# 3D Dilated Multi-Fiber Network for Real-time Brain Tumor Segmentation in MRI

arXiv: https://arxiv.org/abs/1904.03355 · HTML: https://mlsanity.com/paper/1904.03355/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2019.

## Built on by (most cited first, 28 of 28)

- [Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images](https://mlsanity.com/paper/2201.01266/) (2022)
- [SwinMM: Masked Multi-view with Swin Transformers for 3D Medical Image Segmentation](https://mlsanity.com/paper/2307.12591/) (2023)
- [Learning from partially labeled data for multi-organ and tumor segmentation](https://mlsanity.com/paper/2211.06894/) (2022)
- [Clustering Propagation for Universal Medical Image Segmentation](https://mlsanity.com/paper/2403.16646/) (2024)
- [Belief function-based semi-supervised learning for brain tumor segmentation](https://mlsanity.com/paper/2102.00097/) (2021)
- [Point-Unet: A Context-aware Point-based Neural Network for Volumetric Segmentation](https://mlsanity.com/paper/2203.08964/) (2022)
- [Multimodal Data Integration for Precision Oncology: Challenges and Future Directions](https://mlsanity.com/paper/2406.19611/) (2024)
- [LATUP-Net: A Lightweight 3D Attention U-Net with Parallel Convolutions for Brain Tumor Segmentation](https://mlsanity.com/paper/2404.05911/) (2024)
- [Leveraging Human Selective Attention for Medical Image Analysis with Limited Training Data](https://mlsanity.com/paper/2112.01034/) (2021)
- [DC-Seg: Disentangled Contrastive Learning for Brain Tumor Segmentation with Missing Modalities](https://mlsanity.com/paper/2505.11921/) (2025)
- [Med-DANet: Dynamic Architecture Network for Efficient Medical Volumetric Segmentation](https://mlsanity.com/paper/2206.06575/) (2022)
- [Category Guided Attention Network for Brain Tumor Segmentation in MRI](https://mlsanity.com/paper/2203.15383/) (2022)
- [Prototype Learning Guided Hybrid Network for Breast Tumor Segmentation in DCE-MRI](https://mlsanity.com/paper/2408.05803/) (2024)
- [Artificial Intelligence in Tumor Subregion Analysis Based on Medical Imaging: A Review](https://mlsanity.com/paper/2103.13588/) (2021)
- [FAS-UNet: A Novel FAS-driven Unet to Learn Variational Image Segmentation](https://mlsanity.com/paper/2210.15164/) (2022)
- [CASPIANET++: A Multidimensional Channel-Spatial Asymmetric Attention Network with Noisy Student Curriculum Learning Paradigm for Brain Tumor Segmentation](https://mlsanity.com/paper/2107.04099/) (2021)
- [Group Shift Pointwise Convolution for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2109.12629/) (2021)
- [Med-DANet V2: A Flexible Dynamic Architecture for Efficient Medical Volumetric Segmentation](https://mlsanity.com/paper/2310.18656/) (2023)
- [Nested-block self-attention for robust radiotherapy planning segmentation](https://mlsanity.com/paper/2102.13541/) (2021)
- [A Novel SLCA-UNet Architecture for Automatic MRI Brain Tumor Segmentation](https://mlsanity.com/paper/2307.08048/) (2023)
- [Abdominal multi-organ segmentation in CT using Swinunter](https://mlsanity.com/paper/2309.16210/) (2023)
- [MBDRes-U-Net: Multi-Scale Lightweight Brain Tumor Segmentation Network](https://mlsanity.com/paper/2411.01896/) (2024)
- [UPMAD-Net: A Brain Tumor Segmentation Network with Uncertainty Guidance and Adaptive Multimodal Feature Fusion](https://mlsanity.com/paper/2505.03494/) (2025)
- [Mutual Evidential Deep Learning for Medical Image Segmentation](https://mlsanity.com/paper/2505.12418/) (2025)
- [Unified Start, Personalized End: Progressive Pruning for Efficient 3D Medical Image Segmentation](https://mlsanity.com/paper/2509.09267/) (2025)
- [Advances in Medical Image Segmentation: A Comprehensive Survey with a Focus on Lumbar Spine Applications](https://mlsanity.com/paper/2510.03318/) (2025)
- [TRACE-Seg3D: Counterfactual Context Auditing For Robust 3D Glioma Segmentation Under Institutional Shift](https://mlsanity.com/paper/2607.07038/) (2026)
- [Uni-Light: An Ultra-Lightweight Framework via Uncertainty-Aware Knowledge Distillation for Brain Tumour Segmentation](https://mlsanity.com/paper/2609.06729/) (2026)

## Abstract (arXiv)

Brain tumor segmentation plays a pivotal role in medical image processing. In this work, we aim to segment brain MRI volumes. 3D convolution neural networks (CNN) such as 3D U-Net and V-Net employing 3D convolutions to capture the correlation between adjacent slices have achieved impressive segmentation results. However, these 3D CNN architectures come with high computational overheads due to multiple layers of 3D convolutions, which may make these models prohibitive for practical large-scale applications. To this end, we propose a highly efficient 3D CNN to achieve real-time dense volumetric segmentation. The network leverages the 3D multi-fiber unit which consists of an ensemble of lightweight 3D convolutional networks to significantly reduce the computational cost. Moreover, 3D dilated convolutions are used to build multi-scale feature representations. Extensive experimental results on the BraTS-2018 challenge dataset show that the proposed architecture greatly reduces computation cost while maintaining high accuracy for brain tumor segmentation. The source code can be found at https://github.com/China-LiuXiaopeng/BraTS-DMFNet