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Existing direct spine indices measurement approaches fail to explicitly focus on the task\u2010specific 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\u00a0task.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>In this paper, we propose a segmentation\u2010guided 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\u2010Net\u2010like network which includes a segmentation encoder and a decoder which generates multilevel segmentation features and segmentation prediction. The proposed segmentation\u2010guided attention module (SGAM) in the regression encoder extracts the attention\u2010aware regression feature under the guidance of the segmentation feature. Based on the attention\u2010aware regression feature, a fully connected layer is utilized to output the accurate spine indices\u00a0estimation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Experiments on the open\u2010access Lumbar Spine MRI data set show that SGRNet achieves state\u2010of\u2010the\u2010art performance with a mean absolute error of 0.49 mm and mean Pearson correlation coefficient of 0.956 for four indices\u00a0estimation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The proposed SGAM in SGRNet is capable of improving the performance of spine indices measurement by focusing on the task\u2010specific region and feature channel under the guidance of the segmentation\u00a0task.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.15961","type":"journal-article","created":{"date-parts":[[2022,8,27]],"date-time":"2022-08-27T06:10:14Z","timestamp":1661580614000},"page":"104-116","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Automated measurement of spine indices on axial MR images for lumbar spinal stenosis diagnosis using segmentation\u2010guided regression network"],"prefix":"10.1002","volume":"50","author":[{"given":"Chunlan","family":"Pang","sequence":"first","affiliation":[{"name":"Department of Nuclear Medicine Sun Yat\u2010sen University Cancer Center Guangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhihai","family":"Su","sequence":"additional","affiliation":[{"name":"Department of Spinal Surgery the Fifth Affiliated Hospital Sun Yat\u2010sen University Zhuhai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liyan","family":"Lin","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Medical Image Processing School of Biomedical Engineering Southern Medical University Guangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guoye","family":"Lin","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Medical Image Processing School of Biomedical Engineering Southern Medical University Guangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ji","family":"He","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering Guangzhou Medical University Guangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hai","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Spinal Surgery the Fifth Affiliated Hospital Sun Yat\u2010sen University Zhuhai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qianjin","family":"Feng","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Medical Image Processing School of Biomedical Engineering Southern Medical University Guangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shumao","family":"Pang","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering Guangzhou Medical University Guangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2022,9,4]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(21)00733-9"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2021204289"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1097\/01.blo.0000198722.70138.96"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1097\/00007632-199609010-00024"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1097\/BRS.0000000000001476"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.21037\/jss.2019.09.23"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0241309"},{"key":"e_1_2_10_9_1","doi-asserted-by":"crossref","unstructured":"NataliaF MeidiaH AfrilianaN Al\u2010KafriA SudirmanS.Methodology to determine important\u2010points location for automated lumbar spine stenosis diagnosis procedure. 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