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. 2015 Feb;10(2):117-28.
doi: 10.1007/s11548-014-1007-y. Epub 2014 May 6.

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

Tiziano D'Albis  1 , Claire Haegelen, Caroline Essert, Sara Fernández-Vidal, Florent Lalys, Pierre Jannin
Affiliations
Free article

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

Tiziano D'Albis et al. Int J Comput Assist Radiol Surg. 2015 Feb.
Free article

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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