Deep label fusion: A generalizable hybrid multi-atlas and deep convolutional neural network for medical image segmentation.

ABSTRACT: Deep convolutional neural networks (DCNN) achieve very high accuracy in segmenting various anatomical structures in medical images but often suffer from relatively poor generalizability. Multi-atlas segmentation (MAS), while less accurate than DCNN in many applications, tends to generalize well to unseen datasets with different characteristics from the training dataset. Several groups have attempted to integrate the power of DCNN to learn complex data representations and the robustness of MAS to changes in image characteristics. However, these studies primarily focused on replacing individual components of MAS with DCNN models and reported marginal improvements in accuracy. In this study we describe and evaluate a 3D end-to-end hybrid MAS and DCNN segmentation pipeline, called Deep Label F

SUBMITTER: Xie L 

PROVIDER: S-EPMC10009820 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature