Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/168433
Title: Segmentation of human aorta using 3D nnU-net-oriented deep learning
Authors: Li, Feng
Sun, Lianzhong
Lam, Kwok-Yan
Zhang, Songbo
Sun, Zhongming
Peng, Bao
Xu, Hongzeng
Zhang, Libo
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Source: Li, F., Sun, L., Lam, K., Zhang, S., Sun, Z., Peng, B., Xu, H. & Zhang, L. (2022). Segmentation of human aorta using 3D nnU-net-oriented deep learning. Review of Scientific Instruments, 93(11), 114103-. https://dx.doi.org/10.1063/5.0084433
Journal: Review of Scientific Instruments 
Abstract: Computed tomography angiography (CTA) has become the main imaging technique for cardiovascular diseases. Before performing the transcatheter aortic valve intervention operation, segmenting images of the aortic sinus and nearby cardiovascular tissue from enhanced images of the human heart is essential for auxiliary diagnosis and guiding doctors to make treatment plans. This paper proposes a nnU-Net (no-new-Net) framework based on deep learning (DL) methods to segment the aorta and the heart tissue near the aortic valve in cardiac CTA images, and verifies its accuracy and effectiveness. A total of 130 sets of cardiac CTA image data (88 training sets, 22 validation sets, and 20 test sets) of different subjects have been used for the study. The advantage of the nnU-Net model is that it can automatically perform preprocessing and data augmentation according to the input image data, can dynamically adjust the network structure and parameter configuration, and has a high model generalization ability. Experimental results show that the DL method based on nnU-Net can accurately and effectively complete the segmentation task of cardiac aorta and cardiac tissue near the root on the cardiac CTA dataset, and achieves an average Dice similarity coefficient of 0.9698 ± 0.0081. The actual inference segmentation effect basically meets the preoperative needs of the clinic. Using the DL method based on the nnU-Net model solves the problems of low accuracy in threshold segmentation, bad segmentation of organs with fuzzy edges, and poor adaptability to different patients' cardiac CTA images. nnU-Net will become an excellent DL technology in cardiac CTA image segmentation tasks.
URI: https://hdl.handle.net/10356/168433
ISSN: 0034-6748
DOI: 10.1063/5.0084433
Schools: School of Computer Science and Engineering 
Rights: © 2022 Author(s). All rights reserved. This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This article appeared in Li, F., Sun, L., Lam, K., Zhang, S., Sun, Z., Peng, B., Xu, H. & Zhang, L. (2022). Segmentation of human aorta using 3D nnU-net-oriented deep learning. Review of Scientific Instruments, 93(11), 114103-, and may be found at https://doi.org/10.1063/5.0084433.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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