NDLI: Automatic segmentation of vestibular schwannomas from T1-weighted MRI with a deep neural network

Content Provider	Springer Nature : BioMed Central
Author	Wang, Hesheng Qu, Tanxia Bernstein, Kenneth Barbee, David Kondziolka, Douglas
Abstract	Background Long-term follow-up using volumetric measurement could significantly assist in the management of vestibular schwannomas (VS). Manual segmentation of VS from MRI for treatment planning and follow-up assessment is labor-intensive and time-consuming. This study aims to develop a deep learning technique to fully automatically segment VS from MRI. Methods This study retrospectively analyzed MRI data of 737 patients who received gamma knife radiosurgery for VS. Treatment planning T1-weighted isotropic MR and manually contoured gross tumor volumes (GTV) were used for model development. A 3D convolutional neural network (CNN) was built on ResNet blocks. Spatial attenuation and deep supervision modules were integrated in each decoder level to enhance the training for the small tumor volume on brain MRI. The model was trained and tested on 587 and 150 patient data, respectively, from this institution (n = 495) and a publicly available dataset (n = 242). The model performance were assessed by the Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), average symmetric surface (ASSD) and relative absolute volume difference (RAVD) of the model segmentation results against the GTVs. Results Measured on combined testing data from two institutions, the proposed method achieved mean DSC of 0.91 ± 0.08, ASSD of 0.3 ± 0.4 mm, HD95 of 1.3 ± 1.6 mm, and RAVD of 0.09 ± 0.15. The DSCs were 0.91 ± 0.09 and 0.92 ± 0.06 on 100 testing patients of this institution and 50 of the public data, respectively. Conclusions A CNN model was developed for fully automated segmentation of VS on T1-Weighted isotropic MRI. The model achieved good performance compared with physician clinical delineations on a sizeable dataset from two institutions. The proposed method potentially facilitates clinical workflow of radiosurgery for VS patient management.
Related Links	https://ro-journal.biomedcentral.com/counter/pdf/10.1186/s13014-023-02263-y.pdf
Ending Page	9
Page Count	9
Starting Page	1
File Format	HTM / HTML
DOI	10.1186/s13014-023-02263-y
Journal	Radiation Oncology
Issue Number	1
Volume Number	18
Language	English
Publisher	BioMed Central
Publisher Date	2023-05-08
Access Restriction	Open
Subject Keyword	Cancer Research Oncology Radiotherapy Imaging Radiology Image segmentation Vestibular schwannomas Radiosurgery Deep neural network MRI
Content Type	Text
Resource Type	Article
Subject	Radiology, Nuclear Medicine and Imaging Oncology
Journal Impact Factor	3.3/2023
5-Year Journal Impact Factor	3.6/2023

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