Automated classification and measurement of fetal ultrasound images with attention feature pyramid network
文献类型：会议论文
| 作者 | Liu PF(刘鹏飞)1,2,3,4,5 ; Zhao HC(赵怀慈)1,2,3,4; Li PX(李培玄)1,2,3,4,5; Cao FD(曹飞道)1,2,3,4,5
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| 出版日期 | 2019 |
| 会议日期 | August 28-30, 2019 |
| 会议地点 | Shenyang, China |
| 关键词 | Fetal biometric measurement ultrasound image segmentation attention mechanism deep supervision |
| 页码 | 1-6 |
| 英文摘要 | Segmentation of anatomical structures in ultrasound images required radiological technology and a great deal of ultrasonic experience. The manual segmentation is often dependent on expertise of clinicians and time-consuming. Therefore, we present an automatic system for segmentation and measurement of ultrasound images. We propose a scale attention feature pyramid network (SAFNet) for fetal biometric measurements from two-dimensional ultrasound images. The scale attention module is steered to form feature pyramid at each level. Auxiliary layer is used to learn object boundary definition with deep supervision. Further, we present a two-stage framework which is an automatic classification measurement system (ACMS), firstly classifies the image type which has three labels: head, abdomen and femur. Then outputs the final segmentation result. The SAFNet results better performance on our datasets compared to the baseline U-Net. Experiments show that the ACMS results in classification accuracy of 95.27%/90.94%/94.93% of fetal head, abdomen and femur test set, respectively. Feature pyramid and attention mechanism inside the network for feature selection results in improvement in the segmentation accuracy. The ACMS can conveniently obtain segmentation result no matter what type is given. |
| 源文献作者 | Chinese Society for Optical Engineering |
| 产权排序 | 1 |
| 会议录 | Second Target Recognition and Artificial Intelligence Summit Forum
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| 会议录出版者 | SPIE |
| 会议录出版地 | Bellingham, USA |
| 语种 | 英语 |
| ISSN号 | 0277-786X |
| ISBN号 | 978-1-5106-3631-6 |
| WOS记录号 | WOS:000546230500096 |
| 源URL | [http://ir.sia.cn/handle/173321/26420]  |
| 专题 | 沈阳自动化研究所_光电信息技术研究室 |
| 通讯作者 | Liu PF(刘鹏飞) |
| 作者单位 | 1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, Liaoning 110016, China 2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang, Liaoning 110169, China 3.Key Laboratory of Opto-Electronic Information Processing, Shenyang, Liaoning 110016, China 4.Key Laboratory of Image Understanding and Computer Vision, Shenyang, Liaoning 110016, China 5.University of Chinese Academy of Sciences, Beijing 100049, China |
| 推荐引用方式 GB/T 7714 | Liu PF,Zhao HC,Li PX,et al. Automated classification and measurement of fetal ultrasound images with attention feature pyramid network[C]. 见:. Shenyang, China. August 28-30, 2019. |
入库方式： OAI收割
来源：沈阳自动化研究所
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