Segmentation of vestibular schwannoma from MRI, an open annotated dataset and baseline algorithm

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.

PyRaDiSe: A Python package for DICOM-RT-based auto-segmentation pipeline construction and DICOM-RT data conversion

“…The offered examples are available on GitHub or directly rendered in the documentation. In all examples, MR images and segmentations of the head of five subjects from the TCIA [24] "Segmentation of Vestibular Schwannoma from Magnetic Resonance Imaging: An Open Annotated Dataset and Baseline Algorithm" [25,26] dataset are used. Each subject comprises a T1-weighted post-contrast (Gd) image, a T2-weighted image, and manual segmentations of the tumor volume, the cochlea, and the skull.…”

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“…Recently, radiomics studies of other diseases selected ROI through automatic segmentation, such as lung cancer, breast cancer and gastric disease (40)(41)(42). Jonathan et al had developed an algorithm based on convolutional neural network to automatically segment vestibular schwannoma and achieved satisfactory results (43). The application of automatic segmentation could benefit our research and clinical practice.…”

“…Jonathan et al. had developed an algorithm based on convolutional neural network to automatically segment vestibular schwannoma and achieved satisfactory results ( 43 ). The application of automatic segmentation could benefit our research and clinical practice.…”

An Open Dataset of Annotated Metaphase Cell Images for Chromosome Identification

“…In the event of an incomplete database, the trust of experts and patients cannot be gained 22 . Many examples of AI in medical research require a large database to improve the credibility and stability of the AI model [23][24][25][26][27] .…”