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                        <div class="name">智能医学影像理解研究中心</div>
                        <div class="name-es">Intelligent Medical Image Understand Research Center(IMIU)</div>
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            <h3>医学影像智能解译系统</h3>
            <p>Intelligent medical image interpretation system</p>
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                  <li><a href="image_crop.html">图像切分</a></li> 
                  <li><a href="show_search_text.html">文字检索系统</a></li> 
                  <li><a href="show_search_pic.html">图像检索系统</a></li> 
                  <li><a href="Image_binary_segmentation.html">二维医学图像分割</a></li> 
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                <span>三维医学图像分割</span>
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                                <span>器官：</span> 
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                                    <option value="1" selected>肝脏</option> <option value="2">肺部</option> 
                                    <option value="3">肿瘤</option> <option value="4">腹部8器官</option> 
                                    <option value="5">腹部8器官无切片</option> <option value="6">腹部多器官</option> 
                                    <option value="7">腹部多器官无切片</option> <option value="8">头颈部多器官</option>
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                        <h2 class="art-title">成果介绍：</h2> 
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                              <div class="art-info">针对头颈肿瘤调强适形放疗中风险器官的勾画问题，提出了一种基于形状表示模型约束的全卷积神经网络进行头颈部CT的多图像分割方法。实现了快速、高精度的多器官分割，超越了其它传统分割算法和基于深度学习的方法。在AAPM2019 (The American Association of Physicists in Medicine )举办的RT-Mac Challenge中，提出基于注意力机制的卷积神经网络。该模型实现了左右腮腺、下颌腺、level-II、level-III淋巴结区域的自动化分割。在离线和在线竞赛中，分别获得了全球第三的好名次。在临床应用中，病人的CT图像一经上传进MIM系统，通过点击插件按键实现18个风险器官的自动化分割，大大缩减了物理师器官的勾画时间。经过对比，该模型的分割精度超过了市面上多种商用的自动化分割软件，并且已在美国UCLA放疗中心的临床上进入了试用阶段。同时在西安交通大学第一附属医院放疗科与空军军医大学放疗科进行应用。</div> 
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               <p>版权所有：西安电子科技大学智能医学影像理解研究中心&nbsp;&nbsp;&nbsp;地址：西安电子科技大学人工智能学院</p> 
               <p>西安电子科技大学智能医学影像理解研究中心建设和维护 </p> 
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