Neurosurg. 2018;130(1):109–20. Voges J, Volkmann J, Allert N, Lehrke R, Koulousakis A, Freund HJ, et al. Bilateral high-frequency stimulation in the subthalamic nucleus for the treatment of Parkinson disease: correlation of therapeutic effect with anatomical electrode position. J Neurosurg. 2002;96(2):269–79. Lorio S, Fresard S, Adaszewski S, Kherif F, Chowdhury R, Frackowiak RS, et al. New tissue priors for improved automated classification of subcortical brain structures on MRI. Neuroimage. 2016;130:157–66. Wang BT, Poirier S, Guo T, Parrent AG, Peters TM, Khan AR. Generation and evaluation of an ultra-high-field atlas with applications in DBS planning. Medical Imaging 2016: Image Processing. International Society for Optics and Photonics; 2016. Vol. 9784; p. 97840H. Ewert S, Horn A, Finkel F, Li N, Kühn AA, Herrington TM. Optimization and comparative evaluation of nonlinear deformation algorithms for atlas-based segmentation of DBS target nuclei. Neuroimage. 2019;184:586–98. Avants BB, Epstein CL, Grossman M, Gee JC. Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med Image Anal. 2008;12(1):26–41. Schönecker T, Kupsch A, Kühn AA, Schneider GH, Hoffmann KT. Automated optimization of subcortical cerebral MR imaging-atlas coregistration for improved postoperative electrode localization in deep brain stimulation. AJNR Am J Neuroradiol. 2009;30(10):1914–21. Ashburner J, Friston KJ. Diffeomorphic registration using geodesic shooting and Gauss-Newton optimisation. Neuroimage. 2011;55(3):954–67. Tustison NJ, Avants BB. Explicit B-spline regularization in diffeomorphic image registration. Front Neuroinform. 2013;7:39. Dormont D, Ricciardi KG, Tandé D, Parain K, Menuel C, Galanaud D, et al. Is the subthalamic nucleus hypointense on T2-weighted images? A correlation study using MR imaging and stereotactic atlas data. AJNR Am J Neuroradiol. 2004;25(9):1516–23. Benhajali Y, Badhwar A, Spiers H, Urchs S, Armoza J, Ong T, et al. A standardized protocol for efficient and reliable quality control of brain registration in functional MRI studies. Front Neuroinform. 2020;14:7. Ewert S, Plettig P, Li N, Chakravarty MM, Collins DL, Herrington TM, et al. Toward defining deep brain stimulation targets in MNI space: a subcortical atlas based on multimodal MRI, histology and structural connectivity. Neuroimage. 2018;170:271–82. Pauli WM, Nili AN, Tyszka JM. A high-resolution probabilistic in vivo atlas of human subcortical brain nuclei. Sci Data. 2018;5:180063. Noecker AM, Frankemolle-Gilbert AM, Howell B, Petersen MV, Beylergil SB, Shaikh AG, et al. Stimvision v2: examples and applications in subthalamic deep brain stimulation for Parkinson’s disease. Neuromodulation. 2021;24(2):248–58. Rohde GK, Aldroubi A, Dawant BM. The adaptive bases algorithm for intensity-based nonrigid image registration. IEEE Trans Med Imaging. 2003;22(11):1470–9. D’Haese PF, Cetinkaya E, Konrad PE, Kao C, Dawant BM. Computer-aided placement of deep brain stimulators: from planning to intraoperative guidance. IEEE Trans Med Imaging. 2005;24(11):1469–78. Chakravarty MM, Sadikot AF, Germann J, Bertrand G, Collins DL. Towards a validation of atlas warping techniques. Med Image Anal. 2008;12(6):713–26. Chakravarty MM, Sadikot AF, Germann J, Hellier P, Bertrand G, Collins DL. Comparison of piece-wise linear, linear, and nonlinear atlas-to-patient warping techniques: analysis of the labeling of subcortical nuclei for functional neurosurgical applications. Hum Brain Mapp. 2009;30(11):3574–95. Makowski C, Béland S, Kostopoulos P, Bhagwat N, Devenyi GA, Malla AK, et al. Evaluating accuracy of striatal, pallidal, and thalamic segmentation methods: comparing automated approaches to manual delineation. Neuroimage. 2018;170:182–98. Sealed Envelope Ltd. Power calculator for continuous outcome equivalence trial. 2012. Available from: https://www.sealedenvelope.com/power/continuous-equivalence/. Fischl B, Sereno MI, Tootell RB, Dale AM. High-resolution intersubject averaging and a coordinate system for the cortical surface. Hum Brain Mapp. 1999;8(4):272–84. Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, et al. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron. 2002;33(3):341–55. Haegelen C, Coupé P, Fonov V, Guizard N, Jannin P, Morandi X, et al. Automated segmentation of basal ganglia and deep brain structures in MRI of Parkinson’s disease. Int J Comput Assist Radiol Surg. 2013;8(1):99–110. Keuken MC, Bazin PL, Crown L, Hootsmans J, Laufer A, Müller-Axt C, et al. Quantifying inter-individual anatomical variability in the subcortex using 7 T structural MRI. Neuroimage. 