The proposed CAMS-Net architecture, which is currently designed for 2D medical image segmentation, can be extended to 3D medical image segmentation tasks by adapting its core components to handle volumetric data. This can be achieved through several modifications:
3D Patch Extraction: Instead of extracting 2D patches from the input images, the architecture can be modified to extract 3D patches (e.g., 2x2x2 or 3x3x3) from volumetric data. This would allow the model to capture spatial relationships in three dimensions, which is crucial for accurately segmenting anatomical structures in 3D medical images.
3D Mamba Blocks: The existing Mamba blocks can be extended to 3D by incorporating 3D convolutions or 3D state space models that maintain the linear complexity while capturing long-range dependencies in three dimensions. This would involve modifying the Linearly Interconnected Factorized Mamba (LIFM) block to accommodate 3D operations.
3D Aggregators: The Mamba Channel Aggregator (MCA) and Mamba Spatial Aggregator (MSA) can be adapted to learn features across 3D channels and spatial locations. This would involve reshaping the input tensors to account for the additional depth dimension and applying the aggregation operations accordingly.
Bidirectional Scanning in 3D: The bidirectional scanning strategy can be extended to three dimensions, allowing the model to learn spatial dependencies in all three axes. This would enhance the model's ability to capture contextual information across the entire volume.
Training and Evaluation: The training process would need to be adjusted to handle 3D data, including the use of appropriate data augmentation techniques for volumetric images. Evaluation metrics specific to 3D segmentation, such as the Dice score and Hausdorff distance, should be employed to assess performance.
By implementing these modifications, CAMS-Net can effectively tackle 3D medical image segmentation tasks, leveraging its convolution and attention-free design to achieve efficient and accurate results.