# UNETR++: Delving into Efficient and Accurate 3D Medical Image Segmentation

arXiv: https://arxiv.org/abs/2212.04497 · HTML: https://mlsanity.com/paper/2212.04497/

## Key facts

- Cited by 112 papers in the map (data through 14 Oct 2026).
- Builds on 40 of its 52 references that are in the map.
- First posted on arXiv 8 Dec 2022.

## Builds on (most cited first, 30 of 40)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Reformer: The Efficient Transformer](https://mlsanity.com/paper/2001.04451/) (2020)
- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021)
- [Generating Long Sequences with Sparse Transformers](https://mlsanity.com/paper/1904.10509/) (2019)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Linformer: Self-Attention with Linear Complexity](https://mlsanity.com/paper/2006.04768/) (2020)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2105.05537/) (2021)
- [UNETR: Transformers for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.10504/) (2021)
- [Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images](https://mlsanity.com/paper/2201.01266/) (2022)
- [MONAI: An open-source framework for deep learning in healthcare](https://mlsanity.com/paper/2211.02701/) (2022)
- [A large annotated medical image dataset for the development and evaluation of segmentation algorithms](https://mlsanity.com/paper/1902.09063/) (2019)
- [UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation](https://mlsanity.com/paper/2004.08790/) (2020)
- [Medical Transformer: Gated Axial-Attention for Medical Image Segmentation](https://mlsanity.com/paper/2102.10662/) (2021)
- [TransBTS: Multimodal Brain Tumor Segmentation Using Transformer](https://mlsanity.com/paper/2103.04430/) (2021)
- [TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation](https://mlsanity.com/paper/2102.08005/) (2021)
- [CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.03024/) (2021)
- [Large Kernel Matters -- Improve Semantic Segmentation by Global Convolutional Network](https://mlsanity.com/paper/1703.02719/) (2017)
- [Metrics reloaded: Recommendations for image analysis validation](https://mlsanity.com/paper/2206.01653/) (2022)
- [nnFormer: Interleaved Transformer for Volumetric Segmentation](https://mlsanity.com/paper/2109.03201/) (2021)