2014;94:40–6. Garzón B, Sitnikov R, Bäckman L, Kalpouzos G. Automated segmentation of midbrain structures with high iron content. Neuroimage. 2018;170:199–209. Chakravarty MM, Steadman P, van Eede MC, Calcott RD, Gu V, Shaw P, et al. Performing label-fusion-based segmentation using multiple automatically generated templates. Hum Brain Mapp. 2013;34(10):2635–54. Bardinet E, Bhattacharjee M, Dormont D, Pidoux B, Malandain G, Schüpbach M, et al. A three-dimensional histological atlas of the human basal ganglia. II. Atlas deformation strategy and evaluation in deep brain stimulation for Parkinson disease: clinical article. J Neurosurg. 2009;110(2):208–19. Abbass M, Gilmore G, Taha A, Chevalier R, Jach M, Peters TM, et al. Application of the anatomical fiducials framework to a clinical dataset of patients with Parkinson’s disease. Brain Struct Funct. 2022;227(1):393–405. Bus S, van den Munckhof P, Bot M, Pal G, Ouyang B, Sani S, et al. Borders of STN determined by MRI versus the electrophysiological STN. A comparison using intraoperative CT. Acta Neurochir. 2018;160(2):373–83. Richter EO, Hoque T, Halliday W, Lozano AM, Saint-Cyr JA. Determining the position and size of the subthalamic nucleus based on magnetic resonance imaging results in patients with advanced Parkinson disease. J Neurosurg. 2004;100(3):541–6. de Hollander G, Keuken MC, Bazin PL, Weiss M, Neumann J, Reimann K, et al. A gradual increase of iron toward the medial-inferior tip of the subthalamic nucleus. Hum Brain Mapp. 2014;35(9):4440–9. Chakravarty MM, Bertrand G, Hodge CP, Sadikot AF, Collins DL. The creation of a brain atlas for image guided neurosurgery using serial histological data. Neuroimage. 2006;30(2):359–76. Castro FJS, Pollo C, Meuli R, Maeder P, Cuisenaire O, Cuadra MB, et al. A cross validation study of deep brain stimulation targeting: from experts to atlas-based, segmentation-based and automatic registration algorithms. IEEE Trans Med Imaging. 2006;25(11):1440–50. Polanski WH, Martin KD, Engellandt K, von Kummer R, Klingelhoefer L, Fauser M, et al. Accuracy of subthalamic nucleus targeting by T2, FLAIR and SWI-3-Tesla MRI confirmed by microelectrode recordings. Acta Neurochir. 2015;157(3):479–86. Verhagen R, Schuurman PR, van den Munckhof P, Contarino MF, de Bie RMA, Bour LJ. Comparative study of microelectrode recording-based STN location and MRI-based STN location in low to ultra-high field (7.0 T) T2-weighted MRI images. J Neural Eng. 2016;13(6):066009. Reinacher PC, Várkuti B, Krüger MT, Piroth T, Egger K, Roelz R, et al. Automatic segmentation of the subthalamic nucleus: a viable option to support planning and visualization of patient-specific targeting in deep brain stimulation. Oper Neurosurg. 2019;17(5):497–502. Pallavaram S, D’Haese PF, Lake W, Konrad PE, Dawant BM, Neimat JS. Fully automated targeting using nonrigid image registration matches accuracy and exceeds precision of best manual approaches to subthalamic deep brain stimulation targeting in Parkinson disease. Neurosurgery. 2015;76(6):756–65. Li Z, Zhang JG, Ye Y, Li X. Review on factors affecting targeting accuracy of deep brain stimulation electrode implantation between 2001 and 2015. Stereotact Funct Neurosurg. 2016;94(6):351–62. Beliveau V, Nørgaard M, Birkl C, Seppi K, Scherfler C. Automated segmentation of deep brain nuclei using convolutional neural networks and susceptibility weighted imaging. Hum Brain Mapp. 2021;42(15):4809–22. Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. International Conference on Medical image computing and computer-assisted intervention. Springer; 2015. p. 234–41. Çiçek Ö, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O. 3D U-net: learning dense volumetric segmentation from sparse annotation. In: Ourselin S, Joskowicz L, Sabuncu MR, Unal G, Wells W, editors. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016. Cham: Springer International Publishing; 2016. p. 424–32. Solomon O, Palnitkar T, Patriat R, Braun H, Aman J, Park MC, et al. Deep-learning based fully automatic segmentation of the globus pallidus interna and externa using ultra-high 7 Tesla MRI. Hum Brain Mapp. 2021;42(9):2862–79. Article / Publication Details

First-Page Preview

Received: May 10, 2022
Accepted: August 13, 2022
Published online: March 07, 2023

Number of Print Pages: 12
Number of Figures: 4
Number of Tables: 3

ISSN: 1011-6125 (Print)
eISSN: 1423-0372 (Online)

For additional information: https://www.karger.com/SFN

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