LumNet: A Deep Neural Network for Lumbar Paraspinal Muscles Segmentation
文献类型：会议论文
| 作者 | Zhang YD(张英迪)2,3,4,5,6 ; Shi ZL(史泽林)2,5,6; Wang H(王欢)1; Yan CN(阎崇楠)1; Wang LB(王蓝博)1; Mu YM(穆月明)1; Liu YP(刘云鹏)2,5,6; Wu SH(邬抒航)2,5,6; Liu TC(刘天赐)2,5,6
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| 出版日期 | 2019 |
| 会议日期 | December 2-5, 2019 |
| 会议地点 | Adelaide, SA, Australia |
| 关键词 | Convolutional neural networks attention mechanism lumber paraspinal muscles segmentation attention mechanism |
| 页码 | 574-585 |
| 英文摘要 | Lumber paraspinal muscles (LPM) segmentation is of essential importance in predicting response to treatment of low back pain. To date, all LPM segmentation methods are manually based instead of automatic. Manual segmentation of LPM requires vast radiological knowledge and experience. Moreover, the manual segmentation usually induces subjective variance. Therefore, an automatic segmentation is desireable. It is challenging to achieve automatic segmentation mainly because the ambiguous boundary of the LPM can be very difficult to locate. In this paper, we present a novel encoder-decoder and attention based deep convolutional neural network (CNN) to address this problem. With the help of skip connections, the encoder-decoder structure can capture both shadow and deep features which represent local and global information. Pre-trained VGG11 in ImageNet performed as encoder. In the decoder part, an attention block is applied to recalibrate the input feature. With the help of attention block, meaningful features are highlighted while irrelevant features are suppressed. To fully evaluate the performance of our proposed network, we construct the first large-scale LPM segmentation dataset with 1080 images and its segmentation masks. Experimental results show that our proposed network can not only achieve a good LPM segmentation result with a high dice score of 0.94 but also outperforms other state-of-the-art segmentation methods. |
| 产权排序 | 1 |
| 会议录 | AI 2019: Advances in Artificial Intelligence - 32nd Australasian Joint Conference, 2019, Proceedings
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| 会议录出版者 | Springer |
| 会议录出版地 | Berlin |
| 语种 | 英语 |
| ISSN号 | 0302-9743 |
| ISBN号 | 978-3-030-35287-5 |
| 源URL | [http://ir.sia.cn/handle/173321/26061]  |
| 专题 | 沈阳自动化研究所_光电信息技术研究室 |
| 通讯作者 | Zhang YD(张英迪) |
| 作者单位 | 1.Spine Surgery Department, Shengjing Hospital, Shenyang, China 2.Shenyang Institute of Automation, Chinese Academy of Sciences, Beijing, China; Institute for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; Key Lab of Opto-Electronic Information Process, Shenyang, China; 3.The Key Lab of Image Understanding and Computer Vision, Shenyang, China; 4.Spine Surgery Department, Shengjing Hospital, Shenyang, China 5.Key Lab of Opto-Electronic Information Process, Shenyang, China 6.The Key Lab of Image Understanding and Computer Vision, Shenyang, China |
| 推荐引用方式 GB/T 7714 | Zhang YD,Shi ZL,Wang H,et al. LumNet: A Deep Neural Network for Lumbar Paraspinal Muscles Segmentation[C]. 见:. Adelaide, SA, Australia. December 2-5, 2019. |
入库方式： OAI收割
来源：沈阳自动化研究所
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