MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation (2303.09975v5)

Abstract: There has been exploding interest in embracing Transformer-based architectures for medical image segmentation. However, the lack of large-scale annotated medical datasets make achieving performances equivalent to those in natural images challenging. Convolutional networks, in contrast, have higher inductive biases and consequently, are easily trainable to high performance. Recently, the ConvNeXt architecture attempted to modernize the standard ConvNet by mirroring Transformer blocks. In this work, we improve upon this to design a modernized and scalable convolutional architecture customized to challenges of data-scarce medical settings. We introduce MedNeXt, a Transformer-inspired large kernel segmentation network which introduces - 1) A fully ConvNeXt 3D Encoder-Decoder Network for medical image segmentation, 2) Residual ConvNeXt up and downsampling blocks to preserve semantic richness across scales, 3) A novel technique to iteratively increase kernel sizes by upsampling small kernel networks, to prevent performance saturation on limited medical data, 4) Compound scaling at multiple levels (depth, width, kernel size) of MedNeXt. This leads to state-of-the-art performance on 4 tasks on CT and MRI modalities and varying dataset sizes, representing a modernized deep architecture for medical image segmentation. Our code is made publicly available at: https://github.com/MIC-DKFZ/MedNeXt.

• The paper introduces MedNeXt, a CNN architecture that integrates Transformer-inspired components to enhance medical image segmentation performance.
• It employs compound scaling and novel UpKern initialization to optimize 3D segmentation across diverse CT and MRI datasets.
• Experimental results show improved segmentation accuracy over nnUNet and other state-of-the-art models, offering robust performance even with limited data.

MedNeXt: Transformer-Driven Scaling of ConvNets for Medical Image Segmentation

The research paper, "MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation," presents an innovative convolutional network architecture tailored for medical image segmentation, inspired by Transformer models. This work addresses limitations in current approaches due to data scarcity in medical imaging compared to natural image tasks.

Core Contributions

The paper introduces MedNeXt, a scalable Convolutional Neural Network (CNN) architecture designed to leverage the structural strengths of Transformers while retaining the inherent inductive biases of convolutional networks. The key innovations and components within MedNeXt include:

1. Fully ConvNeXt 3D Network: The architecture adapts ConvNeXt blocks across an entire 3D U-Net framework, distinguishing itself by replacing typical procedural elements with sophisticated Transformer-like inversions using depthwise convolutions and wide expansion channels.
2. Residual Inverted Bottlenecks: These blocks are utilized for upsampling and downsampling processes within the network, enhancing semantic richness and improving gradient flow to optimize segmentation tasks.
3. UpKern Initialization Technique: To counteract performance saturation associated with large kernels in limited data scenarios, a novel training initialization approach is proposed. This involves upsampling kernel weights from smaller, pre-trained networks.
4. Compound Scaling: By independently scaling depth, width, and kernel size, MedNeXt permits targeted adaptation for varying task demands, demonstrating its flexibility across different levels of complexity and detail in medical image datasets.

Experimental Evaluation

The paper rigorously evaluates MedNeXt across four diverse medical imaging datasets: BTCV, AMOS22, KiTS19, and BraTS21, covering both CT and MRI modalities. Numerical results indicate that MedNeXt achieves superior or comparable performance to established approaches like nnUNet and various Transformer-based models. Through systematic evaluations:

• Effectiveness of MedNeXt Components: Ablations confirm the efficacy of integrating Transformer-inspired architectural elements, notably the residual pathways and UpKern initialization, which significantly enhance volumetric and surface segmentation accuracies.
• Performance Across Modalities: MedNeXt proves adept at handling both organ and tumor segmentation with commendable accuracy, highlighting its robustness across modality (CT, MRI) and annotation sparsity challenges.

Implications and Future Work

MedNeXt provides a compelling blueprint for future architectures in medical image analysis. Its successful integration of Transformer-inspired components with convolutional designs suggests potential applications in other domains of sparse data contexts beyond medical imaging. The introduction of compound scalable architecture presents an avenue for further exploration into optimal scaling strategies for CNNs in various domains.

Conclusion

In summary, MedNeXt stands as a strong contender in the domain of medical image segmentation, illustrating that Transformer-like scalability can indeed bring substantive benefits to CNN architectures when appropriately tailored for domain-specific challenges. Its advancements open pathways for subsequent research into convolutional architectures that balance complexity, performance, and training stability in resource-constrained environments.