4 DAILY WACV Saturday Oral Presentation Both 3D and purely 2D deep learning-based segmentation methods fall short when confronted with volumetric data of this nature. In general, 2D methods lack the capacity to fully harness the volumetric information effectively, while 3D methods face limitations when confronted with variations in volume resolution. “Sometimes 2D methods are more or less sufficient, and then for more isotropic volumes, 3D methods are sufficient, though there’s not as much need for that,” Alex points out. “But there are problems where only anisotropic images are used. In that scenario, it’s better to use a more effective approach.” The dichotomy between 2D and 3D segmentation methods has led him to explore a different approach to reconcile these disparities and enhance the accuracy of anisotropic image segmentation: 2.5D segmentation. These models focus on learning the relationship across slices. However, existing methods typically have many parameters to train.
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