HPS-Net: Multi-Task Network for Medical Image Segmentation with Predictable Performance

被引:1
|
作者
Wei, Xin [1 ]
Wan, Huan [2 ]
Ye, Fanghua [1 ]
Min, Weidong [1 ]
机构
[1] Nanchang Univ, Sch Software, 235 East Nanjing Rd, Nanchang 330047, Jiangxi, Peoples R China
[2] Jiangxi Normal Univ, Sch Comp Informat Engn, 99 Ziyang Ave, Nanchang 330022, Jiangxi, Peoples R China
来源
SYMMETRY-BASEL | 2021年 / 13卷 / 11期
基金
中国国家自然科学基金;
关键词
symmetrical structure; medical image; image segmentation; deep learning; CNNs; loss function; TRIANGLE INEQUALITY;
D O I
10.3390/sym13112107
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
In recent years, medical image segmentation (MIS) has made a huge breakthrough due to the success of deep learning. However, the existing MIS algorithms still suffer from two types of uncertainties: (1) the uncertainty of the plausible segmentation hypotheses and (2) the uncertainty of segmentation performance. These two types of uncertainties affect the effectiveness of the MIS algorithm and then affect the reliability of medical diagnosis. Many studies have been done on the former but ignore the latter. Therefore, we proposed the hierarchical predictable segmentation network (HPS-Net), which consists of a new network structure, a new loss function, and a cooperative training mode. According to our knowledge, HPS-Net is the first network in the MIS area that can generate both the diverse segmentation hypotheses to avoid the uncertainty of the plausible segmentation hypotheses and the measure predictions about these hypotheses to avoid the uncertainty of segmentation performance. Extensive experiments were conducted on the LIDC-IDRI dataset and the ISIC2018 dataset. The results show that HPS-Net has the highest Dice score compared with the benchmark methods, which means it has the best segmentation performance. The results also confirmed that the proposed HPS-Net can effectively predict TNR and TPR.
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页数:12
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