1 Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada.
2 School of Health, Concordia University, Montreal, Canada.
3 Université Grenoble Alpes, CNRS, Grenoble INP, TIMC, Grenoble, France.
4 Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.
5 Department of Neurosurgery, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway.
6 Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.
7 Department of Computer Science and Software Engineering, Concordia University, Montreal, Canada.
8 Department of Health Research, SINTEF Digital, Trondheim, Norway.
9 Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.

Purpose: Registration and segmentation of magnetic resonance (MR) and ultrasound (US) images could play an essential role in surgical planning and resectioning 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 set of segmentations (RESECT-SEG) of cerebral structures from the previously published RESECT dataset to encourage a more rigorous development and assessment of image-processing techniques for neurosurgery.

Acquisition and validation methods: The RESECT database consists of MR and intraoperative US (iUS) images of 23 patients who underwent brain tumor resection surgeries. The proposed RESECT-SEG dataset contains segmentations of tumor tissues, sulci, falx cerebri, and resection cavity of the RESECT iUS images. Two highly experienced neurosurgeons validated the quality of the segmentations.

Data format and usage notes: Segmentations are provided in 3D NIFTI format in the OSF open-science platform: https://osf.io/jv8bk.

Potential applications: The proposed RESECT-SEG dataset includes segmentations of real-world clinical US brain images that could be used to develop and evaluate segmentation and registration methods. Eventually, this dataset could further improve the quality of image guidance in neurosurgery.