During tumor resection surgery, intraoperative ultrasound images of the brain show anatomical structures, the tumor, and the resection cavity (after resection started). These elements help with the localization and tumor resection, and can be used to register the preoperative MRI to intraoperative images, to compensate for the tissue deformation occurring during surgery. We evaluate a multi-class segmentation model for the sulci, falx cerebri, tumor, resection cavity and ventricle. We present strategies to overcome the severe class imbalance in the training data. We show that a multi-class model may leverage inter-class spatial relationships and produce more accurate results than single-class models.
Because of the deformation of the brain during neurosurgery, intraoperative imaging can be used to visualize the actual location of the brain structures. These images are used for image-guided navigation as well as determining whether the resection is complete and localizing the remaining tumor tissue. Intraoperative ultrasound (iUS) is a convenient modality with short acquisition times. However, iUS images are difficult to interpret because of the noise and artifacts. In particular, tumor tissue is difficult to distinguish from healthy tissue and it is very difficult to delimit tumors in iUS images. In this paper, we propose an automatic method to segment low grade brain tumors in iUS images using a 2-D and 3-D U-Net. We trained the networks on three folds with twelve training cases and five test cases each. The obtained results are promising, with a median Dice score of 0.72. The volume differences between the estimated and ground truth segmentations were similar to the intra-rater volume differences. While these results are preliminary, they suggest that deep learning methods can be successfully applied to tumor segmentation in intraoperative images.
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