MedNeXt: 3D Medical Segmentation

• MedNeXt is a transformer-inspired, fully convolutional 3D segmentation architecture that adapts ConvNeXt design for volumetric medical tasks with compound scaling and large kernel support.
• It incorporates innovative techniques like the UpKern algorithm, residual inverted bottlenecks, and deep supervision to stabilize training and preserve semantic details.
• MedNeXt demonstrates state-of-the-art performance across CT and MRI benchmarks, effectively segmenting challenges such as brain tumors, abdominal organs, and breast lesions.

MedNeXt is a transformer-inspired, fully convolutional 3D medical image segmentation architecture that modernizes the ConvNeXt design for volumetric tasks. Distinguished by its compound scaling capabilities, large kernel support, and robust inductive bias, MedNeXt achieves state-of-the-art performance on segmentation benchmarks spanning both CT and MRI modalities, including challenging domains such as brain tumors, abdominal organs, perivascular spaces, and breast lesions. MedNeXt’s contributions include a fully ConvNeXt 3D encoder-decoder backbone, residual inverted bottleneck up/downsampling for semantic preservation across spatial scales, the UpKern algorithm for stable large kernel initialization, and a scaling scheme allowing simultaneous optimization along depth, width, and receptive field dimensions.

1. Architectural Innovations and Compound Scaling

MedNeXt extends ConvNeXt blocks to a fully 3D encoder-decoder architecture, eschewing conventional plain convolutions in both downsampling and upsampling modules. Core architectural elements include:

• MedNeXt Block: Each comprises a $k \times k \times k$ depthwise convolution (with kernel sizes $k \in \{3, 5, 7, 9\}$), channel-wise GroupNorm, a 1×1×1 expansion convolution with GELU activation, and a 1×1×1 compression to restore original channel dimensionality. Residual connections link input and output.
• Residual Inverted Bottlenecks: Strided or transposed convolutions are integrated in the first depthwise layer, while channel sizing operations are handled in the final compression layer.
• UpKern Algorithm: Large kernels are initialized by trilinearly upsampling the weights of smaller-kernel networks ($K_2 = \text{Interp}(K_1;\,k_2)$), avoiding performance saturation typical in limited-data regimes and enabling stable large-kernel training.
• Compound Scaling: Practitioners tune network parameters along three axes: depth (block count), width (channel expansion ratio $R$), and kernel size ($k$). This facilitates scaling for tasks of variable data availability and anatomical complexity, yielding robust performance from small organ-specific challenges to large tumor segmentation studies (Roy et al., 2023).

2. Training Paradigms, Losses, and Deep Supervision

MedNeXt typically leverages region-based losses and deep supervision:

• Dice and Focal Losses: For segmentation tasks, the Dice Similarity Coefficient (DSC) is calculated as:

$$\text{DSC} = \frac{2 \sum_i p_i g_i}{\sum_i p_i + \sum_i g_i}$$

with focal loss added to penalize misclassified voxels and address class imbalance:

$$L_{\text{Focal}} = -\sum_i (1-p_i)^\gamma \log(p_i)\quad \text{with}\;\gamma=2.0$$

The composite loss is $L_{\text{Total}}=L_{\text{Dice}}+L_{\text{Focal}}$.

• Deep Supervision (DS): Losses are computed at multiple decoder resolutions, each weighted (often halved per level) to improve gradient flow and segmentation boundary precision (Maani et al., 14 Mar 2024).

3. Postprocessing, Model Ensembling, and Application-specific Adaptations

MedNeXt pipelines routinely integrate aggressive postprocessing steps, thresholding, and connected component analysis to improve sensitivity and suppress false positives:

• Thresholding and Filtering: Each output channel is thresholded, and connected component analysis is performed with size/probability criteria (Maani et al., 14 Mar 2024, Hashmi et al., 24 Nov 2024).
• Sliding Window and Test-Time Augmentation: Inference on large volumes is managed via sliding windows (e.g., $128^3$), with TTA via flips ($2^3=8$) and probability averaging to achieve robust predictions (Maani et al., 5 May 2024, Munk et al., 1 Aug 2024).
• Ensembling: Final probability maps often reflect ensemble averages over $N$ cross-validation models:

$P = \frac{1}{N}\sum_{i=1}^N P_i$

• Domain Adaptations: For regions with heavy domain shift, MedNeXt can be fine-tuned using transfer learning, stratified folds, and parameter-efficient adapters. Weighted averages between MedNeXt and other architectures (e.g., nnU-Net) are used to transfer robustness in small dataset scenarios (Parida et al., 5 Dec 2024, Musah et al., 29 Jul 2025).

