IPMI 2021 poster

Deep Label Fusion: A 3D End-to-End Hybrid Multi-Atlas Segmentation and Deep Learning Pipeline

Authors: Long Xie (University of Pennsylvania); Laura Wisse (Lund University); Jiancong Wang (University of Pennsylvania); Sadhana Ravikumar (University of Pennsylvania); Trevor Glenn (University of Pennsylvania); Anica Luther (Lund University); Sydney Lim (University of Pennsylvania); David Wolk (University of Pennsylvania); Paul Yushkevich (UPENN)


Abstract: Deep learning (DL) is the state-of-the-art methodology in various medical image segmentation tasks. However, it requires relatively large amounts of manually labeled training data, which may be infeasible to generate in some applications. In addition, DL methods have relatively poor generalizability to out-of-sample data. Multi-atlas segmentation (MAS), on the other hand, has promising performance using limited amounts of training data and good generalizability. A hybrid method that integrates the high accuracy of DL and good generalizability of MAS is highly desired and could play an important role in segmentation problems where manually labeled data is hard to generate. Most of the prior work focuses on improving single components of MAS using DL rather than directly optimizing the final segmentation accuracy via an end-to-end pipeline. Only one study explored this idea in binary segmentation of 2D images, but it remains unknown whether it generalizes well to multi-class 3D segmentation problems. In this study, we propose a 3D end-to-end hybrid pipeline, named deep label fusion (DLF), that takes advantage of the strengths of MAS and DL. Experimental results demonstrate that DLF yields significant improvements over conventional label fusion methods as well as U-Net, a direct DL approach, in the context of segmenting medial temporal lobe subregions using 3T T1-weighted and T2-weighted structural MRI. Further, when applied to an unseen similar dataset that is acquired in 7T, DLF maintains its superior performance, which demonstrates its good generalizability.