A 3D Dual Path U-Net of Cancer Segmentation Based on MRI
Autor: | Chang Liu, He Yu, Xi Yu, Jian Zhang, Hu Ke, Hong Chao Zhu |
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Rok vydání: | 2018 |
Předmět: |
business.industry
Computer science Deep learning Pattern recognition Context (language use) Image segmentation DUAL (cognitive architecture) 01 natural sciences Field (computer science) Convolution 010309 optics 03 medical and health sciences 0302 clinical medicine 0103 physical sciences Path (graph theory) Segmentation Artificial intelligence business 030217 neurology & neurosurgery |
Zdroj: | 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC). |
DOI: | 10.1109/icivc.2018.8492781 |
Popis: | Nasopharyngeal Carcinoma (NPC) is one of the most common malignant tumors in China. However, the cancer's region is subtle, variability and irregular. In the traditional diagnostic way, clinicians' diagnosis relies on manual delineations which are time consuming and require rich prior experience. Recently, the deep learning architecture of U-Net and Dual Path Network (DPN) apply well in the biomedical segmentation and nature scene respectively. However, U-Net cannot extract abundance texture information from the data and DPN cannot utilize the information of shallow layer and deep layer closely. Moreover, both of them are applied on the slices of images instead of 3D images directly, which discard the anatomic context in 3D spatial domain. Consequently, this paper proposed a novel 3D convolutional network-Dual Path U-Network (DPU) which integrates U-Net and DPN to segment the cancer's region of NPC automatically. The experiment on the MRI dataset of NPC patients has shown that the DPU is more successful than the corresponding 3D version of U-Net and DPN in the field of 3D biomedical image segmentation automatically. |
Databáze: | OpenAIRE |
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