Deep Label Fusion: A 3D End-To-End Hybrid Multi-atlas Segmentation and Deep Learning Pipeline

Long Xie; Laura E.M. Wisse; Jiancong Wang; Sadhana Ravikumar; Trevor Glenn; Anica Luther; Sydney Lim; David A. Wolk; Paul A. Yushkevich

doi:10.1007/978-3-030-78191-0_33

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Deep Label Fusion: A 3D End-To-End Hybrid Multi-atlas Segmentation and Deep Learning Pipeline

Long Xie Laura E.M. Wisse Jiancong Wang Sadhana Ravikumar Trevor Glenn Anica Luther Sydney Lim David A. Wolk Paul A. Yushkevich

Year: 2021 Lecture notes in computer science Pages: 428-439 Publisher: Springer Science+Business Media

DOI: 10.1007/978-3-030-78191-0_33

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Keywords:

Generalizability theory Segmentation Artificial intelligence Pipeline (software) Computer science Deep learning Market segmentation Pattern recognition (psychology) Context (archaeology) Image segmentation Mathematics Geography

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Topics

Advanced Neural Network Applications

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Medical Image Segmentation Techniques

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Domain Adaptation and Few-Shot Learning

Physical Sciences → Computer Science → Artificial Intelligence

Deep Label Fusion: A 3D End-To-End Hybrid Multi-atlas Segmentation and Deep Learning Pipeline

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