A Two-Stage Fully Automatic Segmentation Scheme Using Both 2D and 3D U-Net for Multi-sequence Cardiac MR

Xu, Haohao; Zhuangwei, Xu; Gu, Wei; Zhang, Qi

doi:10.1007/978-3-030-39074-7_33

Cited by 1 publication

(2 citation statements)

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“…Liu et al [57] designed a two-stage U-net for liver segmentation with an intermediate processing module between the two Unets. Xu et al [8] and Li et al [44] have both designed twostage cascaded U-nets in which the first network is a 2D Unet and the second network is a 3D U-net. Other two-stage U-net models are implemented in [5], [6], [29], [53], [71], [86]- [89].…”

Section: J Ensemble U-netmentioning

confidence: 99%

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U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

et al. 2021

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U-net is an image segmentation technique developed primarily for image segmentation tasks. These traits provide U-net with a high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in nearly all major image modalities, from CT scans and MRI to Xrays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. Given that U-net's potential is still increasing, this narrative literature review examines the numerous developments and breakthroughs in the U-net architecture and provides observations on recent trends. We also discuss the many innovations that have advanced in deep learning and discuss how these tools facilitate U-net. In addition, we review the different image modalities and application areas that have been enhanced by U-net.

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Section: J Ensemble U-netmentioning

confidence: 99%

“…U-net implementation has also been applied on cardiovascular MR images [4]- [6], [8]- [11], [47], [58], [62], [74], [83], [86], [90], [137]- [146] to segment structures of the heart. Cancer is a leading cause of deaths worldwide and MR is one of the strongest methods for proper prognosis of various types of cancers.…”

Section: Magnetic Resonance Imaging (Mri)mentioning

confidence: 99%