Segmentation of vestibular schwannoma from MRI, an open annotated dataset and baseline algorithm
Jonathan Shapey, Aaron Kujawa, Reuben Dorent, Guotai Wang, Alexis Dimitriadis, Diana Grishchuk, Ian Paddick, Neil Kitchen, Robert Bradford, Shakeel R. Saeed, Sotirios Bisdas, Sébastien Ourselin, Tom Vercauteren
Abstract
Automatic segmentation of vestibular schwannomas (VS) from magnetic resonance imaging (MRI) could significantly improve clinical workflow and assist patient management. We have previously developed a novel artificial intelligence framework based on a 2.5D convolutional neural network achieving excellent results equivalent to those achieved by an independent human annotator. Here, we provide the first publicly-available annotated imaging dataset of VS by releasing the data and annotations used in our prior work. This collection contains a labelled dataset of 484 MR images collected on 242 consecutive patients with a VS undergoing Gamma Knife Stereotactic Radiosurgery at a single institution. Data includes all segmentations and contours used in treatment planning and details of the administered dose. Implementation of our automated segmentation algorithm uses MONAI, a freely-available open-source framework for deep learning in healthcare imaging. These data will facilitate the development and validation of automated segmentation frameworks for VS and may also be used to develop other multi-modal algorithmic models.