Semi-Automatic Segmentation of Paranasal Sinus from CT images Using Fully Convolutional Networks

被引：0

作者：

Xiong, Kun ^{[1
]}

Kitamura, Takahiro ^{[2
]}

Iwamoto, Yutaro ^{[1
]}

Han, Xian-Hua ^{[3
]}

Matsushiro, Naoki ^{[4
]}

Nishimura, Hiroshi ^{[2
]}

Chen, Yen-Wei ^{[1
]}

机构：

[1] Ritsumeikan Univ, Grad Sch Informat Sci & Engn, Kusatsu, Shiga, Japan

[2] Natl Hosp Org Osaka Natl Hosp, Dept Otorhinolaryngol Head & Neck Surg, Osaka, Japan

[3] Yamaguchi Univ, Grad Sch Sci & Technol Innovat, Yamaguchi, Japan

[4] Osaka Police Hosp, Dept Otolaryngol, Osaka, Japan

来源：

2018 IEEE 7TH GLOBAL CONFERENCE ON CONSUMER ELECTRONICS (GCCE 2018) | 2018年

关键词：

fully convolutional network (FCN); paranasal sinus segmentation; CT image; bounding box;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this paper, we present a novel image representation approach for paranasal sinus segmentation in computed tomography (CT) images of the brain. Our proposed method is based on a fully convolutional network (FCN), which is extensively used in a variety of computer vision applications. Experimental results demonstrate that our proposed method is efficient and effective for paranasal sinus segmentation.

引用

页码：268 / 269

页数：2

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