Joint Vestibular Schwannoma Enlargement Prediction and Segmentation Using a Deep Multi-task Model.

ABSTRACT:

Objective

To develop a deep-learning-based multi-task (DMT) model for joint tumor enlargement prediction (TEP) and automatic tumor segmentation (TS) for vestibular schwannoma (VS) patients using their initial diagnostic contrast-enhanced T1-weighted (ceT1) magnetic resonance images (MRIs).

Methods

Initial ceT1 MRIs for VS patients meeting the inclusion/exclusion criteria of this study were retrospectively collected. VSs on the initial MRIs and their first follow-up scans were manually contoured. Tumor volume and enlargement ratio were measured based on expert contours. A DMT model was constructed for jointly TS and TEP. The manually segmented VS volume on the initial scan and the tumor enlargement label (≥20% volumetric growth) were used as the ground truth for training and

SUBMITTER: Wang K 

PROVIDER: S-EPMC10256836 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature