Combining deep learning with anatomical analysis for segmentation of the portal vein for liver SBRT planning.
Combining deep learning with anatomical analysis for segmentation of the portal vein for liver SBRT planning.
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DOI：
10.1088/1361-6560/aa9262
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发表时间：
2017-11-10
影响因子：
3.5
通讯作者：
Xing L
中科院分区：
文献类型：
--
作者：
Ibragimov B;Toesca D;Chang D;Koong A;Xing L
由于潜在的低血管对比度、复杂的门静脉解剖结构和源自基准标记和血管支架的图像伪影，用于肝脏放疗计划的门静脉（PV）自动分割是一项具有挑战性的任务。在本文中，我们提出了一个新的框架自动PV分割计算机断层扫描（CT）图像。我们应用卷积神经网络（CNN）使用具有参考注释的CT图像训练集来学习PV的一致外观模式，然后在以前看不见的CT图像中增强PV。马尔可夫随机场（MRF）被进一步用于平滑CNN增强结果并去除孤立的错误分割区域。最后，基于CNN-MRF的增强通过依赖于肺静脉解剖学特性（如管状和分支组成）的肺静脉中心线检测来增强。该框架在临床数据库上进行了验证，该数据库包含72例计划接受肝脏立体定向体部放射治疗的患者的CT图像。当分割包含在感兴趣的肺静脉区域中时，获得的分割准确度分别为DSC = 0.83和η = 1.08 mm（根据中值Dice系数和平均对称表面距离）。所获得的结果表明，CNN和解剖分析可用于PV的准确分割，并可能集成到肝脏放射治疗计划中。
Automated segmentation of portal vein (PV) for liver radiotherapy planning is a challenging task due to potentially low vasculature contrast, complex PV anatomy and image artifacts originated from fiducial markers and vasculature stents. In this paper, we propose a novel framework for automated PV segmentation from computed tomography (CT) images. We apply convolutional neural networks (CNN) to learn consistent appearance patterns of PV using a training set of CT images with reference annotations and then enhance PV in previously unseen CT images. Markov Random Fields (MRF) were further used to smooth the CNN enhancement results and remove isolated mis-segmented regions. Finally, CNN-MRF-based enhancement was augmented with PV centerline detection that relied on PV anatomical properties such as tubularity and branch composition. The framework was validated on a clinical database with 72 CT images of patients scheduled to liver stereotactic body radiation therapy. The obtained segmentation accuracy was DSC = 0.83 and η = 1.08 mm in terms of the median Dice coefficient and mean symmetric surface distance, respectively, when segmentation is encompassed into the PV region of interest. The obtained results indicate that CNN and anatomy analysis can be used for accurate segmentation of PV and potentially integrated into liver radiation therapy planning.
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影响因子：
4.6
作者：
Esneault, Simon;Lafon, Cyril;Dillenseger, Jean-Louis
通讯作者：
Dillenseger, Jean-Louis
影响因子：
10.6
作者：
Cheng, Yuanzhi;Hu, Xin;Tamura, Shinichi
通讯作者：
Tamura, Shinichi
DOI：
10.1007/bfb0056195
发表时间：
1998-01-01
期刊：
MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION - MICCAI'98
影响因子：
--
作者：
Frangi, AF;Niessen, WJ;Viergever, MA
通讯作者：
Viergever, MA
影响因子：
4.6
作者：
Gang, L;Chutatape, O;Krishnan, SM
通讯作者：
Krishnan, SM
影响因子：
10.9
作者：
Bauer, Christian;Pock, Thomas;Beichel, Reinhard
通讯作者：
Beichel, Reinhard