Hi everyone,
I’ve been thinking about the trade-offs when processing volumetric medical data like MRI sequences.
When we use a 2D segmentation model and slice each MRI independently, we inevitably lose critical spatial context along the Z-axis between slices. On the flip side, moving to full 3D models introduces massive computational overhead, memory constraints, and requires significantly larger datasets to generalize well without overfitting.
To compensate when data is limited, data augmentation becomes vital. While geometric transforms like Rotation work wonderfully because they maintain anatomical realism, other common techniques (like random pixel shuffling or slicing order permutations) completely destroy the structural integrity of the medical image.
For those who have deployed segmentation models in production: Do you typically lean toward 2D models with multi-slice context inputs (like 2.5D approaches), or do you find that going full 3D is always worth the computational cost? What are your go-to augmentation strategies for maintaining anatomical validity?
Would love to hear your experiences