Fully Automated 3D Vestibular Schwannoma Segmentation with and                     without Gadolinium-based Contrast Material: A Multicenter, Multivendor                     Study

Neve, Olaf M.; Chen, Yunjie; Tao, Qian; Romeijn, Stephan R.; Boer, Nick P. de; Grootjans, Willem; Kruit, Mark C.; Lelieveldt, Boudewijn P. F.; Jansen, Jeroen C.; Hensen, Erik F.; Verbist, Berit M.; Staring, Marius

doi:10.1148/ryai.210300

Cited by 24 publications

(35 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At the time of writing this manuscript, a single study similar to ours was published that employed multi-centre data to develop VS segmentation models. 14 Similarities and differences between both studies are highlighted in the Discussion section of our work. Moreover, we devised a multi-stage annotation pipeline that involved a detailed review of each VS segmentation, to obtain high-quality manual VS segmentations for all 3D-images.…”

Section: Introductionsupporting

confidence: 60%

See 1 more Smart Citation

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

Kujawa

Dorent

Connor

et al. 2022

Preprint

View full text Add to dashboard Cite

ObjectiveAutomatic segmentation of vestibular schwannoma (VS) from routine clinical MRI can improve clinical workflow, facilitate treatment decisions, and assist patient management. Previously, excellent automatic segmentation results were achieved on datasets of standardised MRI images acquired for stereotactic surgery planning. However, diagnostic clinical datasets are generally more diverse and pose a larger challenge to automatic segmentation algorithms. Here, we show that automatic segmentation of VS on such datasets is also possible with high accuracy.MethodsWe acquired a large multi-centre routine clinical (MC-RC) dataset of 168 patients with a single sporadic VS who were referred from 10 medical sites and consecutively seen at a single centre. Up to three longitudinal MRI exams were selected for each patient. Selection rules based on image modality, resolution orientation, and acquisition timepoint were defined to automatically select contrast-enhanced T1-weighted (ceT1w) images (n=130) and T2-weighted images (n=379). Manual ground truth segmentations were obtained in an iterative process in which segmentations were: 1) produced or amended by a specialized company; and 2) reviewed by one of three trained radiologists; and 3) validated by an expert team. Inter- and intra-observer reliability was assessed on a subset of 10 ceT1w and 41 T2w images. The MC-RC dataset was split randomly into 3 nonoverlapping sets for model training, hyperparameter-tuning and testing in proportions 70/10/20%. We applied deep learning to train our VS segmentation model, based on convolutional neural networks (CNN) within the nnU-Net framework.ResultsOur model achieved excellent Dice scores when evaluated on the MC-RC testing set as well as the public testing set. On the MC-RC testing set, Dice scores were 90.8±4.5% for ceT1w, 86.1±11.6% for T2w and 82.3±18.4% for a combined ceT1w+T2w input.ConclusionsWe developed a model for automatic VS segmentation on diverse multi-centre clinical datasets. The results show that the performance of the framework is comparable to that of human annotators. In contrast, a model trained a publicly available dataset acquired for Gamma Knife stereotactic radiosurgery did not perform well on the MC-RC testing set. The application of our model has the potential to greatly facilitate the management of patients in clinical practice. Our pre-trained segmentation models are made available online. Moreover, we are in the process of making the MC-RC dataset publicly available.

show abstract

Section: Introductionsupporting

confidence: 60%

“…The authors employ ceT1w and T2w imaging data from 37 different centres. 14 In contrast to our study, no longitudinal imaging data but only one time point per patient were considered. Furthermore, all post-operative images were excluded from the model training and analysis.…”

Section: Discussionmentioning

confidence: 92%

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

Kujawa

Dorent

Connor

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Previous studies have shown the feasibility of implementing an automated VS segmentation system to analyze magnetic resonance images (3)(4)(5)21) In this context, we developed a pipeline that automatically segments VS tumors and ipsilateral inner ear structures using T1wC and T2+ images, respectively, and analyzes the three-dimensional spatial relationship between the tumor and inner ear (Fig. 2).…”

