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

by   Long Xie, et al.

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 and U-Net, a direct DL approach, in the context of segmenting medial temporal lobe subregions using 3T T1-weighted and T2-weighted MRI. Further, when applied to an unseen similar dataset acquired in 7T, DLF maintains its superior performance, which demonstrates its good generalizability.


page 3

page 8

page 9

page 10


VoteNet: A Deep Learning Label Fusion Method for Multi-Atlas Segmentation

Deep learning (DL) approaches are state-of-the-art for many medical imag...

Automated Human Claustrum Segmentation using Deep Learning Technologies

In recent years, Deep Learning (DL) has shown promising results in condu...

A Prior Knowledge Based Tumor and Tumoral Subregion Segmentation Tool for Pediatric Brain Tumors

In the past few years, deep learning (DL) models have drawn great attent...

Leveraging Self-supervised Denoising for Image Segmentation

Deep learning (DL) has arguably emerged as the method of choice for the ...

Few-shot brain segmentation from weakly labeled data with deep heteroscedastic multi-task networks

In applications of supervised learning applied to medical image segmenta...

AssemblyNet: A Novel Deep Decision-Making Process for Whole Brain MRI Segmentation

Whole brain segmentation using deep learning (DL) is a very challenging ...

Please sign up or login with your details

Forgot password? Click here to reset