# D-UNet: a dimension-fusion U shape network for chronic stroke lesion segmentation

arXiv: https://arxiv.org/abs/1908.05104 · HTML: https://mlsanity.com/paper/1908.05104/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2019.

## Built on by (most cited first, 24 of 24)

- [Swin-UMamba: Mamba-based UNet with ImageNet-based pretraining](https://mlsanity.com/paper/2402.03302/) (2024)
- [SAN-Net: Learning Generalization to Unseen Sites for Stroke Lesion Segmentation with Self-Adaptive Normalization](https://mlsanity.com/paper/2205.04329/) (2022)
- [Small Lesion Segmentation in Brain MRIs with Subpixel Embedding](https://mlsanity.com/paper/2109.08791/) (2021)
- [Enhancing the vision-language foundation model with key semantic knowledge-emphasized report refinement](https://mlsanity.com/paper/2401.11421/) (2024)
- [Transformers-based architectures for stroke segmentation: A review](https://mlsanity.com/paper/2403.18637/) (2024)
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- [Semi-Supervised and Self-Supervised Collaborative Learning for Prostate 3D MR Image Segmentation](https://mlsanity.com/paper/2211.08840/) (2022)
- [Meta-Analysis of Transfer Learning for Segmentation of Brain Lesions](https://mlsanity.com/paper/2306.11714/) (2023)
- [Enhancing Representation in Medical Vision-Language Foundation Models via Multi-Scale Information Extraction Techniques](https://mlsanity.com/paper/2401.01583/) (2024)
- [Deep models for stroke segmentation: do complex architectures always perform better?](https://mlsanity.com/paper/2403.17177/) (2024)
- [R2U3D: Recurrent Residual 3D U-Net for Lung Segmentation](https://mlsanity.com/paper/2105.02290/) (2021)
- [Hybrid Dual Mean-Teacher Network With Double-Uncertainty Guidance for Semi-Supervised Segmentation of MRI Scans](https://mlsanity.com/paper/2303.05126/) (2023)
- [AGMR-Net: Attention Guided Multiscale Recovery framework for stroke segmentation](https://mlsanity.com/paper/2202.13687/) (2022)
- [DIGEST: Deeply supervIsed knowledGE tranSfer neTwork learning for brain tumor segmentation with incomplete multi-modal MRI scans](https://mlsanity.com/paper/2211.07993/) (2022)
- [FAN-Net: Fourier-Based Adaptive Normalization For Cross-Domain Stroke Lesion Segmentation](https://mlsanity.com/paper/2304.11557/) (2023)
- [BeSt-LeS: Benchmarking Stroke Lesion Segmentation using Deep Supervision](https://mlsanity.com/paper/2310.07060/) (2023)
- [CV-Attention UNet: Attention-based UNet for 3D Cerebrovascular Segmentation of Enhanced TOF-MRA Images](https://mlsanity.com/paper/2311.10224/) (2023)
- [Collaborative Learning for Annotation-Efficient Volumetric MR Image Segmentation](https://mlsanity.com/paper/2312.10978/) (2023)
- [Stroke Lesion Segmentation with Visual Cortex Anatomy Alike Neural Nets](https://mlsanity.com/paper/2105.06544/) (2021)
- [MGA: Medical generalist agent through text-guided knowledge transformation](https://mlsanity.com/paper/2303.08562/) (2023)
- [CarbonNet: How Computer Vision Plays a Role in Climate Change? Application: Learning Geomechanics from Subsurface Geometry of CCS to Mitigate Global Warming](https://mlsanity.com/paper/2403.06025/) (2024)
- [KANDU-Net:A Dual-Channel U-Net with KAN for Medical Image Segmentation](https://mlsanity.com/paper/2409.20414/) (2024)
- [Multi-View MRI Approach for Classification of MGMT Methylation in Glioblastoma Patients](https://mlsanity.com/paper/2512.14232/) (2025)
- [VRXU-net: A Deep Learning Approach for Brain Ischemic Stroke Lesion Detection and Segmentation in T1W MRI](https://mlsanity.com/paper/2605.21633/) (2026)

## Abstract (arXiv)

Assessing the location and extent of lesions caused by chronic stroke is critical for medical diagnosis, surgical planning, and prognosis. In recent years, with the rapid development of 2D and 3D convolutional neural networks (CNN), the encoder-decoder structure has shown great potential in the field of medical image segmentation. However, the 2D CNN ignores the 3D information of medical images, while the 3D CNN suffers from high computational resource demands. This paper proposes a new architecture called dimension-fusion-UNet (D-UNet), which combines 2D and 3D convolution innovatively in the encoding stage. The proposed architecture achieves a better segmentation performance than 2D networks, while requiring significantly less computation time in comparison to 3D networks. Furthermore, to alleviate the data imbalance issue between positive and negative samples for the network training, we propose a new loss function called Enhance Mixing Loss (EML). This function adds a weighted focal coefficient and combines two traditional loss functions. The proposed method has been tested on the ATLAS dataset and compared to three state-of-the-art methods. The results demonstrate that the proposed method achieves the best quality performance in terms of DSC = 0.5349+0.2763 and precision = 0.6331+0.295).