{"id":177,"date":"2025-04-02T22:59:28","date_gmt":"2025-04-02T14:59:28","guid":{"rendered":"http:\/\/106.52.84.14\/?p=177"},"modified":"2025-04-02T22:59:28","modified_gmt":"2025-04-02T14:59:28","slug":"19-12-mics%e5%9c%a8%e7%ba%bf%e5%ad%a6%e6%9c%af%e8%ae%b2%e5%ba%a7%ef%bc%9a%e7%a7%a6%e7%92%9f","status":"publish","type":"post","link":"https:\/\/www.mics-ai.com\/index.php\/2025\/04\/02\/19-12-mics%e5%9c%a8%e7%ba%bf%e5%ad%a6%e6%9c%af%e8%ae%b2%e5%ba%a7%ef%bc%9a%e7%a7%a6%e7%92%9f\/","title":{"rendered":"[#19-12] MICS\u5728\u7ebf\u5b66\u672f\u8bb2\u5ea7\uff1a\u79e6\u749f"},"content":{"rendered":"\n
\u5609\u5bbe<\/strong><\/td>\u79e6\u749f\uff08\u9999\u6e2f\u7406\u5de5\u5927\u5b66\uff09<\/td><\/tr>
\u65f6\u95f4<\/strong><\/td>2019\u5e7407\u670816\u65e5\uff08\u661f\u671f\u4e8c\uff09\u4e0b\u53488:00\uff08\u5317\u4eac\u65f6\u95f4\uff09<\/td><\/tr>
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\u4e3b\u6301<\/strong><\/td>\u738b\u8fde\u751f\uff08\u53a6\u95e8\u5927\u5b66\uff09<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

\u62a5\u544a\u6458\u8981<\/strong><\/p>\n\n\n\n

\u5148\u8fdb\u7684\u8ba1\u7b97\u673a\u6280\u672f\u4e0d\u65ad\u63a8\u52a8\u624b\u672f\u6280\u672f\u5411\u81ea\u52a8\u5316\u3001\u667a\u80fd\u5316\uff0c\u4e2a\u6027\u5316\u53d1\u5c55\u3002\u79e6\u535a\u58eb\u53ca\u5176\u56e2\u961f\u548c\u5408\u4f5c\u8005\u5728\u5229\u7528\u6df1\u5ea6\u5b66\u4e60\u548c\u865a\u62df\u73b0\u5b9e\u6280\u672f\u8fdb\u884c\u667a\u80fd\u5316\u624b\u672f\u8bad\u7ec3\u53ca\u8ba1\u5212\u7cfb\u7edf\u7814\u53d1\u65b9\u9762\u5177\u6709\u5341\u591a\u5e74\u7684\u7ecf\u9a8c\u3002\u8bb2\u5ea7\u4e2d\uff0c\u9996\u5148\u4ecb\u7ecd\u6df1\u5ea6\u5b66\u4e60\u6280\u672f\u5728\u533b\u5b66\u56fe\u50cf\u81ea\u52a8\u5316\u5206\u5272\u4e0a\u53d6\u5f97\u7684\u6210\u679c\uff0c\u5305\u62ec\u809d\u810fCT\u56fe\u50cf\u81ea\u52a8\u5316\u5206\u5272\uff0c\u8111\u90e8MR\u56fe\u50cf\u81ea\u52a8\u5316\u5206\u5272\uff0c\u5fc3\u810fMR\u56fe\u50cf\u81ea\u52a8\u5316\u5206\u5272\uff0c\u80be\u810fMR\u56fe\u50cf\u81ea\u52a8\u5316\u5206\u5272\uff0c\u4e09\u7ef4\u8d85\u58f0\u56fe\u50cf\u81ea\u52a8\u5316\u5206\u5272\u7b49\uff0c\u8fd9\u4e9b\u5206\u5272\u6280\u672f\u4e3a\u6784\u5efa\u667a\u80fd\u5316\u624b\u672f\u8bad\u7ec3\u53ca\u8ba1\u5212\u7cfb\u7edf\u5960\u5b9a\u4e86\u624e\u5b9e\u7684\u57fa\u7840\u3002\u968f\u540e\u4ecb\u7ecd\u51e0\u4e2a\u8fd1\u5e74\u7814\u53d1\u7684\u624b\u672f\u8bad\u7ec3\u53ca\u8ba1\u5212\u7cfb\u7edf\uff0c\u5305\u62ec\u5404\u79cd\u9aa8\u79d1\u624b\u672f\uff0c\u8d85\u58f0\u5f15\u5bfc\u4e0b\u7684\u7a7f\u523a\u624b\u672f\uff0c\u8840\u7ba1\u4ecb\u5165\u624b\u672f\uff0c\u80c6\u56ca\u5207\u9664\u624b\u672f\uff0c\u6574\u5f62\u624b\u672f\u4ee5\u53ca\u673a\u5668\u4eba\u624b\u672f\u7b49\u3002<\/p>\n\n\n\n

