Automated measurement of spine indices on axial MR images for lumbar spinal stenosis diagnosis using segmentation‐guided regression network.
Saved in:
| Title: | Automated measurement of spine indices on axial MR images for lumbar spinal stenosis diagnosis using segmentation‐guided regression network. |
|---|---|
| Authors: | Pang, Chunlan1 (AUTHOR), Su, Zhihai2 (AUTHOR), Lin, Liyan3 (AUTHOR), Lin, Guoye3 (AUTHOR), He, Ji4 (AUTHOR), Lu, Hai2 (AUTHOR), Feng, Qianjin3 (AUTHOR), Pang, Shumao4 (AUTHOR) pangshumao@126.com |
| Source: | Medical Physics. Jan2023, Vol. 50 Issue 1, p104-116. 13p. |
| Subjects: | Spinal stenosis, Lumbar vertebrae, Magnetic resonance imaging, Spine, Pearson correlation (Statistics), Magnetic resonance |
| Abstract: | Purpose: Automated measurement of spine indices on axial magnetic resonance (MR) images plays a significant role in lumbar spinal stenosis diagnosis. Existing direct spine indices measurement approaches fail to explicitly focus on the task‐specific region or feature channel with the additional information for guiding. We aim to achieve accurate spine indices measurement by introducing the guidance of the segmentation task. Methods: In this paper, we propose a segmentation‐guided regression network (SGRNet) to achieve automated spine indices measurement. SGRNet consists of a segmentation path for generating the spine segmentation prediction and a regression path for producing spine indices estimation. The segmentation path is a U‐Net‐like network which includes a segmentation encoder and a decoder which generates multilevel segmentation features and segmentation prediction. The proposed segmentation‐guided attention module (SGAM) in the regression encoder extracts the attention‐aware regression feature under the guidance of the segmentation feature. Based on the attention‐aware regression feature, a fully connected layer is utilized to output the accurate spine indices estimation. Results: Experiments on the open‐access Lumbar Spine MRI data set show that SGRNet achieves state‐of‐the‐art performance with a mean absolute error of 0.49 mm and mean Pearson correlation coefficient of 0.956 for four indices estimation. Conclusions: The proposed SGAM in SGRNet is capable of improving the performance of spine indices measurement by focusing on the task‐specific region and feature channel under the guidance of the segmentation task. [ABSTRACT FROM AUTHOR] |
| Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | Purpose: Automated measurement of spine indices on axial magnetic resonance (MR) images plays a significant role in lumbar spinal stenosis diagnosis. Existing direct spine indices measurement approaches fail to explicitly focus on the task‐specific region or feature channel with the additional information for guiding. We aim to achieve accurate spine indices measurement by introducing the guidance of the segmentation task. Methods: In this paper, we propose a segmentation‐guided regression network (SGRNet) to achieve automated spine indices measurement. SGRNet consists of a segmentation path for generating the spine segmentation prediction and a regression path for producing spine indices estimation. The segmentation path is a U‐Net‐like network which includes a segmentation encoder and a decoder which generates multilevel segmentation features and segmentation prediction. The proposed segmentation‐guided attention module (SGAM) in the regression encoder extracts the attention‐aware regression feature under the guidance of the segmentation feature. Based on the attention‐aware regression feature, a fully connected layer is utilized to output the accurate spine indices estimation. Results: Experiments on the open‐access Lumbar Spine MRI data set show that SGRNet achieves state‐of‐the‐art performance with a mean absolute error of 0.49 mm and mean Pearson correlation coefficient of 0.956 for four indices estimation. Conclusions: The proposed SGAM in SGRNet is capable of improving the performance of spine indices measurement by focusing on the task‐specific region and feature channel under the guidance of the segmentation task. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 00942405 |
| DOI: | 10.1002/mp.15961 |
