Comparing 3D, 2.5D, and 2D Approaches to Brain MRI Segmentation
Arman Avesta, Sajid Hossain, MingDe Lin, Mariam Aboian, Harlan M. Krumholz, Sanjay Aneja · Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition · 2024
We compared 3D, 2.5D, and 2D approaches to brain MRI auto-segmentation and concluded that the 3D approach is more accurate, achieves better performance when training data is limited, and is faster to train and deploy. Our results hold across various deep-learning architectures, including capsule networks, UNets, and nnUNets. The only downside of 3D approach is that it requires 20 times more computational memory compared to 2.5D or 2D approaches. Because 3D capsule networks only need twice the computational memory that 2.5D or 2D UNets and nnUNets need, we suggest using 3D capsule networks in settings where computational memory is limited.