Article

Deep Learning for Automatic Segmentation of Vestibular Schwannoma: A Retrospective Study from Multi-Centre Routine MRI

2022-08-02

Abstract excerpt

<h4>ABSTRACT</h4> Automatic segmentation of vestibular schwannoma (VS) from routine clinical MRI has potential to improve clinical workflow, facilitate treatment decisions, and assist patient management. Previous work demonstrated reliable automatic segmentation performance on datasets of standardised MRI images acquired for stereotactic surgery planning. However, diagnostic clinical datasets are generally more di...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 35c67c39-345d-5907-ba86-e213b829051d
DOI: 10.1101/2022.08.01.22278193

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Deep Learning for Automatic Segmentation of Vestibular Schwannoma: A Retrospective Study from Multi-Centre Routine MRIDOI 10.1101/2022.08.01.22278193Read

AI Segmentation of Vestibular Schwannomas with Radiomic Analysis and Clinical CorrelatesSemantic proximity Read

Artificial intelligence for personalized management of vestibular schwannoma: A clinical implementation study within a multidisciplinary decision making environmentSemantic proximity Read

Automated Pediatric Brain Tumor Imaging Assessment Tool from CBTN: Enhancing Suprasellar Region Inclusion and Managing Limited Data with Deep LearningSemantic proximity Read

Advancing Precision: A Comprehensive Review of MRI Segmentation Datasets from BraTS Challenges (2012–2024)Semantic proximity Read

Automated Tumor Segmentation and Brain Tissue Extraction from Multiparametric MRI of Pediatric Brain Tumors: A Multi-Institutional StudySemantic proximity Read

Deep Learning for Automated Meningioma Segmentation: Toward Clinical Integration and Workflow EfficiencySemantic proximity Read

Automated Deep Learning–Based Demyelination Load Segmentation in Metachromatic LeukodystrophySemantic proximity Read

Expert-level pediatric brain tumor segmentation in a limited data scenario with stepwise transfer learningSemantic proximity Read

MRICombo: a deep-learning-based framework for universal volume segmentation, grading-staging, and malignancy detection across heterogeneous MRISemantic proximity Read

Segmenting with Confidence: Uncertainty Quantification for Brain Tumor ImagingSemantic proximity Read

Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networksSemantic proximity Read

Automatic Segmentation of Ameloblastoma on CT Images using Deep Learning with Limited DataSemantic proximity Read