胰腺神经内分泌肿瘤筛选的分割分类
Segmentation for Classification of Screening Pancreatic Neuroendocrine Tumors
摘要：
这项工作提出了全面的结果，在早期发现胰腺神经内分泌肿瘤（PNDT），一组内分泌肿瘤发生在胰腺，这是胰腺癌的第二种常见类型，通过检查腹部CT扫描。据我们所知，这个任务以前没有被研究过作为一个计算任务。为了向放射科医生提供肿瘤位置，我们采用分割框架来通过检查是否至少有足够数量的体素被分割为肿瘤来对CT体积进行分类。为了定量分析我们的方法，我们收集并逐体素标记一个新的腹部CT数据集，该数据集包含$376$病例，每个病例都具有动脉和静脉相，其中$228$病例被诊断为PNNI，而其余$148$病例是正常的，这是目前我们所知的最大的PNNI数据集。为了将放射科医师的丰富知识纳入我们的框架，我们也对扩张的胰管进行了注释，这被认为是胰腺癌的高风险标志。定量而言，我们的方法优于最先进的分割网络，并在$81.08\%$的特异性下实现了$89.47\%$的灵敏度，这表明了通过早期肿瘤检测实现与癌症诊断相关的临床影响的潜在方向。
英文摘要：
This work presents comprehensive results to detect in the early stage the pancreatic neuroendocrine tumors (PNETs), a group of endocrine tumors arising in the pancreas, which are the second common type of pancreatic cancer, by checking the abdominal CT scans. To the best of our knowledge, this task has not been studied before as a computational task. To provide radiologists with tumor locations, we adopt a segmentation framework to classify CT volumes by checking if at least a sufficient number of voxels is segmented as tumors. To quantitatively analyze our method, we collect and voxelwisely label a new abdominal CT dataset containing $376$ cases with both arterial and venous phases available for each case, in which $228$ cases were diagnosed with PNETs while the remaining $148$ cases are normal, which is currently the largest dataset for PNETs to the best of our knowledge. In order to incorporate rich knowledge of radiologists to our framework, we annotate dilated pancreatic duct as well, which is regarded as the sign of high risk for pancreatic cancer. Quantitatively, our approach outperforms state-of-the-art segmentation networks and achieves a sensitivity of $89.47\%$ at a specificity of $81.08\%$, which indicates a potential direction to achieve a clinical impact related to cancer diagnosis by earlier tumor detection.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