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  1. Q. Dou, L. Yu, H. Chen, Y. Jin, X. Yang, J. Qin, P. A. Heng. 3D Deeply Supervised Network for Automated Segmentation of Volumetric Medical Images, Medical Image Analysis, vol. 41, pp. 40-54.<\/li>\n\n\n\n
  2. H. Chen, Q. Dou, L. Yu, J. Qin, P. A. Heng. VoxResNet: Deep Voxelwise Residual Networks for Brain Segmentation from 3D MR Images, NeuroImage, vol. 170, pp. 446-455.<\/li>\n\n\n\n
  3. H. Chen, X. Qi, L. Yu, Q. Dou, J. Qin and P. A. Heng. DCAN: Deep Contour-aware Networks for Object Instance Segmentation from Histology Images, Medical Image Analysis, 36, pp. 135-146, 2017.<\/li>\n\n\n\n
  4. M. Wei, J. Huang, X. Xie, L. Liu, J. Wang, and J. Qin, Mesh Denoising Guided by Patch Normal Co-filtering via Kernel Low-rank Recovery, accepted to be published in IEEE Transactions on Visualization and Computer Graphics.<\/li>\n\n\n\n
  5. X. Liao, W. Si, Z. Yuan, H. Sun, J. Qin, Q. Wang and P. A. Heng. Animating Wall-bounded Turbulent Smoke via Filament-Mesh Particle-Particle Method, IEEE Transactions on Visualization and Computer Graphics, vol. 24, no. 3, pp.1260-1273.<\/li>\n\n\n\n
  6. X. Liu, W. M. Pang, J. Qin, and C. W. Fu. Turbulence Simulation by Adaptive Multi-Relaxation Lattice Boltzmann Modeling, IEEE Transactions on Visualization and Computer Graphics, vol. 20, no. 2, pp.289-302, 2014.<\/li>\n<\/ol>\n\n\n\n

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    \u7279\u522b\u9e23\u8c22\u672c\u6b21Webinar\u4e3b\u8981\u7ec4\u7ec7\u8005\uff1a<\/strong>
    \u738b\u8fde\u751f\uff08\u53a6\u95e8\u5927\u5b66\uff09<\/strong><\/p>\n\n\n\n

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    \u6d3b\u52a8\u987b\u77e5<\/strong><\/p>\n\n\n\n

    1. MICS\u5728\u7ebf\u5b66\u672f\u8bb2\u5ea7\u4f9d\u6258\u5728\u7ebf\u76f4\u64ad\u5e73\u53f0\u8fdb\u884c\uff0c\u542c\u4f17\u8bf7\u70b9\u51fb\u76f4\u64ad\u94fe\u63a5https:\/\/live.polyv.cn\/watch\/346748\uff08\u6ce8\uff1a\u8be5\u94fe\u63a5\u4e3a07\u670816\u65e5\u62a5\u544a\u94fe\u63a5\uff0c\u8be5\u94fe\u63a5\u6bcf\u671f\u4f1a\u548c\u8bb2\u8005\u4fe1\u606f\u4e00\u8d77\u66f4\u65b0\uff09\u53c2\u52a0\u6d3b\u52a8\uff0c\u652f\u6301\u5b89\u88c5Windows\u7cfb\u7edf\u7684\u7535\u8111\u3001MAC\u7535\u8111\u3001\u624b\u673a\u7b49\u8bbe\u5907\uff1b\u624b\u673a\u5ba2\u6237\u7aef\u4e5f\u53ef\u76f4\u63a5\u626b\u63cf\u4e8c\u7ef4\u7801\u8fdb\u5165\u76f4\u64ad\uff1b<\/p>\n\n\n

