{"id":1685,"date":"2024-03-15T20:16:00","date_gmt":"2024-03-15T12:16:00","guid":{"rendered":"https:\/\/tongjicdi.com\/?p=1685"},"modified":"2025-05-29T21:14:51","modified_gmt":"2025-05-29T13:14:51","slug":"scanext-enhancing-3d-medical-image-segmentation-with-dual-attention-network-and-depth-wise-convolution","status":"publish","type":"post","link":"https:\/\/tongjicdi.com\/index.php\/2024\/03\/15\/scanext-enhancing-3d-medical-image-segmentation-with-dual-attention-network-and-depth-wise-convolution\/","title":{"rendered":"SCANeXt: Enhancing 3D medical image segmentation with dual attention network and depth-wise convolution"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"1685\" class=\"elementor elementor-1685\" data-elementor-post-type=\"post\">\n\t\t\t\t<div class=\"elementor-element elementor-element-767c761f e-flex e-con-boxed e-con e-parent\" data-id=\"767c761f\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-670400d8 elementor-widget elementor-widget-theme-post-featured-image elementor-widget-image\" data-id=\"670400d8\" data-element_type=\"widget\" data-widget_type=\"theme-post-featured-image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.22.0 - 24-06-2024 *\/\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t\t<img fetchpriority=\"high\" decoding=\"async\" width=\"800\" height=\"615\" src=\"https:\/\/tongjicdi.com\/wp-content\/uploads\/2025\/05\/gr001_lrg-1024x787.jpg\" class=\"attachment-large size-large wp-image-1686\" alt=\"\" srcset=\"https:\/\/tongjicdi.com\/wp-content\/uploads\/2025\/05\/gr001_lrg-1024x787.jpg 1024w, https:\/\/tongjicdi.com\/wp-content\/uploads\/2025\/05\/gr001_lrg-300x231.jpg 300w, https:\/\/tongjicdi.com\/wp-content\/uploads\/2025\/05\/gr001_lrg-768x590.jpg 768w, https:\/\/tongjicdi.com\/wp-content\/uploads\/2025\/05\/gr001_lrg-1536x1181.jpg 1536w, https:\/\/tongjicdi.com\/wp-content\/uploads\/2025\/05\/gr001_lrg-2048x1575.jpg 2048w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1c30edf1 elementor-widget elementor-widget-theme-post-title elementor-page-title elementor-widget-heading\" data-id=\"1c30edf1\" data-element_type=\"widget\" data-widget_type=\"theme-post-title.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.22.0 - 24-06-2024 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h1 class=\"elementor-heading-title elementor-size-default\">SCANeXt: Enhancing 3D medical image segmentation with dual attention network and depth-wise convolution<\/h1>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-123556ce elementor-widget elementor-widget-text-editor\" data-id=\"123556ce\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.22.0 - 24-06-2024 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p>Yajun Liu, Zenghui Zhang, Jiang Yue, Weiwei Guo<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2e046816 elementor-widget elementor-widget-text-editor\" data-id=\"2e046816\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Heliyon (2024)<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-57cc17c6 e-flex e-con-boxed e-con e-parent\" data-id=\"57cc17c6\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-ec063ce elementor-widget elementor-widget-heading\" data-id=\"ec063ce\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">\u6458\u8981\/Abstract<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-1985ee29 e-flex e-con-boxed e-con e-parent\" data-id=\"1985ee29\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-3b99337b e-con-full e-flex e-con e-child\" data-id=\"3b99337b\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-60f7f11d elementor-widget elementor-widget-text-editor\" data-id=\"60f7f11d\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>\u73b0\u6709\u7684 3D \u533b\u5b66\u56fe\u50cf\u5206\u5272\u65b9\u6cd5\u901a\u5e38\u53ef\u5206\u4e3a\u57fa\u4e8e\u5377\u79ef\u6216\u57fa\u4e8e Transformer \u7684\u65b9\u6cd5\u3002\u867d\u7136\u5377\u79ef\u795e\u7ecf\u7f51\u7edc (CNN) \u5728\u63d0\u53d6\u5c40\u90e8\u7279\u5f81\u65b9\u9762\u8868\u73b0\u51fa\u8272\uff0c\u4f46\u5b83\u4eec\u5728\u6355\u6349\u5168\u5c40\u8868\u5f81\u65b9\u9762\u9047\u5230\u4e86\u6311\u6218\u3002\u76f8\u6bd4\u4e4b\u4e0b\uff0c\u89c6\u89c9 Transformer \u4e2d\u5b58\u5728\u7684\u8fde\u7eed\u81ea\u6ce8\u610f\u6a21\u5757\u64c5\u957f\u6355\u6349\u957f\u8ddd\u79bb\u4f9d\u8d56\u5173\u7cfb\u5e76\u5b9e\u73b0\u6269\u5927\u7684\u611f\u53d7\u91ce\u3002\u5728\u672c\u6587\u4e2d\uff0c\u6211\u4eec\u63d0\u51fa\u4e86\u4e00\u79cd\u7528\u4e8e 3D \u533b\u5b66\u56fe\u50cf\u5206\u5272\u7684\u65b0\u65b9\u6cd5\uff0c\u79f0\u4e3aSCANeXt \u3002\u6211\u4eec\u7684\u65b9\u6cd5\u7ed3\u5408\u4e86\u53cc\u91cd\u6ce8\u610f\uff08\u7a7a\u95f4\u548c\u901a\u9053\u6ce8\u610f\uff09\u548c Conv NeXt\u7684\u4f18\u52bf\uff0c\u4ee5\u589e\u5f3a 3D \u533b\u5b66\u56fe\u50cf\u7684\u8868\u5f81\u5b66\u4e60\u3002\u5177\u4f53\u800c\u8a00\uff0c\u6211\u4eec\u63d0\u51fa\u4e86\u4e00\u79cd\u65b0\u9896\u7684\u81ea\u6ce8\u610f\u673a\u5236\uff0c\u65e8\u5728\u6db5\u76d6\u6574\u4e2a\u7279\u5f81\u7ef4\u5ea6\u7684\u7a7a\u95f4\u548c\u901a\u9053\u5173\u7cfb\u3002\u4e3a\u4e86\u8fdb\u4e00\u6b65\u63d0\u53d6\u591a\u5c3a\u5ea6\u7279\u5f81\uff0c\u6211\u4eec\u5728\u53cc\u91cd\u6ce8\u610f\u5757\u4e4b\u540e\u5f15\u5165\u4e86\u4e00\u4e2a\u53d7 ConvNeXt \u542f\u53d1\u7684\u6df1\u5ea6\u5377\u79ef\u5757\u3002\u5728 Synapse\u3001BraTS \u548c ACDC \u4e09\u4e2a\u57fa\u51c6\u6570\u636e\u96c6\u4e0a\u8fdb\u884c\u7684\u5927\u91cf\u8bc4\u4f30\u8bc1\u660e\u4e86\u6211\u4eec\u63d0\u51fa\u7684\u65b9\u6cd5\u5728\u51c6\u786e\u6027\u65b9\u9762\u7684\u6709\u6548\u6027\u3002\u6211\u4eec\u7684 SCANeXt \u6a21\u578b\u5728 ACDC \u6570\u636e\u96c6\u4e0a\u53d6\u5f97\u4e86\u6700\u5148\u8fdb\u7684\u7ed3\u679c\uff0c\u9ab0\u5b50\u76f8\u4f3c\u5ea6\u5f97\u5206\u4e3a 95.18%\uff0c\u8fdc\u8fdc\u4f18\u4e8e\u5f53\u524d\u65b9\u6cd5\u3002<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-660ab9aa e-con-full e-flex e-con e-child\" data-id=\"660ab9aa\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-23e91bbf elementor-widget elementor-widget-text-editor\" data-id=\"23e91bbf\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\tExisting approaches to 3D medical image segmentation can be generally categorized into convolution-based or transformer-based methods. While convolutional neural networks (CNNs) demonstrate proficiency in extracting local features, they encounter challenges in capturing global representations. In contrast, the consecutive self-attention modules present in vision transformers excel at capturing long-range dependencies and achieving an expanded receptive field. In this paper, we propose a novel approach, termed SCANeXt, for 3D medical image segmentation. Our method combines the strengths of dual attention (Spatial and Channel Attention) and ConvNeXt to enhance representation learning for 3D medical images. In particular, we propose a novel self-attention mechanism crafted to encompass spatial and channel relationships throughout the entire feature dimension. To further extract multiscale features, we introduce a depth-wise convolution block inspired by ConvNeXt after the dual attention block. Extensive evaluations on three benchmark datasets, namely Synapse, BraTS, and ACDC, demonstrate the effectiveness of our proposed method in terms of accuracy. Our SCANeXt model achieves a state-of-the-art result with a Dice Similarity Score of 95.18% on the ACDC dataset, significantly outperforming current methods.\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-3c7a0f0 e-flex e-con-boxed e-con e-parent\" data-id=\"3c7a0f0\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-3524f4cb elementor-widget elementor-widget-spacer\" data-id=\"3524f4cb\" data-element_type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.22.0 - 24-06-2024 *\/\n.elementor-column 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elementor-size-default\">\u76f8\u5173\u4fe1\u606f\/Info<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-3fdc5d5f e-flex e-con-boxed e-con e-parent\" data-id=\"3fdc5d5f\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t<div class=\"elementor-element elementor-element-611e3684 e-con-full e-flex e-con e-child\" data-id=\"611e3684\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-3973c104 elementor-widget elementor-widget-heading\" data-id=\"3973c104\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">\u4f5c\u8005\/Authors<br><\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bed4d56 elementor-widget