Automated Contouring of Contrast and Noncontrast Computed Tomography Liver Images With Fully Convolutional Networks.
Automated Contouring of Contrast and Noncontrast Computed Tomography Liver Images With Fully Convolutional Networks.
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DOI：
10.1016/j.adro.2020.04.023
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发表时间：
2021-01
影响因子：
2.3
通讯作者：
Brock KK
中科院分区：
文献类型：
--
作者：
Anderson BM;Lin EY;Cardenas CE;Gress DA;Erwin WD;Odisio BC;Koay EJ;Brock KK
肝脏的可变形性质可能使病灶治疗具有挑战性，并且无法通过简单的刚性配准技术充分解决。更先进的配准技术可以考虑变形（例如，生物力学建模），但需要为每次扫描分割整个肝脏，这是一个时间密集型过程。我们假设完全卷积网络可以用于快速准确地自动分割肝脏，消除生物力学建模的时间瓶颈。本研究收集了在我们机构接受治疗的183例患者的计算机断层扫描的手动肝脏分割和来自医学图像计算和计算机辅助干预挑战的30次扫描。研究了三种用于肝脏快速自动分割的架构（VGG-16、DeepLabv 3+和三维UNet）。将五十六例病例作为定量模型评价的最终测试集。使用Dice相似系数和平均表面距离评估自动分割的准确性。3名放射肿瘤学家还对50例既往接受过临床治疗的肝脏轮廓独立病例进行了定性评价。使用最终模型DeepLabv 3+的测试组的平均（最小-最大）平均表面距离如下：μContrast（N = 17）：0.99 mm（0.47-2.2），μNon_Contrast（N = 19）l：1.12 mm（0.41-2.87）和μ Miccal（N = 30）t：1.48 mm（0.82-3.96）。定性评价显示，在设盲比较中，50次自动分割中有30次（60%）优于手动轮廓（多数投票），50次自动分割中有48次（96%）被至少1名审查医生视为临床可接受。在大多数情况下，与手动定义的轮廓相比，自动分割是首选。在本研究中实现的以高精度快速分割肝脏的能力具有将基于生物力学模型的配准有效集成到临床工作流程中的潜力。
The deformable nature of the liver can make focal treatment challenging and is not adequately addressed with simple rigid registration techniques. More advanced registration techniques can take deformations into account (eg, biomechanical modeling) but require segmentations of the whole liver for each scan, which is a time-intensive process. We hypothesize that fully convolutional networks can be used to rapidly and accurately autosegment the liver, removing the temporal bottleneck for biomechanical modeling. Manual liver segmentations on computed tomography scans from 183 patients treated at our institution and 30 scans from the Medical Image Computing & Computer Assisted Intervention challenges were collected for this study. Three architectures were investigated for rapid automated segmentation of the liver (VGG-16, DeepLabv3 +, and a 3-dimensional UNet). Fifty-six cases were set aside as a final test set for quantitative model evaluation. Accuracy of the autosegmentations was assessed using Dice similarity coefficient and mean surface distance. Qualitative evaluation was also performed by 3 radiation oncologists on 50 independent cases with previously clinically treated liver contours. The mean (minimum-maximum) mean surface distance for the test groups with the final model, DeepLabv3 +, were as follows: μContrast(N = 17): 0.99 mm (0.47-2.2), μNon_Contrast(N = 19)l: 1.12 mm (0.41-2.87), and μMiccai(N = 30)t: 1.48 mm (0.82-3.96). The qualitative evaluation showed that 30 of 50 autosegmentations (60%) were preferred to manual contours (majority voting) in a blinded comparison, and 48 of 50 autosegmentations (96%) were deemed clinically acceptable by at least 1 reviewing physician. The autosegmentations were preferred compared with manually defined contours in the majority of cases. The ability to rapidly segment the liver with high accuracy achieved in this investigation has the potential to enable the efficient integration of biomechanical model-based registration into a clinical workflow.
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影响因子：
3.8
作者：
Velec M;Moseley JL;Svensson S;Hårdemark B;Jaffray DA;Brock KK
通讯作者：
Brock KK
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影响因子：
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作者：
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作者：
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作者：
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通讯作者：
de Bruijne, Marleen
影响因子：
3.8
作者：
Zhou, Xiangrong;Takayama, Ryosuke;Fujita, Hiroshi
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