## Built on by (most cited first, 30 of 112)

- [3DSAM-adapter: Holistic adaptation of SAM from 2D to 3D for promptable tumor segmentation](https://mlsanity.com/paper/2306.13465/) (2023)
- [SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications](https://mlsanity.com/paper/2303.15446/) (2023)
- [Large AI Models in Health Informatics: Applications, Challenges, and the Future](https://mlsanity.com/paper/2303.11568/) (2023)
- [SAM3D: Segment Anything Model in Volumetric Medical Images](https://mlsanity.com/paper/2309.03493/) (2023)
- [Beyond Self-Attention: Deformable Large Kernel Attention for Medical Image Segmentation](https://mlsanity.com/paper/2309.00121/) (2023)
- [MIS-FM: 3D Medical Image Segmentation using Foundation Models Pretrained on a Large-Scale Unannotated Dataset](https://mlsanity.com/paper/2306.16925/) (2023)
- [Primus: Enforcing Attention Usage for 3D Medical Image Segmentation](https://mlsanity.com/paper/2503.01835/) (2025)
- [Dino U-Net: Exploiting High-Fidelity Dense Features from Foundation Models for Medical Image Segmentation](https://mlsanity.com/paper/2508.20909/) (2025)
- [SegStitch: Multidimensional Transformer for Robust and Efficient Medical Imaging Segmentation](https://mlsanity.com/paper/2408.00496/) (2024)
- [Raptor: Scalable Train-Free Embeddings for 3D Medical Volumes Leveraging Pretrained 2D Foundation Models](https://mlsanity.com/paper/2507.08254/) (2025)
- [xLSTM-UNet can be an Effective 2D & 3D Medical Image Segmentation Backbone with Vision-LSTM (ViL) better than its Mamba Counterpart](https://mlsanity.com/paper/2407.01530/) (2024)
- [ReCo-KD: Region- and Context-Aware Knowledge Distillation for Efficient 3D Medical Image Segmentation](https://mlsanity.com/paper/2601.08301/) (2026)
- [NexToU: Efficient Topology-Aware U-Net for Medical Image Segmentation](https://mlsanity.com/paper/2305.15911/) (2023)
- [Towards a Comprehensive, Efficient and Promptable Anatomic Structure Segmentation Model using 3D Whole-body CT Scans](https://mlsanity.com/paper/2403.15063/) (2024)
- [A Comprehensive Review of U-Net and Its Variants: Advances and Applications in Medical Image Segmentation](https://mlsanity.com/paper/2502.06895/) (2025)
- [Attention Mechanisms in Medical Image Segmentation: A Survey](https://mlsanity.com/paper/2305.17937/) (2023)
- [D-Net: Dynamic Large Kernel with Dynamic Feature Fusion for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2403.10674/) (2024)
- [Rethinking Brain Tumor Segmentation from the Frequency Domain Perspective](https://mlsanity.com/paper/2506.10142/) (2025)
- [MAUP: Training-free Multi-center Adaptive Uncertainty-aware Prompting for Cross-domain Few-shot Medical Image Segmentation](https://mlsanity.com/paper/2508.03511/) (2025)
- [SeUNet-Trans: A Simple yet Effective UNet-Transformer Model for Medical Image Segmentation](https://mlsanity.com/paper/2310.09998/) (2023)
- [Does DINOv3 Set a New Medical Vision Standard? Benchmarking 2D and 3D Classification, Segmentation, and Registration](https://mlsanity.com/paper/2509.06467/) (2025)
- [Frequency Domain Adversarial Training for Robust Volumetric Medical Segmentation](https://mlsanity.com/paper/2307.07269/) (2023)
- [Self Pre-training with Topology- and Spatiality-aware Masked Autoencoders for 3D Medical Image Segmentation](https://mlsanity.com/paper/2406.10519/) (2024)
- [MobileUNETR: A Lightweight End-To-End Hybrid Vision Transformer For Efficient Medical Image Segmentation](https://mlsanity.com/paper/2409.03062/) (2024)
- [Sli2Vol+: Segmenting 3D Medical Images Based on an Object Estimation Guided Correspondence Flow Network](https://mlsanity.com/paper/2411.13873/) (2024)
- [Visual-Attribute Prompt Learning for Progressive Mild Cognitive Impairment Prediction](https://mlsanity.com/paper/2310.14158/) (2023)
- [No More Sliding Window: Efficient 3D Medical Image Segmentation with Differentiable Top-k Patch Sampling](https://mlsanity.com/paper/2501.10814/) (2025)
- [Enhancing Efficiency in Vision Transformer Networks: Design Techniques and Insights](https://mlsanity.com/paper/2403.19882/) (2024)
- [LHU-Net: a Lean Hybrid U-Net for Cost-efficient, High-performance Volumetric Segmentation](https://mlsanity.com/paper/2404.05102/) (2024)
- [HCMA-UNet: A Hybrid CNN-Mamba UNet with Axial Self-Attention for Efficient Breast Cancer Segmentation](https://mlsanity.com/paper/2501.00751/) (2025)

## Abstract (arXiv)

Owing to the success of transformer models, recent works study their applicability in 3D medical segmentation tasks. Within the transformer models, the self-attention mechanism is one of the main building blocks that strives to capture long-range dependencies. However, the self-attention operation has quadratic complexity which proves to be a computational bottleneck, especially in volumetric medical imaging, where the inputs are 3D with numerous slices. In this paper, we propose a 3D medical image segmentation approach, named UNETR++, that offers both high-quality segmentation masks as well as efficiency in terms of parameters, compute cost, and inference speed. The core of our design is the introduction of a novel efficient paired attention (EPA) block that efficiently learns spatial and channel-wise discriminative features using a pair of inter-dependent branches based on spatial and channel attention. Our spatial attention formulation is efficient having linear complexity with respect to the input sequence length. To enable communication between spatial and channel-focused branches, we share the weights of query and key mapping functions that provide a complimentary benefit (paired attention), while also reducing the overall network parameters. Our extensive evaluations on five benchmarks, Synapse, BTCV, ACDC, BRaTs, and Decathlon-Lung, reveal the effectiveness of our contributions in terms of both efficiency and accuracy. On Synapse, our UNETR++ sets a new state-of-the-art with a Dice Score of 87.2%, while being significantly efficient with a reduction of over 71% in terms of both parameters and FLOPs, compared to the best method in the literature. Code: https://github.com/Amshaker/unetr_plus_plus.