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PyDBS: an automated image processing workflow for deep brain stimulation surgery.

Abstract : Purpose - Deep brain stimulation (DBS) is a surgical procedure for treating motor-related neurological disorders. DBS clinical efficacy hinges on precise surgical planning and accurate electrode placement, which in turn call upon several image processing and visualization tasks, such as image registration, image segmentation, image fusion, and 3D visualization. These tasks are often performed by a heterogeneous set of software tools, which adopt differing formats and geometrical conventions and require patient-specific parameterization or interactive tuning. To overcome these issues, we introduce in this article PyDBS, a fully integrated and automated image processing workflow for DBS surgery. Methods - PyDBS consists of three image processing pipelines and three visualization modules assisting clinicians through the entire DBS surgical workflow, from the preoperative planning of electrode trajectories to the postoperative assessment of electrode placement. The system's robustness, speed, and accuracy were assessed by means of a retrospective validation, based on 92 clinical cases. Results - The complete PyDBS workflow achieved satisfactory results in 92 % of tested cases, with a median processing time of 28 min per patient. Conclusion - The results obtained are compatible with the adoption of PyDBS in clinical practice.
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https://www.hal.inserm.fr/inserm-01116063
Contributor : Lotfi Senhadji <>
Submitted on : Thursday, February 12, 2015 - 2:41:52 PM
Last modification on : Wednesday, November 25, 2020 - 6:44:05 PM
Long-term archiving on: : Wednesday, May 13, 2015 - 10:22:37 AM

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Tiziano d'Albis, Claire Haegelen, Caroline Essert, Sara Fernández-Vidal, Florent Lalys, et al.. PyDBS: an automated image processing workflow for deep brain stimulation surgery.. International Journal of Computer Assisted Radiology and Surgery, Springer Verlag, 2015, 10 (2), pp.117-28. ⟨10.1007/s11548-014-1007-y⟩. ⟨inserm-01116063⟩

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