消息
×
loading..
MetricUNet: Synergistic image- and voxel-level learning for precise prostate segmentation via online sampling
2021-07
发表期刊MEDICAL IMAGE ANALYSIS (IF:10.7[JCR-2023],11.9[5-Year])
ISSN1361-8415
EISSN1361-8423
卷号71
发表状态已发表
DOI10.1016/j.media.2021.102039
摘要

Fully convolutional networks (FCNs), including UNet and VNet, are widely-used network architectures for semantic segmentation in recent studies. However, conventional FCN is typically trained by the cross entropy or Dice loss, which only calculates the error between predictions and ground-truth labels for pixels individually. This often results in non-smooth neighborhoods in the predicted segmentation. This problem becomes more serious in CT prostate segmentation as CT images are usually of low tissue contrast. To address this problem, we propose a two-stage framework, with the first stage to quickly localize the prostate region, and the second stage to precisely segment the prostate by a multi-task UNet architecture. We introduce a novel online metric learning module through voxel-wise sampling in the multitask network. Therefore, the proposed network has a dual-branch architecture that tackles two tasks: (1) a segmentation sub-network aiming to generate the prostate segmentation, and (2) a voxel-metric learning sub-network aiming to improve the quality of the learned feature space supervised by a metric loss. Specifically, the voxel-metric learning sub-network samples tuples (including triplets and pairs) in voxel-level through the intermediate feature maps. Unlike conventional deep metric learning methods that generate triplets or pairs in image-level before the training phase, our proposed voxel-wise tuples are sampled in an online manner and operated in an end-to-end fashion via multi-task learning. To evaluate the proposed method, we implement extensive experiments on a real CT image dataset consisting 339 patients. The ablation studies show that our method can effectively learn more representative voxellevel features compared with the conventional learning methods with cross-entropy or Dice loss. And the comparisons show that the proposed method outperforms the state-of-the-art methods by a reasonable margin. (c) 2021 Elsevier B.V. All rights reserved.

关键词Prostate cancer Sampling Metric learning Fully convolutional networks Triplet Contrast learning
收录类别SCIE ; EI
语种英语
WOS研究方向Computer Science ; Engineering ; Radiology, Nuclear Medicine & Medical Imaging
WOS类目Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Radiology, Nuclear Medicine & Medical Imaging
WOS记录号WOS:000663615600013
出版者ELSEVIER
原始文献类型Article
引用统计
正在获取...
文献类型期刊论文
条目标识符https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/127598
专题生物医学工程学院_PI研究组_沈定刚组
通讯作者Zhang, Junfeng; Shen, Dinggang
作者单位
1.Nanjing Univ, Med Sch, Nanjing, Peoples R China;
2.Nanjing Univ, Natl Inst Healthcare Data Sci, Nanjing, Peoples R China;
3.Xi An Jiao Tong Univ, Sch Math & Stat, Xian, Shanxi, Peoples R China;
4.Stanford Univ, Dept Psychiat & Behav Sci, Stanford, CA 94305 USA;
5.Stanford Univ, Dept Comp Sci, Stanford, CA 94305 USA;
6.Nanjing Univ, State Key Lab Novel Software Technol, Nanjing, Peoples R China;
7.Nanjing Univ, Sch Med, Nanjing Drum Tower Hosp, Dept Radiol, Nanjing, Peoples R China;
8.ShanghaiTech Univ, Sch Biomed Engn, Shanghai, Peoples R China;
9.Shanghai United Imaging Intelligence Co Ltd, Dept Res & Dev, Shanghai, Peoples R China;
10.Korea Univ, Dept Artificial Intelligence, Seoul 02841, South Korea
通讯作者单位生物医学工程学院
推荐引用方式
GB/T 7714
He, Kelei,Lian, Chunfeng,Adeli, Ehsan,et al. MetricUNet: Synergistic image- and voxel-level learning for precise prostate segmentation via online sampling[J]. MEDICAL IMAGE ANALYSIS,2021,71.
APA He, Kelei.,Lian, Chunfeng.,Adeli, Ehsan.,Huo, Jing.,Gao, Yang.,...&Shen, Dinggang.(2021).MetricUNet: Synergistic image- and voxel-level learning for precise prostate segmentation via online sampling.MEDICAL IMAGE ANALYSIS,71.
MLA He, Kelei,et al."MetricUNet: Synergistic image- and voxel-level learning for precise prostate segmentation via online sampling".MEDICAL IMAGE ANALYSIS 71(2021).
条目包含的文件
文件名称/大小 文献类型 版本类型 开放类型 使用许可
个性服务
查看访问统计
谷歌学术
谷歌学术中相似的文章
[He, Kelei]的文章
[Lian, Chunfeng]的文章
[Adeli, Ehsan]的文章
百度学术
百度学术中相似的文章
[He, Kelei]的文章
[Lian, Chunfeng]的文章
[Adeli, Ehsan]的文章
必应学术
必应学术中相似的文章
[He, Kelei]的文章
[Lian, Chunfeng]的文章
[Adeli, Ehsan]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。