Enhanced registration of ultrasound volumes by segmentation of resection cavity in neurosurgical procedures
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ORIGINAL ARTICLE
Enhanced registration of ultrasound volumes by segmentation of resection cavity in neurosurgical procedures Luca Canalini1,2
· Jan Klein1 · Dorothea Miller3 · Ron Kikinis4
Received: 10 January 2020 / Accepted: 25 September 2020 © The Author(s) 2020
Abstract Purpose Neurosurgeons can have a better understanding of surgical procedures by comparing ultrasound images obtained at different phases of the tumor resection. However, establishing a direct mapping between subsequent acquisitions is challenging due to the anatomical changes happening during surgery. We propose here a method to improve the registration of ultrasound volumes, by excluding the resection cavity from the registration process. Methods The first step of our approach includes the automatic segmentation of the resection cavities in ultrasound volumes, acquired during and after resection. We used a convolution neural network inspired by the 3D U-Net. Then, subsequent ultrasound volumes are registered by excluding the contribution of resection cavity. Results Regarding the segmentation of the resection cavity, the proposed method achieved a mean DICE index of 0.84 on 27 volumes. Concerning the registration of the subsequent ultrasound acquisitions, we reduced the mTRE of the volumes acquired before and during resection from 3.49 to 1.22 mm. For the set of volumes acquired before and after removal, the mTRE improved from 3.55 to 1.21 mm. Conclusions We proposed an innovative registration algorithm to compensate the brain shift affecting ultrasound volumes obtained at subsequent phases of neurosurgical procedures. To the best of our knowledge, our method is the first to exclude automatically segmented resection cavities in the registration of ultrasound volumes in neurosurgery. Keywords Ultrasound · Image-guided surgery · Resection cavity · Deep learning · Neurosurgery
Introduction In the neurosurgical planning for tumor resection, preoperative magnetic resonance imaging (MRI) data are usually acquired [1, 2]. Subsequently, neuronavigation systems are utilized to make these images available during the tumor resection. Through a rigid transformation computed between the surgical scene and the MRI data, neurosurgeons are able to map any intracranial position to the preoperative data.
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Luca Canalini [email protected]
1
Fraunhofer MEVIS, Institute for Digital Medicine, Bremen, Germany
2
Medical Imaging Computing, University of Bremen, Bremen, Germany
3
Department of Neurosurgery, University Hospital Knappschaftskrankenhaus, Bochum, Germany
4
Surgical Planning Laboratory, Brigham and Women’s Hospital, Harvard Medical School, Boston, USA
This is beneficial for the surgery outcome, since it decreases the risk of tumor residuals and increases the survival rate of the operated patients. However, image-guided surgery based only on preoperative data has some limitations [3]. In the early stages of the procedure, inaccuracies in the neurosurgical setting can degrade the rigid registration computed by the neuronavigatio
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