Section: Discussionmentioning

confidence: 99%

“…Recent studies describe automated methods that can accurately measure VS tumor volume from magnetic resonance imaging (MRI) using deep learning (DL) algorithms (3)(4)(5). Automated methods can facilitate screening for these lesions and assessing change in size over time.…”

Section: Introductionmentioning

confidence: 99%

“…Automated methods can facilitate screening for these lesions and assessing change in size over time. Automated DL approaches might provide insight into the clinical importance of tumor configurations within the IAC and CPA (5). Analysis of tumor geometry-including surface area, eccentricity (elongation, roundness), and proximity to the inner ear-may reveal objective correlates with otologic dysfunction seen in VS patients (6)(7)(8)(9)(10).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automated Radiomic Analysis of Vestibular Schwannomas and Inner Ears Using Contrast-Enhanced T1-Weighted and T2-Weighted Magnetic Resonance Imaging Sequences and Artificial Intelligence

et al. 2023

View full text Add to dashboard Cite

Objective To objectively evaluate vestibular schwannomas (VSs) and their spatial relationships with the ipsilateral inner ear (IE) in magnetic resonance imaging (MRI) using deep learning. Study Design Cross-sectional study. Patients A total of 490 adults with VS, high-resolution MRI scans, and no previous neurotologic surgery. Interventions MRI studies of VS patients were split into training (390 patients) and test (100 patients) sets. A three-dimensional convolutional neural network model was trained to segment VS and IE structures using contrast-enhanced T1-weighted and T2-weighted sequences, respectively. Manual segmentations were used as ground truths. Model performance was evaluated on the test set and on an external set of 100 VS patients from a public data set (Vestibular-Schwannoma-SEG). Main Outcome Measure(s) Dice score, relative volume error, average symmetric surface distance, 95th-percentile Hausdorff distance, and centroid locations. Results Dice scores for VS and IE volume segmentations were 0.91 and 0.90, respectively. On the public data set, the model segmented VS tumors with a Dice score of 0.89 ± 0.06 (mean ± standard deviation), relative volume error of 9.8 ± 9.6%, average symmetric surface distance of 0.31 ± 0.22 mm, and 95th-percentile Hausdorff distance of 1.26 ± 0.76 mm. Predicted VS segmentations overlapped with ground truth segmentations in all test subjects. Mean errors of predicted VS volume, VS centroid location, and IE centroid location were 0.05 cm3, 0.52 mm, and 0.85 mm, respectively. Conclusions A deep learning system can segment VS and IE structures in high-resolution MRI scans with excellent accuracy. This technology offers promise to improve the clinical workflow for assessing VS radiomics and enhance the management of VS patients.

show abstract

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

et al. 2022

View full text Add to dashboard Cite

ObjectiveTo 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).MethodsInitial 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 evaluating the TS and TEP modules, respectively.ResultsWe performed 5‐fold cross‐validation with the eligible patients (n = 103). Median segmentation dice coefficient, prediction sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) were measured and achieved the following values: 84.20%, 0.68, 0.78, 0.72, and 0.77, respectively. The segmentation result is significantly better than the separate TS network (dice coefficient of 83.13%, p = 0.03) and marginally lower than the state‐of‐the‐art segmentation model nnU‐Net (dice coefficient of 86.45%, p = 0.16). The TEP performance is significantly better than the single‐task prediction model (AUC = 0.60, p = 0.01) and marginally better than a radiomics‐based prediction model (AUC = 0.70, p = 0.17).ConclusionThe proposed DMT model is of higher learning efficiency and achieves promising performance on TEP and TS. The proposed technology has the potential to improve VS patient management.Level of EvidenceNA Laryngoscope, 133:2754–2760, 2023

show abstract

Fully Automated 3D Vestibular Schwannoma Segmentation with and without Gadolinium-based Contrast Material: A Multicenter, Multivendor Study

Cited by 24 publications

References 19 publications

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

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

Automated Radiomic Analysis of Vestibular Schwannomas and Inner Ears Using Contrast-Enhanced T1-Weighted and T2-Weighted Magnetic Resonance Imaging Sequences and Artificial Intelligence

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

Contact Info

Product

Resources

About