  Purpose: Registration and segmentation of magnetic resonance (MR) and
ultrasound (US) images play an essential role in surgical planning and
resection of brain tumors. However, validating these techniques is challenging
due to the scarcity of publicly accessible sources with high-quality ground
truth information. To this end, we propose a unique annotation dataset of tumor
tissues and resection cavities from the previously published RESECT dataset
(Xiao et al. 2017) to encourage a more rigorous assessments of image processing
techniques. Acquisition and validation methods: The RESECT database consists of
MR and intraoperative US (iUS) images of 23 patients who underwent resection
surgeries. The proposed dataset contains tumor tissues and resection cavity
annotations of the iUS images. The quality of annotations were validated by two
highly experienced neurosurgeons through several assessment criteria. Data
format and availability: Annotations of tumor tissues and resection cavities
are provided in 3D NIFTI formats. Both sets of annotations are accessible
online in the \url{https://osf.io/6y4db}. Discussion and potential
applications: The proposed database includes tumor tissue and resection cavity
annotations from real-world clinical ultrasound brain images to evaluate
segmentation and registration methods. These labels could also be used to train
deep learning approaches. Eventually, this dataset should further improve the
quality of image guidance in neurosurgery.
