3D Dilated Multi-Fiber Network for Real-time Brain Tumor Segmentation in MRI (1904.03355v5)

Abstract: 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

• The paper introduces DMFNet, a novel 3D CNN architecture that leverages multi-fiber and dilated convolutions to achieve real-time brain tumor segmentation with high accuracy and reduced computational cost.
• The network attains competitive Dice scores of 80.12%, 90.62%, and 84.54% for enhancing tumor, whole tumor, and tumor core while using 10 times fewer parameters and 55 times fewer FLOPs compared to state-of-the-art models.
• This approach enhances clinical diagnostics by providing an efficient, scalable solution for MRI-based brain tumor segmentation and sets a precedent for resource-efficient 3D medical image analysis.

Overview of the 3D Dilated Multi-Fiber Network for Real-time Brain Tumor Segmentation

The paper presents a novel approach for brain tumor segmentation using MRI data through the introduction of a 3D Dilated Multi-Fiber Network (DMFNet). This research addresses the need for real-time, accurate segmentation of brain tumors by innovatively leveraging 3D CNNs while mitigating the significant computational costs typically associated with such networks. The proposed DMFNet achieves an efficient balance between accuracy and computational efficiency in processing large-scale 3D MRI volumes.

Methodological Advancements

The core contribution of this paper is the DMFNet architecture, which employs two distinct innovations:

1. 3D Multi-Fiber Unit: Building on the concept of group convolutions, the authors introduce a lightweight ensemble consisting of multiple small 3D CNNs which they refer to as the Multi-Fiber Unit. This design not only reduces the computational demands but also maintains effective communication between channels through the multiplexer, facilitating a robust information exchange in the network.
2. 3D Dilated Convolution: The network capitalizes on dilated convolutions to capture multi-scale representations of tumor structures in the MRIs. This approach enhances the receptive field without increasing the parameter count excessively, thus offering an efficient solution for detailed volumetric segmentation.

Empirical Evaluation

The authors thoroughly evaluated DMFNet using the BraTS-2018 challenge dataset, achieving notable results with a significantly reduced computational footprint. The network demonstrated dice scores of 80.12%, 90.62%, and 84.54% for enhancing tumor, whole tumor, and tumor core, respectively. These results are in close proximity to the state-of-the-art but achieved with 10 times fewer parameters and 55 times fewer floating-point operations per second (FLOPs) compared to other high-performing networks, highlighting DMFNet's efficiency.

Implications and Future Directions

The implications of this research are substantial for both clinical and computational applications. Clinically, the ability to perform high-accuracy segmentation in real-time on large-scale datasets can enhance diagnostic processes, treatment planning, and patient monitoring. Computationally, the proposed architecture sets a precedent for developing resource-efficient neural networks capable of handling 3D medical data.

Future developments may explore further optimization of the fiber and dilation parameters to refine segmentation quality or adaptability to other volumetric image modalities beyond MRIs. Additionally, integrating the architecture into a broader end-to-end automated diagnostic system could amplify its utility in clinical workflows.

Conclusion

This paper effectively demonstrates a method for improving brain tumor segmentation through a balance of computational efficiency and accuracy. By adopting group convolutions and adaptive dilated convolutions, the DMFNet provides a viable solution for real-time brain tumor segmentation, bridging the gap between high computational overheads and the practical demands of clinical application in medical imaging.