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    \"\"<\/figure>\n<\/div>\n\n\n

    \u53ef\u76f4\u63a5\u626b\u63cf\u4e8c\u7ef4\u7801\u8fdb\u516507\u670816\u65e5\u76f4\u64ad<\/p>\n\n\n\n

    2. \u6d3b\u52a8\u5f00\u59cb\u524d5\u5206\u949f\u5de6\u53f3\uff0c\u8bb2\u8005\u4f1a\u5f00\u542f\u76f4\u64ad\uff0c\u6d3b\u52a8\u65f6\u8bb2\u8005\u4f1a\u4e0a\u4f20PPT\u6216\u5171\u4eab\u5c4f\u5e55\uff0c\u542c\u4f17\u53ef\u4ee5\u770b\u5230Slides\uff0c\u542c\u5230\u8bb2\u8005\u7684\u8bed\u97f3\uff0c\u5e76\u901a\u8fc7\u804a\u5929\u529f\u80fd\u4e0e\u8bb2\u8005\u4ea4\u4e92\uff1b<\/p>\n\n\n\n

    3. \u6d3b\u52a8\u8fc7\u7a0b\u4e2d\uff0c\u8bf7\u4e0d\u8981\u8bf4\u65e0\u5173\u8bdd\u8bed\uff0c\u4ee5\u514d\u5f71\u54cd\u6d3b\u52a8\u6b63\u5e38\u8fdb\u884c\uff1b\u6d3b\u52a8\u8fc7\u7a0b\u4e2d\uff0c\u5982\u51fa\u73b0\u542c\u4e0d\u5230\u6216\u770b\u4e0d\u5230\u89c6\u9891\u7b49\u95ee\u9898\uff0c\u5efa\u8bae\u9000\u51fa\u518d\u91cd\u65b0\u8fdb\u5165\uff0c\u4e00\u822c\u90fd\u80fd\u89e3\u51b3\u95ee\u9898\uff1b\u5efa\u8bae\u901a\u8fc7\u901f\u5ea6\u8f83\u5feb\u7684\u7f51\u7edc\u53c2\u52a0\u6d3b\u52a8\uff0c\u4f18\u5148\u91c7\u7528\u6709\u7ebf\u7f51\u7edc\u8fde\u63a5\uff1b<\/p>\n\n\n\n

    4. \u540e\u7eed\u6bcf\u671f\u8bb2\u5ea7\u4fe1\u606f\uff0c\u4f1a\u901a\u8fc7MICS\u5fae\u4fe1\u516c\u4f17\u53f7\u201c\u533b\u5b66\u56fe\u50cf\u8ba1\u7b97\u9752\u5e74\u7814\u8ba8\u4f1a\u201d\uff08\u626b\u63cf\u4e0b\u65b9\u4e8c\u7ef4\u7801\u5173\u6ce8MICS\u5fae\u4fe1\u516c\u4f17\u53f7\uff09\u6216MICS QQ\u7fa4\uff08\u7fa4\u53f7\uff1a641894878\uff09\u8fdb\u884c\u901a\u77e5\uff08\u6ce8\uff1a\u7533\u8bf7\u52a0\u5165MICS QQ\u7fa4\u65f6\u9700\u9a8c\u8bc1\u59d3\u540d\u3001\u5355\u4f4d\u548c\u8eab\u4efd\uff0c\u7f3a\u4e00\u4e0d\u53ef\uff1b\u5165\u7fa4\u540e\uff0c\u8bf7\u5b9e\u540d\uff0c\u59d3\u540d\u8eab\u4efd\u5355\u4f4d\uff1b\u8eab\u4efd\uff1a\u5b66\u6821\u53ca\u79d1\u7814\u5355\u4f4d\u4eba\u5458T\uff0c\u4f01\u4e1a\u7814\u53d1I\uff0c\u535a\u58ebD\uff0c\u7855\u58ebM\uff09\u3002<\/p>\n\n\n

