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Segmentation-based Registration of Ultrasound Volumes for Glioma Resection in Image-guided Neurosurgery

Institution:
1Fraunhofer MEVIS, Institute for Digital Medicine, Bremen, Germany. luca.canalini@mevis.fraunhofer.de.
2Surgical Planning Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
Publication Date:
Aug-2019
Journal:
Int J Comput Assist Radiol Surg
Citation:
Int J Comput Assist Radiol Surg. 2019 Aug 7.
PubMed ID:
31392670
Keywords:
Convolutional neural network, Image registration, Image segmentation, Image-guided surgery, Ultrasound
Appears in Collections:
NAC, NCIGT
Sponsors:
P41 EB015898/EB/NIBIB NIH HHS/United States
P41 EB015902/EB/NIBIB NIH HHS/United States
Generated Citation:
Canalini L., Klein J., Miller D., Kikinis R. Segmentation-based Registration of Ultrasound Volumes for Glioma Resection in Image-guided Neurosurgery. Int J Comput Assist Radiol Surg. 2019 Aug 7. PMID: 31392670.
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PURPOSE: In image-guided surgery for glioma removal, neurosurgeons usually plan the resection on images acquired before surgery and use them for guidance during the subsequent intervention. However, after the surgical procedure has begun, the preplanning images become unreliable due to the brain shift phenomenon, caused by modifications of anatomical structures and imprecisions in the neuronavigation system. To obtain an updated view of the resection cavity, a solution is to collect intraoperative data, which can be additionally acquired at different stages of the procedure in order to provide a better understanding of the resection. A spatial mapping between structures identified in subsequent acquisitions would be beneficial. We propose here a fully automated segmentation-based registration method to register ultrasound (US) volumes acquired at multiple stages of neurosurgery. METHODS: We chose to segment sulci and falx cerebri in US volumes, which remain visible during resection. To automatically segment these elements, first we trained a convolutional neural network on manually annotated structures in volumes acquired before the opening of the dura mater and then we applied it to segment corresponding structures in different surgical phases. Finally, the obtained masks are used to register US volumes acquired at multiple resection stages. RESULTS: Our method reduces the mean target registration error (mTRE) between volumes acquired before the opening of the dura mater and during resection from 3.49 mm (± 1.55 mm) to 1.36 mm (± 0.61 mm). Moreover, the mTRE between volumes acquired before opening the dura mater and at the end of the resection is reduced from 3.54 mm (± 1.75 mm) to 2.05 mm (± 1.12 mm). CONCLUSION: The segmented structures demonstrated to be good candidates to register US volumes acquired at different neurosurgical phases. Therefore, our solution can compensate brain shift in neurosurgical procedures involving intraoperative US data.