ArticleActa neurochirurgica2024
NnU-Net versus mesh growing algorithm as a tool for the robust and timely segmentation of neurosurgical 3D images in contrast-enhanced T1 MRI scans.
Article in Acta neurochirurgica, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning in neurosurgery: a systematic literature review with a structured analysis of applications across subspecialties.Frontiers in neurology · 2025Pooled it
- Comparing augmented reality-assisted and freehand external ventricular drain placement: a multicenter randomized controlled crossover phantom study.Acta neurochirurgica · 2025Trial
- AI-Assisted Papillary Thyroid Carcinoma Localization on Cervical Non-contrast CT: A Multicenter Validation and Two-Reader Pilot Study of a Standalone Executable nnU-Net Software.Journal of imaging informatics in medicine · 2026Article
- A human-AI collaborative workflow for improved gross tumor volume delineation in esophageal cancer radiotherapy.BMC medical imaging · 2026Article
- Precision surgery in the era of 3D visualization, AR/VR, and 3D printing: current applications and future directions.Frontiers in medicine · 2026Review
- Application of deep neural networks in automatized ventriculometry and segmentation of the aqueduct in pediatric hydrocephalus patients.Neuroradiology · 2026Article
- Deep Learning Based Automatic Ankle Tenosynovitis Quantification from MRI in Patients with Psoriatic Arthritis: A Feasibility Study.Diagnostics (Basel, Switzerland) · 2025Article
- C2FAU-Net: A Deep Learning Approach with Multi-scale Strategy for Automated Delineation of Organs-at-risk in Cervical Cancer High-dose Rate Brachytherapy.Journal of medical physicsArticle
- Automated pediatric brain tumor imaging assessment tool from CBTN: Enhancing suprasellar region inclusion and managing limited data with deep learning.Neuro-oncology advancesArticle
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Authors and funding
10 authors.
Funding
Abstract
purposeThis study evaluates the nnU-Net for segmenting brain, skin, tumors, and ventricles in contrast-enhanced T1 (T1CE) images, benchmarking it against an established mesh growing algorithm (MGA).
methodsWe used 67 retrospectively collected annotated single-center T1CE brain scans for training models for brain, skin, tumor, and ventricle segmentation. An additional 32 scans from two centers were used test performance compared to that of the MGA. The performance was measured using the Dice-Sørensen coefficient (DSC), intersection over union (IoU), 95th percentile Hausdorff distance (HD95), and average symmetric surface distance (ASSD) metrics, with time to segment also compared.
resultsThe nnU-Net models significantly outperformed the MGA (p < 0.0125) with a median brain segmentation DSC of 0.971 [95CI: 0.945-0.979], skin: 0.997 [95CI: 0.984-0.999], tumor: 0.926 [95CI: 0.508-0.968], and ventricles: 0.910 [95CI: 0.812-0.968]. Compared to the MGA's median DSC for brain: 0.936 [95CI: 0.890, 0.958], skin: 0.991 [95CI: 0.964, 0.996], tumor: 0.723 [95CI: 0.000-0.926], and ventricles: 0.856 [95CI: 0.216-0.916]. NnU-Net performance between centers did not significantly differ except for the skin segmentations Additionally, the nnU-Net models were faster (mean: 1139 s [95CI: 685.0-1616]) than the MGA (mean: 2851 s [95CI: 1482-6246]).
conclusionsThe nnU-Net is a fast, reliable tool for creating automatic deep learning-based segmentation pipelines, reducing the need for extensive manual tuning and iteration. The models are able to achieve this performance despite a modestly sized training set. The ability to create high-quality segmentations in a short timespan can prove invaluable in neurosurgical settings.
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