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    \"\"<\/figure>\n<\/div>\n\n\n

    \u201c\u533b\u5b66\u56fe\u50cf\u8ba1\u7b97\u9752\u5e74\u7814\u8ba8\u4f1a\u201d\u5fae\u4fe1\u516c\u4f17\u53f7<\/p>\n\n\n\n

    MICS\u5728\u7ebf\u5b66\u672f\u8bb2\u5ea7\u7684\u6a21\u5f0f\u548c\u7ec4\u7ec7\u65b9\u5f0f\u501f\u9274\u4e86\u5f88\u591aVALSE\u7684\u7ecf\u9a8c\uff0c\u4eceVALSE\u5f97\u5230\u4e86\u5f88\u591a\u7684\u542f\u53d1\uff0c\u5728\u6b64\u5bf9VALSE\u7ec4\u59d4\u4f1a\u8868\u793a\u8877\u5fc3\u7684\u611f\u8c22\uff0c\u4e5f\u795d\u613fMICS\u548cVALSE\u8d8a\u529e\u8d8a\u597d\uff01<\/p>\n\n\n\n

    \n\n\n\n

    [#18-01] \u6c88\u5b9a\u521a-\u5317\u5361\u7f57\u6765\u7eb3\u5927\u5b66\u6559\u5802\u5c71\u5206\u6821<\/a><\/p>\n\n\n\n

    [#19-01] \u5f20\u9053\u5f3a-\u5357\u4eac\u822a\u7a7a\u822a\u5929\u5927\u5b66<\/a><\/p>\n\n\n\n

    [#19-02] \u9648\u65b0\u5efa-\u82cf\u5dde\u5927\u5b66<\/a><\/p>\n\n\n\n

    [#19-03] \u5b59\u5251-\u897f\u5b89\u4ea4\u901a\u5927\u5b66<\/a><\/p>\n\n\n\n

    [#19-04] \u5f90\u519b-\u5357\u4eac\u4fe1\u606f\u5de5\u7a0b\u5927\u5b66<\/a><\/p>\n\n\n\n

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    [#19-06] \u502a\u4e1c-\u6df1\u5733\u5927\u5b66<\/a><\/p>\n\n\n\n

    [#19-07] \u738b\u6d2a\u51ef-\u5927\u8fde\u7406\u5de5\u5927\u5b66<\/a><\/p>\n\n\n\n

    [#19-08] \u5e84\u5413\u6d77-\u590d\u65e6\u5927\u5b66<\/a><\/p>\n\n\n\n

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    [#19-10] \u859b\u5fe0-\u4e0a\u6d77\u8054\u5f71\u667a\u80fd\u533b\u7597\u79d1\u6280\u6709\u9650\u516c\u53f8<\/a><\/p>\n\n\n\n

    [#19-11] \u5f20\u5bd2-\u5317\u5361\u7f57\u6765\u7eb3\u5927\u5b66\u6559\u5802\u5c71\u5206\u6821<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

    \u5609\u5bbe \u79e6\u749f\uff08\u9999\u6e2f\u7406\u5de5\u5927\u5b66\uff09 \u65f6\u95f4 201…<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[4],"tags":[],"class_list":["post-177","post","type-post","status-publish","format-standard","hentry","category-4"],"_links":{"self":[{"href":"https:\/\/www.mics-ai.com\/index.php\/wp-json\/wp\/v2\/posts\/177","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.mics-ai.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.mics-ai.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.mics-ai.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.mics-ai.com\/index.php\/wp-json\/wp\/v2\/comments?post=177"}],"version-history":[{"count":0,"href":"https:\/\/www.mics-ai.com\/index.php\/wp-json\/wp\/v2\/posts\/177\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.mics-ai.com\/index.php\/wp-json\/wp\/v2\/media?parent=177"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.mics-ai.com\/index.php\/wp-json\/wp\/v2\/categories?post=177"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.mics-ai.com\/index.php\/wp-json\/wp\/v2\/tags?post=177"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}