Summary
Brain tumors strike people in the prime of life. Surgical resection is the initial treatment for nearly all brain tumors and aims at maximizing the extent of tumor resection while preserving the patient's cognitive function. To optimize this tradeoff, neuronavigation systems have been developed to provide intraoperative guidance to surgeons. These systems allow for the visualization of the position of surgeons' surgical tools relative to the tumor and critical brain areas visible in preoperative Magnetic Resonance Imaging. However, these systems become inaccurate as the surgery progresses since they do not account for brain deformation and tissue resection occurring during surgery.
In an interdisciplinary effort, project SafeREG combines the researcher's background to the expertise of computational scientists from INRIA and clinicians from Parisian hospitals. Its objective is to invent a novel image registration methodology with intraoperative ultrasound that is rich enough to capture complex deformations occurring at the tumor and resection cavity boundaries, fast enough to be employable clinically, and interpretable enough for informed decision-making by neurosurgeons. This will be accomplished by pushing the envelope of scientific knowledge in (1) cross-modality domain adaptation for weakly- and unsupervised image segmentation; (2) modality-invariance representation learning using contrastive learning; (3) non-rigid registration with discrete probabilistic methods; (4) simulated-based variational inference for registration uncertainty quantification that leverages biomechanical knowledge. This research has the potential to deliver accurate and informed image-guided surgery, conferring a lower risk of new neurologic deficits and improved patient prognosis. Beyond neurosurgery, it has broad applications to additional areas of image-guided therapy, including spine, liver, and prostate surgery.
In an interdisciplinary effort, project SafeREG combines the researcher's background to the expertise of computational scientists from INRIA and clinicians from Parisian hospitals. Its objective is to invent a novel image registration methodology with intraoperative ultrasound that is rich enough to capture complex deformations occurring at the tumor and resection cavity boundaries, fast enough to be employable clinically, and interpretable enough for informed decision-making by neurosurgeons. This will be accomplished by pushing the envelope of scientific knowledge in (1) cross-modality domain adaptation for weakly- and unsupervised image segmentation; (2) modality-invariance representation learning using contrastive learning; (3) non-rigid registration with discrete probabilistic methods; (4) simulated-based variational inference for registration uncertainty quantification that leverages biomechanical knowledge. This research has the potential to deliver accurate and informed image-guided surgery, conferring a lower risk of new neurologic deficits and improved patient prognosis. Beyond neurosurgery, it has broad applications to additional areas of image-guided therapy, including spine, liver, and prostate surgery.
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| Web resources: | https://cordis.europa.eu/project/id/101154248 |
| Start date: | 01-08-2024 |
| End date: | 31-07-2026 |
| Total budget - Public funding: | - 195 914,00 Euro |
Cordis data
Original description
Brain tumors strike people in the prime of life. Surgical resection is the initial treatment for nearly all brain tumors and aims at maximizing the extent of tumor resection while preserving the patient's cognitive function. To optimize this tradeoff, neuronavigation systems have been developed to provide intraoperative guidance to surgeons. These systems allow for the visualization of the position of surgeons' surgical tools relative to the tumor and critical brain areas visible in preoperative Magnetic Resonance Imaging. However, these systems become inaccurate as the surgery progresses since they do not account for brain deformation and tissue resection occurring during surgery.In an interdisciplinary effort, project SafeREG combines the researcher's background to the expertise of computational scientists from INRIA and clinicians from Parisian hospitals. Its objective is to invent a novel image registration methodology with intraoperative ultrasound that is rich enough to capture complex deformations occurring at the tumor and resection cavity boundaries, fast enough to be employable clinically, and interpretable enough for informed decision-making by neurosurgeons. This will be accomplished by pushing the envelope of scientific knowledge in (1) cross-modality domain adaptation for weakly- and unsupervised image segmentation; (2) modality-invariance representation learning using contrastive learning; (3) non-rigid registration with discrete probabilistic methods; (4) simulated-based variational inference for registration uncertainty quantification that leverages biomechanical knowledge. This research has the potential to deliver accurate and informed image-guided surgery, conferring a lower risk of new neurologic deficits and improved patient prognosis. Beyond neurosurgery, it has broad applications to additional areas of image-guided therapy, including spine, liver, and prostate surgery.
Status
SIGNEDCall topic
HORIZON-MSCA-2023-PF-01-01Update Date
29-09-2024
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