A Clinical Guideline Driven Automated Linear Feature Extraction for Vestibular Schwannoma

TY - CHAP

T1 - A Clinical Guideline Driven Automated Linear Feature Extraction for Vestibular Schwannoma

AU - Wijethilake, Navodini

AU - Connor, Steve

AU - Oviedova, Anna

AU - Kujawa, Aaron

AU - Burger, Rebecca

AU - Vercauteren, Tom

AU - Shapey, Jonathan

N1 - Publisher Copyright: © 2024 SPIE.

PY - 2024/4/2

Y1 - 2024/4/2

N2 - 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.

AB - 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.

KW - Extrameatal

KW - Intrameatal

KW - Maximum Tumour Diameter

KW - Segmentation

KW - Vestibular Schwannoma

UR - http://www.scopus.com/inward/record.url?scp=85193525848&partnerID=8YFLogxK

U2 - 10.1117/12.3006526

DO - 10.1117/12.3006526

M3 - Conference paper

AN - SCOPUS:85193525848

T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE

BT - Medical Imaging 2024

A2 - Colliot, Olivier

A2 - Mitra, Jhimli

PB - SPIE

T2 - Medical Imaging 2024: Image Processing

Y2 - 19 February 2024 through 22 February 2024

ER -