elementor-widget-heading\" data-id=\"bed4d56\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">\u94fe\u63a5\/Link<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-30f5a72 e-con-full e-flex e-con e-child\" data-id=\"30f5a72\" data-element_type=\"container\">\n\t\t\t\t<div class=\"elementor-element elementor-element-7561ac23 elementor-widget elementor-widget-text-editor\" data-id=\"7561ac23\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Yajun Liu, Zenghui Zhang, Jiang Yue, Weiwei Guo<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-47c647ab elementor-widget elementor-widget-text-editor\" data-id=\"47c647ab\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>https:\/\/www.cell.com\/heliyon\/fulltext\/S2405-8440(24)02806-8<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-46f19693 e-flex e-con-boxed e-con e-parent\" data-id=\"46f19693\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-560e80b9 elementor-widget elementor-widget-spacer\" data-id=\"560e80b9\" data-element_type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-51d56fea e-flex e-con-boxed e-con e-parent\" data-id=\"51d56fea\" 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href=\"https:\/\/tongjicdi.com\/wp-content\/uploads\/2025\/05\/gr008_lrg.jpg\" data-elementor-open-lightbox=\"no\">\n\t\t\t\t\t<div class=\"e-gallery-image elementor-gallery-item__image\" data-thumbnail=\"https:\/\/tongjicdi.com\/wp-content\/uploads\/2025\/05\/gr008_lrg.jpg\" data-width=\"1460\" data-height=\"838\" aria-label=\"\" role=\"img\" ><\/div>\n\t\t\t\t\t\t\t\t\t\t\t<div class=\"elementor-gallery-item__overlay\"><\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Existing approaches to 3D medical image segmentation can be generally categorized into convolution-based or transformer-based methods. While convolutional neural networks (CNNs) demonstrate proficiency in extracting local features, they encounter challenges in capturing global representations. In contrast, the consecutive self-attention modules present in vision transformers excel at capturing long-range dependencies and achieving an expanded receptive field. In this paper, we propose a novel approach, termed SCANeXt, for 3D medical image segmentation. Our method combines the strengths of dual attention (Spatial and Channel Attention) and ConvNeXt to enhance representation learning for 3D medical images. In particular, we propose a novel self-attention mechanism crafted to encompass spatial and channel relationships throughout the entire feature dimension. To further extract multiscale features, we introduce a depth-wise convolution block inspired by ConvNeXt after the dual attention block. Extensive evaluations on three benchmark datasets, namely Synapse, BraTS, and ACDC, demonstrate the effectiveness of our proposed method in terms of accuracy. Our SCANeXt model achieves a state-of-the-art result with a Dice Similarity Score of 95.18% on the ACDC dataset, significantly outperforming current methods.<\/p>\n","protected":false},"author":1,"featured_media":1686,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[29],"tags":[],"class_list":["post-1685","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-cv"],"_links":{"self":[{"href":"https:\/\/tongjicdi.com\/index.php\/wp-json\/wp\/v2\/posts\/1685","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/tongjicdi.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/tongjicdi.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/tongjicdi.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/tongjicdi.com\/index.php\/wp-json\/wp\/v2\/comments?post=1685"}],"version-history":[{"count":4,"href":"https:\/\/tongjicdi.com\/index.php\/wp-json\/wp\/v2\/posts\/1685\/revisions"}],"predecessor-version":[{"id":1701,"href":"https:\/\/tongjicdi.com\/index.php\/wp-json\/wp\/v2\/posts\/1685\/revisions\/1701"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/tongjicdi.com\/index.php\/wp-json\/wp\/v2\/media\/1686"}],"wp:attachment":[{"href":"https:\/\/tongjicdi.com\/index.php\/wp-json\/wp\/v2\/media?parent=1685"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/tongjicdi.com\/index.php\/wp-json\/wp\/v2\/categories?post=1685"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/tongjicdi.com\/index.php\/wp-json\/wp\/v2\/tags?post=1685"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}