Abstract
PurposeNeurosurgeons 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.MethodsThe 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.ResultsRegarding 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.ConclusionsWe 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.
Highlights
In the neurosurgical planning for tumor resection, preoperative magnetic resonance imaging (MRI) data are usually acquired [1, 2]
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
To maintain a good tradeoff between the visibility of the surrounding anatomical structures and visualization of the mask of the resection cavity, we decided to highlight the element of interest with a border in green and purple
Summary
In the neurosurgical planning for tumor resection, preoperative magnetic resonance imaging (MRI) data are usually acquired [1, 2]. 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. The resection of the tumor leads to other anatomical modifications, with no counterpart in the preoperative data. All these effects combined together are denoted as brain shift [3]. This phenomenon impedes a correct mapping between preoperative data and surgical scene. The probability of missing pathological tissue in the resection increases, reducing the survival rates of the operated patients [4, 5]
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