Comparing 3D, 2.5D, and 2D Approaches to Brain Image Auto-Segmentation.

ABSTRACT: Deep-learning methods for auto-segmenting brain images either segment one slice of the image (2D), five consecutive slices of the image (2.5D), or an entire volume of the image (3D). Whether one approach is superior for auto-segmenting brain images is not known. We compared these three approaches (3D, 2.5D, and 2D) across three auto-segmentation models (capsule networks, UNets, and nnUNets) to segment brain structures. We used 3430 brain MRIs, acquired in a multi-institutional study, to train and test our models. We used the following performance metrics: segmentation accuracy, performance with limited training data, required computational memory, and computational speed during training and deployment. The 3D, 2.5D, and 2D approaches respectively gave the highest to lowest Dice scores acro

SUBMITTER: Avesta A 

PROVIDER: S-EPMC9952534 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature