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    <title>Segmentation on Qualia Radiomics</title>
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      <title>AI-Powered Auto-Segmentation in Liver Cancer Therapy</title>
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      <description>&lt;p&gt;We’re excited to share our latest work published in &lt;em&gt;Technology in Cancer Research &amp;amp; Treatment&lt;/em&gt;: &lt;strong&gt;“Deep Learning-Based Auto-Segmentation for Liver Yttrium-90 Selective Internal Radiation Therapy”&lt;/strong&gt; — a collaboration between Jun Li, Rani Anne, and myself.&lt;/p&gt;
&lt;p&gt;This study introduces a &lt;strong&gt;deep learning (DL) model built on the 3D U-Net architecture&lt;/strong&gt;, developed to automatically segment the liver in CT scans for patients undergoing Y-90 Selective Internal Radiation Therapy (SIRT). Accurate liver segmentation is a critical step for calculating Y-90 dosage, traditionally done manually — a time-consuming and subjective process.&lt;/p&gt;</description>
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      <title>Deep Learning Segmentation for Accurate GTV and OAR Segmentation in MR-Guided Adaptive Radiotherapy for Pancreatic Cancer Patients</title>
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&lt;p&gt;&lt;a href=&#34;https://aapm.confex.com/aapm/2023am/meetingapp.cgi/Paper/3903&#34;&gt;AAPM 2023&lt;/a&gt;, &lt;a href=&#34;https://pubmed.ncbi.nlm.nih.gov/37785478/&#34;&gt;ASTRO 2023&lt;/a&gt;&lt;/p&gt;</description>
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      <title>2023 Accepted/Invited Annual Meeting abstracts</title>
      <link>https://www.qradiomics.com/posts/2023-05-08-2023-accepted-invited-annual-meeting-abstracts/</link>
      <pubDate>Mon, 08 May 2023 17:22:21 -0400</pubDate>
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&lt;li&gt;
&lt;p&gt;AAPM Annual Meeting (Houston, TX • July 23 ‒ 27, 2023)&lt;/p&gt;
&lt;ol&gt;
&lt;li&gt;
&lt;p&gt;Novel Functional Delta-Radiomics for Predicting Overall Survival in Lung Cancer Radiotherapy Using Cardiac FDG-PET Uptake&lt;br&gt;
&lt;strong&gt;Wookjin Choi&lt;/strong&gt;, Yevgeniy Vinogradskiy&lt;br&gt;
Interactive ePoster Discussions: Sunday, July 23, 2023: 3:00 PM - 3:30 PM, GRBCC, Exhibit Hall | Forum 6&lt;br&gt;
&lt;a href=&#34;https://aapm.confex.com/aapm/2023am/meetingapp.cgi/Paper/2188&#34;&gt;SU-300-IePD-F6-4 Novel Functional Delta-Radiomics for Predicting Overall Survival in Lung Cancer Radiotherapy Using Cardiac FDG-PET Uptake&lt;/a&gt;&lt;/p&gt;
&lt;/li&gt;
&lt;li&gt;
&lt;p&gt;Deep Learning Segmentation for Accurate GTV and OAR Segmentation in MR-Guided Adaptive Radiotherapy for Pancreatic Cancer Patients&lt;br&gt;
&lt;strong&gt;Wookjin Choi&lt;/strong&gt;, Hamidreza Nourzadeh, Yingxuan Chen, Christopher G. Ainsley, Vimal K. Desai, Alexander A. Kubli, Yevgeniy Vinogradskiy, Maria Werner-Wasik, Adam Mueller, and Karen E. Mooney&lt;br&gt;
&lt;a href=&#34;https://aapm.confex.com/aapm/2023am/meetingapp.cgi/Paper/3903&#34;&gt;PO-GePV-D-50 Deep Learning Segmentation for Accurate GTV and OAR Segmentation in MR-Guided Adaptive Radiotherapy for Pancreatic Cancer Patients&lt;/a&gt;&lt;/p&gt;</description>
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      <title>Lung structure segmentation and nodule detection based on 3D block analysis in CT image (Korean)</title>
      <link>https://www.qradiomics.com/posts/2014-10-02-lung-structure-segmentation-and-nodule-detection-based-on-3d-block-analysis-in-ct-image-korean/</link>
      <pubDate>Thu, 02 Oct 2014 01:38:33 -0400</pubDate>
      <guid>https://www.qradiomics.com/posts/2014-10-02-lung-structure-segmentation-and-nodule-detection-based-on-3d-block-analysis-in-ct-image-korean/</guid>
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      <title>Lung Volume Segmentation using Graph-Cut (Korean)</title>
      <link>https://www.qradiomics.com/posts/2014-10-02-lung-volume-segmentation-using-graph-cut-korean/</link>
      <pubDate>Thu, 02 Oct 2014 01:36:49 -0400</pubDate>
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