[1]王琳,曹艳,邓子微,等.基于3D-nnUnet的三维超声胎肺容积自动测量[J].深圳大学学报理工版,2022,39(3):324-333.[doi:10.3724/SP.J.1249.2022.03324]
 WANG Lin,CAO Yan,DENG Ziwei,et al.Automatic measurement of fetal lung volume with 3D ultrasound based on 3D-nnUnet[J].Journal of Shenzhen University Science and Engineering,2022,39(3):324-333.[doi:10.3724/SP.J.1249.2022.03324]
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基于3D-nnUnet的三维超声胎肺容积自动测量()
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《深圳大学学报理工版》[ISSN:1000-2618/CN:44-1401/N]

卷:
第39卷
期数:
2022年第3期
页码:
324-333
栏目:
电子与信息科学
出版日期:
2022-05-16

文章信息/Info

Title:
Automatic measurement of fetal lung volume with 3D ultrasound based on 3D-nnUnet
文章编号:
202203011
作者:
王琳曹艳邓子微胡炯通梁嘉敏曹晓焱潘文雄严玉玲孙志伟杨鑫倪东
1)深圳市福田妇幼保健院,广东深圳 518016;2)深圳大学医学部生物医学工程学院,广东深圳 518073
Author(s):
WANG Lin CAO Yan DENG Ziwei HU Jiongtong LIANG Jiamin CAO Xiaoyan PAN Wenxiong YAN Yuling SUN Zhiwei YANG Xin and NI Dong
1) Futian Women and Children Hospital, Shenzhen 518016, Guangdong Province, P. R. China 2) School of Biomedical Engineering, Shenzhen University, Shenzhen 518073, Guangdong Province, P. R. China
关键词:
人工智能胎肺成熟度三维超声语义分割深度学习网络自适应医学影像
Keywords:
artificial intelligence fetal lung maturity three-dimensional ultrasound semantic segmentation deep learning network adaptation medical image
分类号:
R318;TP751
DOI:
10.3724/SP.J.1249.2022.03324
文献标志码:
A
摘要:
胎肺发育不良常在胎儿出生时引发严重的呼吸窘迫,甚至造成新生儿死亡.胎肺容积测量是临床上无创评估肺成熟度的一项重要手段,但现有的胎肺容积测量方法不仅误差大,繁琐耗时且临床实用性差.本研究基于3D-nnUnet首次提出一种高效稳定的胎肺自动分割和测量方法,利用网络对胎肺数据的自适应,有效克服图像组织对比度低和边缘模糊问题,实现了三维超声胎肺的精确分割.此外,针对胎肺超声图像在不同孕周差异大、样本数分布极不均衡的问题,提出利用HMEP(hard-mining and easy-penalized)损失来提升模型的泛化能力和稳定性.与二维最优分割网络DeepLab V3+和3D-Unet分割结果相比,基于3D-nnUnet的分割网络性能最佳,分割准确率高达85.7%;HMEP Loss能够使3D-nnUnet模型专注地学习少数困难样本,将分割准确率提升近2%;分割模型在不同孕周的数据上所测得胎肺容积和医生手动勾画的胎肺容积经一致性检验无显著的统计学差异.实验结果表明:该方法可高效实现三维超声胎肺的自动精确分割和容积测量,具有良好的稳定性和泛化能力,可避免以往胎肺容积测量方法繁琐耗时、误差较大的问题,在诊断胎肺发育状况及评估肺成熟度方面有较好的应用前景.
Abstract:
Fetal lung dysplasia often causes severe respiratory distress and even death in the newborn. Fetal lung volume measurement is an important method for clinical non-invasive assessment of lung maturity. However, the existing methods for measuring fetal lung volume suffer from huge errors, tedious and time-consuming operations, and poor clinical practicability. For this reason, we first propose an efficient and stable automatic segmentation and measurement method for fetal lung based on 3D-nnUnet, which uses network adaptation to effectively overcome the problems of low tissue contrast and blurred edges and achieves accurate segmentation for 3-dimension (3D) fetal lung ultrasound. In addition, to solve the problem of large difference and the extremely uneven distribution of sample numbers at different gestational weeks, we innovatively propose HMEP (hard-mining and easy-penalized ) loss to improve the generalization ability of the model. Compared with the segmentation results of 2-dimension optimal DeepLab V3+ and 3D-Unet, our network has the best segmentation performance, and the accuracy is as high as 85.7%. In addition, HMEP Loss can increase the learning of the smaller samples, which increases the accuracy by nearly 2%. There is no significant statistical difference between the fetal lung volumes measured by our network and gold standard using the consistency test in different gestational weeks. The experimental results show that our method can efficiently realize the automatic and accurate segmentation and volume measurement for 3D fetal lung ultrasound, and has good stability and generalization ability, which can avoid the cumbersome, time-consuming operations and large errors of the previous fetal lung volume measurement methods. Our method has promising application prospects in diagnosing fetal lung dysplasia and assessing fetal lung maturity.

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更新日期/Last Update: 2022-05-30