ArticleBioengineering (Basel, Switzerland)2023
Comparing 3D, 2.5D, and 2D Approaches to Brain Image Auto-Segmentation.
Article in Bioengineering (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 3 of them syntheses that pooled it.
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Who cites it
53 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- The use of machine learning models for subdural hematoma detection: a single-arm meta-analysis.Neurosurgical review · 2026Pooled it
- Artificial intelligence-based models for quantification of intra-pancreatic fat deposition and their clinical relevance: a systematic review of imaging studies.European radiology · 2026Pooled it
- Deep learning for MRI-based acute and subacute ischaemic stroke lesion segmentation-a systematic review, meta-analysis, and pilot evaluation of key results.Frontiers in medical technology · 2025Pooled it
- Zero-Shot Self-Supervised Learning of Single Breath-Hold Magnetic Resonance Cholangiopancreatography (MRCP) Reconstruction.Magnetic resonance in medicine · 2026Article
- CT-based deep learning for survival stratification in EGFR-mutant lung adenocarcinoma after EGFR-TKI resistance: A multicenter study.iScience · 2026Article
- Super-resolution MRI and 2.5D deep learning for intratumoral-peritumoral radiomics in preoperative prediction of rectal cancer perineural invasion.Insights into imaging · 2026Article
- A generative adversarial network model for improved three-dimensional mapping of pulmonary arteries and veins from non-contrast computed tomography in sublobar resection planning.Quantitative imaging in medicine and surgery · 2026Article
- GDNet: A Robust 2.5D Multimodal MRI Brain Tumor Segmentation Framework with EMA Stabilization and Tumor-Aware Sampling.Journal of imaging · 2026Article
- KneeXNet-2.5D: a clinically-oriented and explainable deep learning framework for MRI-based knee cartilage and meniscus segmentation.npj health systems · 2026Article
- AI-Based Pulmonary Embolism Detection: The Added Value of a False-Positive Reduction Module over a Region Proposal Network.Diagnostics (Basel, Switzerland) · 2026Article
- Challenges in Using Deep Neural Networks Across Multiple Readers in Delineating Prostate Gland Anatomy.Journal of imaging informatics in medicine · 2026Article
- SynPoC: a high-quality generative diffusion model for transforming ultra-low-field point-of-care MRI using high-field MRI representations.Scientific reports · 2026Article
- Deep learning based volumetric analysis of infrarenal abdominal aortic aneurysms characterized on CTA.NPJ digital medicine · 2026Article
- Advancing Medical Decision-Making with AI: A Comprehensive Exploration of the Evolution from Convolutional Neural Networks to Capsule Networks.Journal of imaging · 2025Review
- Development of a hybrid 2.5D deep learning model for glioma survival prediction using T1-weighted MRI from the CGGA database.European journal of radiology open · 2025Article
- Comparison of 2D, 2.5D, and 3D landmark localization networks for 3D cephalometry in CT images.BMC oral health · 2025Article
- Incremental 2D self-labelling for effective 3D medical volume segmentation with minimal annotations.BMC medical imaging · 2025Article
- A generalizable 3D framework and model for self-supervised learning in medical imaging.NPJ digital medicine · 2025Article
- 2.5D Multi-View Averaging Diffusion Model for 3D Medical Image Translation: Application to Low-Count PET Reconstruction With CT-Less Attenuation Correction.IEEE transactions on medical imaging · 2025Article
- A deep learning-based approach for measuring patellar cartilage deformations from knee MR images.Journal of biomechanics · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Deep-learning methods for auto-segmenting brain images either segment one slice of the image (2D), five consecutive slices of the image (2.5D), or an entire volume of the image (3D). Whether one approach is superior for auto-segmenting brain images is not known. We compared these three approaches (3D, 2.5D, and 2D) across three auto-segmentation models (capsule networks, UNets, and nnUNets) to segment brain structures. We used 3430 brain MRIs, acquired in a multi-institutional study, to train and test our models. We used the following performance metrics: segmentation accuracy, performance with limited training data, required computational memory, and computational speed during training and deployment. The 3D, 2.5D, and 2D approaches respectively gave the highest to lowest Dice scores across all models. 3D models maintained higher Dice scores when the training set size was decreased from 3199 MRIs down to 60 MRIs. 3D models converged 20% to 40% faster during training and were 30% to 50% faster during deployment. However, 3D models require 20 times more computational memory compared to 2.5D or 2D models. This study showed that 3D models are more accurate, maintain better performance with limited training data, and are faster to train and deploy. However, 3D models require more computational memory compared to 2.5D or 2D models.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.