4. Performance Benchmarks and Modalities

MedNeXt has demonstrated leading performance across standard medical segmentation datasets:

• Brain Tumor Segmentation: Achieves lesion-wise Dice scores in excess of 0.84 on sub-Saharan Africa and pediatric datasets (Hashmi et al., 24 Nov 2024, Musah et al., 29 Jul 2025, Ankomah et al., 3 Oct 2025), up to 0.87–0.90 in validation for adult glioma and head/neck (Moradi et al., 22 Nov 2024).
• Abdominal Organ Segmentation: Outperforms nnUNet and transformer-based baselines on BTCV and AMOS22 datasets (Roy et al., 2023).
• Breast Tumor and Perivascular Space Segmentation: Delivers Dice scores of 0.67 (breast, NormHD 0.24) (Musah, 3 Aug 2025), 0.88 (white matter PVS, T2w), and generalizes across multi-site and modality data (Low et al., 27 Aug 2025).
• Stroke Lesion Segmentation: Competitive with lighter models, but MeshNet achieves similar DICE with ~1/1000th the parameters (Fedorov et al., 7 Mar 2025).

Challenges include instability in training with larger kernels in low-resource domains and the need for careful selection of ensemble strategies to avoid performance degradation.

5. Specialized Extensions and Variants

MedNeXt’s modular design has enabled methodical extensions:

• EMedNeXt (MedNeXt V2): Incorporates deep supervision, expanded region of interest, and robust model ensembling. In sub-Saharan Africa datasets, EMedNeXt reports lesion-wise DSC near 0.90 and Normalized Surface Dice 0.84 (@1.0 mm) (Jaheen et al., 31 Jul 2025).
• Adapter-based PEFT: Enables parameter-efficient fine-tuning for transfer across domain-shifted datasets, highlighting adaptive approaches to segmentation in resource-constrained environments (Adhikari et al., 18 Dec 2024).
• Integration into Hybrid Frameworks: MedNeXt serves as the decoder in Swin-NeXt (nnY-Net), supporting cross-attended fusion with Swin Transformer encoders and multi-modal data streams (Liu et al., 2 Jan 2025).

6. Comparative Analyses and Limitations

While MedNeXt offers robust performance and adaptability through compound scaling and inductive biases, comparative studies reveal context-sensitive tradeoffs:

• Efficiency Considerations: Lighter architectures such as MeshNet may achieve similar accuracy with drastically lower parameter counts, making them preferable for edge deployment (Fedorov et al., 7 Mar 2025).
• Generalization Across Modalities: Large kernels and transformer-inspired blocks confer robust global context in high-contrast T2w imaging but do not universally outperform simpler CNNs such as nnU-Net on low-contrast T1w or highly heterogeneous datasets (Low et al., 27 Aug 2025).
• Training Stability: Larger models and kernels may suffer instability unless initialized via approaches such as UpKern. Multi-channel models are also susceptible to collapse if label distributions are imbalanced (Moradi et al., 22 Nov 2024).
• Postprocessing Dependency: Segmentation accuracy depends on rigorous postprocessing, e.g., connected component analysis and label hierarchy enforcement, especially for small lesions and challenging populations (Ankomah et al., 3 Oct 2025, Jaheen et al., 31 Jul 2025).

7. Future Directions

Potential advancements for MedNeXt-based pipelines include:

• Extension of compound scaling and kernel upsampling to more architectural components for broader receptive field optimization.
• Investigation of transfer learning and semi-supervised methods to enable adaptation with minimal annotated data.
• Integration with richer attention mechanisms from transformer literature to further boost segmentation, especially in complex anatomical regions.
• Improved calibration and fairness assessment, particularly where radiomic and clinical variables interact with segmentation-driven downstream tasks (Musah, 3 Aug 2025).
• Formal benchmarking against human expert annotations across larger, multi-ethnic, and multi-modal datasets.

MedNeXt constitutes a scalable, transformer-informed convolutional backbone for medical image segmentation, delivering precision across modalities and clinical domains via principled architectural design, loss functions, and targeted data augmentation. Its demonstrated ability to adapt to diverse datasets, integrate into hybrid frameworks, and serve as the foundation for parameter-efficient fine-tuning underscores its continuing relevance to the advancement of automated medical image analysis.