# Bridging 2D and 3D Segmentation Networks for Computation Efficient Volumetric Medical Image Segmentation: An Empirical Study of 2.5D Solutions arXiv: https://arxiv.org/abs/2010.06163 ยท HTML: https://mlsanity.com/paper/2010.06163/ ## Key facts - Cited by 31 papers in the map (data through 14 Oct 2026). - First posted on arXiv 13 Oct 2020. ## Built on by (most cited first, 30 of 31) - [Segment Anything Model for Medical Image Segmentation: Current Applications and Future Directions](https://mlsanity.com/paper/2401.03495/) (2024) - [Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation](https://mlsanity.com/paper/2207.14191/) (2022) - [Uncertainty-Guided Mutual Consistency Learning for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2112.02508/) (2021) - [Towards Segment Anything Model (SAM) for Medical Image Segmentation: A Survey](https://mlsanity.com/paper/2305.03678/) (2023) - [SemiSAM: Enhancing Semi-Supervised Medical Image Segmentation via SAM-Assisted Consistency Regularization](https://mlsanity.com/paper/2312.06316/) (2023) - [Unleashing the Potential of SAM2 for Biomedical Images and Videos: A Survey](https://mlsanity.com/paper/2408.12889/) (2024) - [Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model](https://mlsanity.com/paper/2505.04522/) (2025) - [CTFlow: Video-Inspired Latent Flow Matching for 3D CT Synthesis](https://mlsanity.com/paper/2508.12900/) (2025) - [Beyond Pixel-Wise Supervision for Medical Image Segmentation: From Traditional Models to Foundation Models](https://mlsanity.com/paper/2404.13239/) (2024) - [Deep learning-based neurodevelopmental assessment in preterm infants](https://mlsanity.com/paper/2601.11944/) (2026) - [MOSformer: Momentum encoder-based inter-slice fusion transformer for medical image segmentation](https://mlsanity.com/paper/2401.11856/) (2024) - [Generalist Models in Medical Image Segmentation: A Survey and Performance Comparison with Task-Specific Approaches](https://mlsanity.com/paper/2506.10825/) (2025) - [MOSAIC: A Multi-View 2.5D Organ Slice Selector with Cross-Attentional Reasoning for Anatomically-Aware CT Localization in Medical Organ Segmentation](https://mlsanity.com/paper/2505.10672/) (2025) - [Plug-and-Play with 2.5D Artifact Reduction Prior for Fast and Accurate Industrial Computed Tomography Reconstruction](https://mlsanity.com/paper/2506.14719/) (2025) - [Pixels-to-Graph: Real-time Integration of Building Information Models and Scene Graphs for Semantic-Geometric Human-Robot Understanding](https://mlsanity.com/paper/2506.22593/) (2025) - [SRUG: A Fusion-Driven Generator Network for Medical Image Translation](https://mlsanity.com/paper/2601.04785/) (2026) - [Universality Reconsidered: Rethinking the Validation of Foundation Models for General-Purpose 3D Medical Segmentation](https://mlsanity.com/paper/2602.07643/) (2026) - [Developing Foundation Models for Universal Segmentation from 3D Whole-Body Positron Emission Tomography](https://mlsanity.com/paper/2603.11627/) (2026) - [Spatiotemporal Modeling Encounters 3D Medical Image Analysis: Slice-Shift UNet with Multi-View Fusion](https://mlsanity.com/paper/2307.12853/) (2023) - [Adapting SAM for Volumetric X-Ray Data-sets of Arbitrary Sizes](https://mlsanity.com/paper/2403.12066/) (2024) - [Triage of 3D pathology data via 2.5D multiple-instance learning to guide pathologist assessments](https://mlsanity.com/paper/2406.07061/) (2024) - [iCBIR-Sli: Interpretable Content-Based Image Retrieval with 2D Slice Embeddings](https://mlsanity.com/paper/2501.01642/) (2025) - [Mitigating Pretraining-Induced Attention Asymmetry in 2D+ Electron Microscopy Image Segmentation](https://mlsanity.com/paper/2505.14105/) (2025) - [Leveraging Swin Transformer for enhanced diagnosis of Alzheimer's disease using multi-shell diffusion MRI](https://mlsanity.com/paper/2507.09996/) (2025) - [XAG-Net: A Cross-Slice Attention and Skip Gating Network for 2.5D Femur MRI Segmentation](https://mlsanity.com/paper/2508.06258/) (2025) - [PULSE: A Unified Multi-Task Architecture for Cardiac Segmentation, Diagnosis, and Few-Shot Cross-Modality Clinical Adaptation](https://mlsanity.com/paper/2512.03848/) (2025) - [Improving Neuropathological Reconstruction Fidelity via AI Slice Imputation](https://mlsanity.com/paper/2602.00669/) (2026) - [YOLO-PVC: 2D-to-3D Consolidation of Slice-wise Detections for Volumetric Liver Tumor Localization in MRI](https://mlsanity.com/paper/2608.04642/) (2026) - [Label-Free Deep-Tissue Peripheral Nerve Detection with a Handheld Multimodal OCT Probe and NerveDetNet](https://mlsanity.com/paper/2608.13807/) (2026) - [Neoadjuvant chemotherapy response prediction using pretreatment diffusion and contrast-enhanced magnetic resonance imaging with clinical variables](https://mlsanity.com/paper/2609.26105/) (2026) ## Abstract (arXiv) Recently, deep convolutional neural networks have achieved great success for medical image segmentation. However, unlike segmentation of natural images, most medical images such as MRI and CT are volumetric data. In order to make full use of volumetric information, 3D CNNs are widely used. However, 3D CNNs suffer from higher inference time and computation cost, which hinders their further clinical applications. Additionally, with the increased number of parameters, the risk of overfitting is higher, especially for medical images where data and annotations are expensive to acquire. To issue this problem, many 2.5D segmentation methods have been proposed to make use of volumetric spatial information with less computation cost. Despite these works lead to improvements on a variety of segmentation tasks, to the best of our knowledge, there has not previously been a large-scale empirical comparison of these methods. In this paper, we aim to present a review of the latest developments of 2.5D methods for volumetric medical image segmentation. Additionally, to compare the performance and effectiveness of these methods, we provide an empirical study of these methods on three representative segmentation tasks involving different modalities and targets. Our experimental results highlight that 3D CNNs may not always be the best choice. Despite all these 2.5D methods can bring performance gains to 2D baseline, not all the methods hold the benefits on different datasets. We hope the results and conclusions of our study will prove useful for the community on exploring and developing efficient volumetric medical image segmentation methods.