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    <meta name="author" content="Aditya P. Apte">
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			<li> Aditya P. Apte, Ph.D. </li>
			<li> Aditi Iyer, M.S. </li>
			<li> Eve LoCastro, M.S. </li>
			<li> Joseph O. Deasy, Ph.D. </li>
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        <tr>
          <th> Date </th>
          <th> Type </th>
          <th> Description </th>
          <th> GitHub hash </th>
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          <td> 09/30/2020 </td>
          <td> Bug </td>
          <td> Based SUV calculation on type of Decay correction. Used Series time for decay correction = "START". Note that Acquisition time was used irrespective of decay correction type. </td>
          <td> a9cb4fd </td>
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          <td> 10/12/2020 </td>
          <td> Bug </td>
          <td> Fixed bug where structure segments on 1st and last row of image were not rasterized. </td>
          <td> 95c4647 </td>
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      <h1 class="my-4">A Computational Environment for Radiological Research
        <small>Features</small>
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            <h4 class="card-title">
                <a href="https://github.com/cerr/CERR/wiki/Auto-Segmentation-models">Deep Learning segmentation models</a>
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            <a href="https://github.com/cerr/CERR/wiki/Auto-Segmentation-models"><img class="card-img-top" src="CERR_core/WebPage/images/dl_seg_container.png" alt=""></a>
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              <p class="card-text">CERR provides a platform to deploy deep learning segmentation models.
              Models can be deployed on Windows/Max/Linux operating systems via Singularity containers or Anaconda environments.
              CERR provides a large number of pre and post processing options to pass datasets to segmentation models.
              A library of models for segmenting structures useful for Radiotherapy outcomes modeling of Prostate, H&N and Lung is included. </p>
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            <h4 class="card-title">
              <a href="https://github.com/cerr/CERR/wiki/Radiomics">Radiomics and Texture calculation</a>
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            <a href="https://github.com/cerr/CERR/wiki/Radiomics"><img class="card-img-top" src="CERR_core/WebPage/images/texture_gui.png" alt=""></a>
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              <p class="card-text">CERR's Radiomics capabilities are built keeping in mind Speed, Quality Assurance and Interfacing with Clinical Software.</p>
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            <h4 class="card-title">
              <a href="https://github.com/cerr/CERR/wiki/Importing-to-CERR">Data Import</a>
            </h4>
            <a href="https://github.com/cerr/CERR/wiki/Importing-to-CERR"><img class="card-img-top" src="CERR_core/WebPage/images/data_import.png" alt=""></a>
            <div class="card-body">
              <p class="card-text">CERR can import data from a wide variety of formats such as RTOG, DICOM, MHA, NRRD. Various DICOM modalities like CT, PET, MR, US, MG, RTSTRUCT, PR, RTDOSE, RTPLAN are supported.</p>
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            <h4 class="card-title">
              <a href="https://github.com/cerr/CERR/wiki/Contouring-tools">Contouring</a>
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            <a href="https://github.com/cerr/CERR/wiki/Contouring-tools"><img class="card-img-top" src="CERR_core/WebPage/images/contouring_tools_brush_eraser.png" alt=""></a>
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              <p class="card-text">CERR provides Pencil, Brush and Active-contour based thresholding tools for manual segmentations. </p>
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          <div class="card h-100">
            <h4 class="card-title">
              <a href="https://github.com/cerr/CERR/wiki/dose-volume-histograms">Outcomes Modeling</a>
            </h4>
            <a href="https://github.com/cerr/CERR/wiki/dose-volume-histograms"><img class="card-img-top" src="CERR_core/WebPage/images/dvh_outcomes.png" alt=""></a>
            <div class="card-body">
              <p class="card-text">CERR provides utility functions to batch extract dose volume histogram (DVH) based features. These features can be used for modeling toxicity and tumor control. </p>
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            <h4 class="card-title">
              <a href="https://github.com/cerr/CERR/wiki/IMRT-optimization-interfacing-with-an-external-solver">IMRTP</a>
            </h4>
            <a href="https://github.com/cerr/CERR/wiki/IMRT-optimization-interfacing-with-an-external-solver"><img class="card-img-top" src="CERR_core/WebPage/images/imrtp_gui.jpg" alt=""></a>
            <div class="card-body">
              <p class="card-text">CERR provides access to the underlying RTPLAN meta-data. The dose calculation options are QIB and Monte Carlo algorithms. The influence matrix and the beamlets are accessible for IMRTP research.</p>
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          <div class="card h-100">
            <h4 class="card-title">
              <a href="https://github.com/cerr/CERR/wiki/ROE">ROE</a>
            </h4>
            <a href="https://github.com/cerr/CERR/wiki/ROE"><img class="card-img-top" src="CERR_core/WebPage/images/roe.png" alt=""></a>
            <div class="card-body">
              <p class="card-text">Radiotherapy Outcomes Explorer (ROE) is useful in exploring the clinical impact of scaling dose on Tumor Control Probability (TCP) and Normal Tissue Complication Probability (NTCP) in radiotherapy.</p>
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            <h4 class="card-title">
              <a href="https://github.com/cerr/CERR/wiki/Data-Structure-(The-planC-object)">User-friendly access to RT and Radiology meta-data</a>
            </h4>
            <a href="https://github.com/cerr/CERR/wiki/Data-Structure-(The-planC-object)"><img class="card-img-top" src="CERR_core/WebPage/images/planC_structure.png" alt="" width="80" height="280"></a>
            <div class="card-body">
              <p class="card-text">CERR provides an extensible data structure for radiotherapy and radiology objects. This makes it convenient to extract and transform the underlying data for prototyping algorithms.</p>
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