A clinical guideline driven automated linear feature extraction for vestibular schwannoma

Navodini Wijethilake; Steve Connor; Anna Oviedova; Aaron Kujawa; Rebecca Burger; Tom Vercauteren; Jonathan Shapey

doi:10.1117/12.3006526

2 April 2024 A clinical guideline driven automated linear feature extraction for vestibular schwannoma

Navodini Wijethilake, Steve Connor, Anna Oviedova, Aaron Kujawa, Rebecca Burger, Tom Vercauteren, Jonathan Shapey

Proceedings Volume 12926, Medical Imaging 2024: Image Processing; 129262J (2024) https://doi.org/10.1117/12.3006526
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

Vestibular Schwannoma is a benign brain tumour that grows from one of the balance nerves. Patients may be treated by surgery, radiosurgery or with a conservative ”wait-and-scan” strategy. Clinicians typically use manually extracted linear measurements to aid clinical decision making. This work aims to automate and improve this process by using deep learning based segmentation to extract relevant clinical features through computational algorithms. To the best of our knowledge, our study is the first to propose an automated approach to replicate local clinical guidelines. Our deep learning based segmentation provided Dice-scores of 0.8124 ± 0.2343 and 0.8969 ± 0.0521 for extrameatal and whole tumour regions respectively for T2 weighted MRI, whereas 0.8222 ± 0.2108 and 0.9049 ± 0.0646 were obtained for T1 weighted MRI. We propose a novel algorithm to choose and extract the most appropriate maximum linear measurement from the segmented regions based on the size of the extrameatal portion of the tumour. Using this tool, clinicians will be provided with a visual guide and related metrics relating to tumour progression that will function as a clinical decision aid. In this study, we utilize 187 scans obtained from 50 patients referred to a tertiary specialist neurosurgical service in the United Kingdom. The measurements extracted manually by an expert neuroradiologist indicated a significant correlation with the automated measurements (p < 0.0001). Our code is publicly available at https: //github.com/navodini/AutomatedReportGenerationVS.

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Navodini Wijethilake, Steve Connor, Anna Oviedova, Aaron Kujawa, Rebecca Burger, Tom Vercauteren, and Jonathan Shapey "A clinical guideline driven automated linear feature extraction for vestibular schwannoma", Proc. SPIE 12926, Medical Imaging 2024: Image Processing, 129262J (2 April 2024); https://doi.org/10.1117/12.3006526

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