MultiResUNet : Rethinking the U-Net architecture for multimodal biomedical image segmentation

被引:1268
作者
Ibtehaz, Nabil [1 ]
Rahman, M. Sohel [2 ]
机构
[1] Samsung R&D Inst, Dhaka, Bangladesh
[2] BUET, Dept CSE, ECE Bldg, Dhaka 1205, Bangladesh
关键词
Convolutional neural networks; Medical imaging; Semantic segmentation; U-Net; VALIDATION; GLAND;
D O I
10.1016/j.neunet.2019.08.025
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In recent years Deep Learning has brought about a breakthrough in Medical Image Segmentation. In this regard, U-Net has been the most popular architecture in the medical imaging community. Despite outstanding overall performance in segmenting multimodal medical images, through extensive experimentations on some challenging datasets, we demonstrate that the classical U-Net architecture seems to be lacking in certain aspects. Therefore, we propose some modifications to improve upon the already state-of-the-art U-Net model. Following these modifications, we develop a novel architecture, MultiResUNet, as the potential successor to the U-Net architecture. We have tested and compared MultiResUNet with the classical U-Net on a vast repertoire of multimodal medical images. Although only slight improvements in the cases of ideal images are noticed, remarkable gains in performance have been attained for the challenging ones. We have evaluated our model on five different datasets, each with their own unique challenges, and have obtained a relative improvement in performance of 10.15%, 5.07%, 2.63%, 1.41%, and 0.62% respectively. We have also discussed and highlighted some qualitatively superior aspects of MultiResUNet over classical U-Net that are not really reflected in the quantitative measures. (C) 2019 Elsevier Ltd. All rights reserved.
引用
收藏
页码:74 / 87
页数:14
相关论文
共 56 条
[1]  
Abadi M, 2016, PROCEEDINGS OF OSDI'16: 12TH USENIX SYMPOSIUM ON OPERATING SYSTEMS DESIGN AND IMPLEMENTATION, P265
[2]  
[Anonymous], ARXIV180310417
[3]  
[Anonymous], 2007, USENIX ANN TECH C
[4]  
[Anonymous], PROC CVPR IEEE
[5]  
[Anonymous], ARXIV151100561
[6]  
[Anonymous], 2017, COMMUN ACM, DOI DOI 10.1145/3065386
[7]  
[Anonymous], 2017, P 31 AAAI C ART INT
[8]  
[Anonymous], ARXIV160608921
[9]  
[Anonymous], 2015, PROC CVPR IEEE
[10]  
[Anonymous], MED PHYS