Deep Learning Renal Segmentation for Fully Automated Radiation Dose Estimation in Unsealed Source Therapy.
Deep Learning Renal Segmentation for Fully Automated Radiation Dose Estimation in Unsealed Source Therapy.
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DOI：
10.3389/fonc.2018.00215
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发表时间：
2018
影响因子：
4.7
通讯作者：
Hicks RJ
中科院分区：
文献类型：
--
作者：
Jackson P;Hardcastle N;Dawe N;Kron T;Hofman MS;Hicks RJ
卷积神经网络（cnn）已经被证明是帮助物体检测的强大工具，并且像人类观察者一样，可以基于相对较小的参考对象队列进行训练。快速，准确的器官识别医学成像允许各种新的定量诊断技术。在使用靶向放射性核素进行治疗的情况下，它可能允许进行全面的辐射剂量分析，而使用传统方法往往会耗费大量时间。开发了一种基于三维cnn的自动图像分割工具，用于非对比CT图像的左右肾轮廓检测。该模型基于89例人工轮廓病例进行训练，并在接受177lu -前列腺特异性膜抗原-617治疗转移性前列腺癌的患者队列中进行测试。将自动生成的轮廓与专家绘制的轮廓进行比较，并根据骰子得分、平均距离一致和总分割体积来评估相似性。此外，这些轮廓线应用于治疗后定量SPECT成像计算的体素剂量图，以估计治疗后肾脏辐射剂量。神经网络分割能够高度准确地识别所有患者的左右肾。该系统被集成到医院图像数据库中，在大约90秒内为选定的研究返回轮廓。右肾和左肾的平均dice评分分别为0.91和0.86。3例囊性肾患者表现不佳，其中只有少数患者被纳入训练数据。人工算法和自动算法在平均辐射吸收剂量上无显著差异。使用cnn的自动轮廓显示有希望提供功能SPECT和可能的PET图像的定量评估；在这种情况下，与人类观察者相比，非密封源治疗的辐射剂量解释具有相当的准确性。
Convolutional neural networks (CNNs) have been shown to be powerful tools to assist with object detection and—like a human observer—may be trained based on a relatively small cohort of reference subjects. Rapid, accurate organ recognition in medical imaging permits a variety of new quantitative diagnostic techniques. In the case of therapy with targeted radionuclides, it may permit comprehensive radiation dose analysis in a manner that would often be prohibitively time-consuming using conventional methods. An automated image segmentation tool was developed based on three-dimensional CNNs to detect right and left kidney contours on non-contrast CT images. Model was trained based on 89 manually contoured cases and tested on a cohort of patients receiving therapy with 177Lu-prostate-specific membrane antigen-617 for metastatic prostate cancer. Automatically generated contours were compared with those drawn by an expert and assessed for similarity based on dice score, mean distance-to-agreement, and total segmented volume. Further, the contours were applied to voxel dose maps computed from post-treatment quantitative SPECT imaging to estimate renal radiation dose from therapy. Neural network segmentation was able to identify right and left kidneys in all patients with a high degree of accuracy. The system was integrated into the hospital image database, returning contours for a selected study in approximately 90 s. Mean dice score was 0.91 and 0.86 for right and left kidneys, respectively. Poor performance was observed in three patients with cystic kidneys of which only few were included in the training data. No significant difference in mean radiation absorbed dose was observed between the manual and automated algorithms. Automated contouring using CNNs shows promise in providing quantitative assessment of functional SPECT and possibly PET images; in this case demonstrating comparable accuracy for radiation dose interpretation in unsealed source therapy relative to a human observer.
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DOI：
10.1102/1470-7330.2011.0012
发表时间：
2011-06-15
期刊：
Cancer imaging : the official publication of the International Cancer Imaging Society
影响因子：
--
作者：
Beauregard JM;Hofman MS;Pereira JM;Eu P;Hicks RJ
通讯作者：
Hicks RJ
影响因子：
1.3
作者：
Shim, Hackjoon;Chang, Samuel;Bae, Kyongtae T.
通讯作者：
Bae, Kyongtae T.
DOI：
10.1109/tpami.2012.277
发表时间：
2013-08-01
影响因子：
23.6
作者：
Shin, Hoo-Chang;Orton, Matthew R.;Leach, Martin O.
通讯作者：
Leach, Martin O.
影响因子：
3.8
作者：
Jackson, Price A.;Beauregard, Jean-Mathieu;Hicks, Rodney J.
通讯作者：
Hicks, Rodney J.
使用深度扩张卷积神经网络自动分割直肠癌计划 CT 中的临床靶区和危险器官
影响因子：
3.8
作者：
Men, Kuo;Dai, Jianrong;Li, Yexiong
通讯作者：
Li, Yexiong