Deep Learning Based Automatic Left Ventricle Segmentation from the Transgastric Short-Axis View on Transesophageal Echocardiography: A Feasibility Study

被引:0
作者
Tian, Yuan [1 ]
Qin, Wenting [2 ]
Zhao, Zihang [2 ]
Wang, Chunrong [1 ]
Tian, Yajie [1 ]
Zhang, Yuelun [1 ]
He, Kai [1 ]
Zhang, Yuguan [1 ]
Shen, Le [1 ]
Zhou, Zhuhuang [2 ]
Yu, Chunhua [1 ]
机构
[1] Chinese Acad Med Sci & Peking Union Med Coll, Peking Union Med Coll Hosp, Dept Anesthesiol, Beijing 100730, Peoples R China
[2] Beijing Univ Technol, Coll Chem & Life Sci, Dept Biomed Engn, Beijing 100124, Peoples R China
关键词
transesophageal echocardiography; deep learning; left ventricle segmentation; transgastric short-axis view; convolutional neural network; ULTRASOUND; SURGERY;
D O I
10.3390/diagnostics14151655
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Segmenting the left ventricle from the transgastric short-axis views (TSVs) on transesophageal echocardiography (TEE) is the cornerstone for cardiovascular assessment during perioperative management. Even for seasoned professionals, the procedure remains time-consuming and experience-dependent. The current study aims to evaluate the feasibility of deep learning for automatic segmentation by assessing the validity of different U-Net algorithms. A large dataset containing 1388 TSV acquisitions was retrospectively collected from 451 patients (32% women, average age 53.42 years) who underwent perioperative TEE between July 2015 and October 2023. With image preprocessing and data augmentation, 3336 images were included in the training set, 138 images in the validation set, and 138 images in the test set. Four deep neural networks (U-Net, Attention U-Net, UNet++, and UNeXt) were employed for left ventricle segmentation and compared in terms of the Jaccard similarity coefficient (JSC) and Dice similarity coefficient (DSC) on the test set, as well as the number of network parameters, training time, and inference time. The Attention U-Net and U-Net++ models performed better in terms of JSC (the highest average JSC: 86.02%) and DSC (the highest average DSC: 92.00%), the UNeXt model had the smallest network parameters (1.47 million), and the U-Net model had the least training time (6428.65 s) and inference time for a single image (101.75 ms). The Attention U-Net model outperformed the other three models in challenging cases, including the impaired boundary of left ventricle and the artifact of the papillary muscle. This pioneering exploration demonstrated the feasibility of deep learning for the segmentation of the left ventricle from TSV on TEE, which will facilitate an accelerated and objective alternative of cardiovascular assessment for perioperative management.
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页数:15
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  • [21] MAEF-Net: Multi-attention efficient feature fusion network for left ventricular segmentation and quantitative analysis in two-dimensional echocardiography
    Zeng, Yan
    Tsui, Po-Hsiang
    Pang, Kunjing
    Bin, Guangyu
    Li, Jiehui
    Lv, Ke
    Wu, Xining
    Wu, Shuicai
    Zhou, Zhuhuang
    [J]. ULTRASONICS, 2023, 127
  • [22] UNet plus plus : Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation
    Zhou, Zongwei
    Siddiquee, Md Mahfuzur Rahman
    Tajbakhsh, Nima
    Liang, Jianming
    [J]. IEEE TRANSACTIONS ON MEDICAL IMAGING, 2020, 39 (06) : 1856 - 1867
  • [23] UNet plus plus : A Nested U-Net Architecture for Medical Image Segmentation
    Zhou, Zongwei
    Siddiquee, Md Mahfuzur Rahman
    Tajbakhsh, Nima
    Liang, Jianming
    [J]. DEEP LEARNING IN MEDICAL IMAGE ANALYSIS AND MULTIMODAL LEARNING FOR CLINICAL DECISION SUPPORT, DLMIA 2018, 2018, 11045 : 3 - 11
  • [24] Ultrasound-enhanced Unet model for quantitative photoacoustic tomography of ovarian lesions
    Zou, Yun
    Amidi, Eghbal
    Luo, Hongbo
    Zhu, Quing
    [J]. PHOTOACOUSTICS, 2022, 28