Abstract
During neurosurgical operations, surgeons can decide to acquire intraoperative data to better proceed with the removal of a tumor. A valid option is given by ultrasound (US) imaging, which can be easily obtained at subsequent surgical stages, giving therefore multiple updates of the resection cavity. To improve the efficacy of the intraoperative guidance, neurosurgeons may benefit from having a direct correspondence between anatomical structures identified at different US acquisitions. In this context, the commonly available neuronavigation systems already provide registration methods, which however are not enough accurate to overcome the anatomical changes happening during resection. Therefore, our aim with this work is to improve the registration of intraoperative US volumes. In the proposed methodology, first a distance mapping of automatically segmented anatomical structures is computed and then the transformed images are utilized in the registration step. Our solution is tested on a public dataset of 17 cases, where the average landmark registration error between volumes acquired at the beginning and at the end of neurosurgical procedures is reduced from 3.55 mm to 1.27 mm.
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References
Gerard, I.J., Kersten-Oertel, M., Petrecca, K., Sirhan, D., Hall, J.A., Collins, D.L.: Brain shift in neuronavigation of brain tumors: a review. Med. Image Anal. 35, 403–420 (2017)
Unsgaard, G., Ommedal, S., Muller, T., Gronningsaeter, A., Nagelhus Hernes, T.A.: Neuronavigation by intraoperative three-dimensional ultrasound: initial experience during brain tumor resection. Neurosurgery 50(4), 804–812 (2002)
Unsgaard, G., et al.: Intra-operative 3D ultrasound in neurosurgery. Acta Neurochir. 148(3), 235–253 (2006)
Rygh, O.M., Selbekk, T., Torp, S.H., Lydersen, S., Hernes, T.A., Unsgaard, G.: Comparison of navigated 3D ultrasound findings with histopathology in subsequent phases of glioblastoma resection. Acta Neurochir. 150, 1033–1042 (2008)
Mercier, L., Araujo, D., Haegelen, C., Del Maestro, R.F., Petrecca, K., Collins, D.L.: Registering pre- and postresection 3-dimensional ultrasound for improved visualization of residual brain tumor. Ultrasound Med. Biol. 39(1), 16–29 (2013)
Rivaz, H., Collins, D.L.: Near real-time robust nonrigid registration of volumetric ultrasound images for neurosurgery. Ultrasound Med. Biol. 41(2), 574–587 (2015)
Rivaz, H., Collins, D.L.: Deformable registration of preoperative MR, pre-resection ultrasound, and post-resection ultrasound images of neurosurgery. Int. J. Comput. Assist. Radiol. Surg. 10(7), 1017–1028 (2015)
Hang, Z., Rivaz, H.: Registration of pre- and postresection ultrasound volumes with noncorresponding regions in neurosurgery. IEEE J. Biomed. Health Inform. 20, 1240–1249 (2016)
Machado, I., et al.: Non-rigid registration of 3D ultrasound for neurosurgery using automatic feature detection and matching. Int. J. Comput. Assist. Radiol. Surg. 13(10), 1525–1538 (2018)
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. 14(10), 1697–1713 (2019)
Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. CoRR, 1606.06650 (2016)
Xiao, Y., Fortin, M., Unsgård, G., Rivaz, H., Reinertsen, I.: REtroSpective Evaluation of Cerebral Tumors (RESECT): a clinical database of pre-operative MRI and intra-operative ultrasound in low-grade glioma surgeries. Med. Phys. 44(7), 3875–3882 (2017)
Modersitzki, J.: Flexible algorithms for image registration. SIAM (2009)
Liu, D.C., Nocedal, J.: On the limited memory BFGS method for large scale optimization. Math. Program. 45(1–3), 503–528 (1989)
Acknowledgements
This work was funded by the H2020 Marie-Curie ITN TRABIT (765148) project. Moreover, Prof. Dr. Kikinis is supported by NIH grants P41 EB015902, P41 EB015898, and U24 CA180918.
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Canalini, L., Klein, J., Miller, D., Kikinis, R. (2019). Registration of Ultrasound Volumes Based on Euclidean Distance Transform. In: Zhou, L., et al. Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention. LABELS HAL-MICCAI CuRIOUS 2019 2019 2019. Lecture Notes in Computer Science(), vol 11851. Springer, Cham. https://doi.org/10.1007/978-3-030-33642-4_14
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DOI: https://doi.org/10.1007/978-3-030-33642-4_14
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