Automated quantification of COVID-19 severity and progression using chest CT images.
Automated quantification of COVID-19 severity and progression using chest CT images.
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DOI：
10.1007/s00330-020-07156-2
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发表时间：
2021-01
影响因子：
5.9
通讯作者：
Jin C
中科院分区：
文献类型：
--
作者：
Pu J;Leader JK;Bandos A;Ke S;Wang J;Shi J;Du P;Guo Y;Wenzel SE;Fuhrman CR;Wilson DO;Sciurba FC;Jin C
开发和测试计算机软件，使用胸部CT扫描来检测、量化和监测与新冠肺炎相关的肺炎的进展。120例肺部浸润性病变患者的胸部CT扫描被用于训练深度学习算法以分割肺区域和血管。来自24名新冠肺炎受试者的72次序列扫描被用于开发和测试算法，以检测和量化与新冠肺炎相关的浸润物的存在和进展。该算法包括(1)自动肺边界和血管分割，(2)连续扫描之间肺边界的配准，(3)肺炎区域的计算机识别，以及(4)疾病进展的评估。使用Dice系数评估放射科医生手动勾画的区域和计算机检测的区域之间的一致性。序列扫描被登记并用于生成可视化扫描之间的变化的热图。两位放射科医生使用5分Likert分级法，主观地评价了热图在表示进展方面的准确性。计算机检测与人工勾画肺炎区的符合率较高，Dice系数为81%(CI为76~86%)。在检测大型肺炎区域(>； 200亿mm~3)时，该算法的敏感性为95%(CI 94-97%)，特异性为84%(CI 81-86%)。放射科医生将95%(CI 72至99)的热图评定为表示疾病进展至少是“可接受的”。初步结果表明，使用计算机软件检测和量化与新冠肺炎相关的肺炎区域，并生成可用于可视化和评估进展的热图是可行的。·利用计算机视觉和深度学习技术开发计算机软件，以量化CT图像上描绘的与新冠肺炎相关的肺炎的存在和进展。·使用定量实验和主观评估对计算机软件进行了测试。·该计算机软件有可能帮助检测肺炎区域，监测疾病进展，并评估与新冠肺炎相关的治疗疗效。本文的在线版本(10.1007/s00330-020-07156-2)包含补充材料，可供授权用户使用。
To develop and test computer software to detect, quantify, and monitor progression of pneumonia associated with COVID-19 using chest CT scans. One hundred twenty chest CT scans from subjects with lung infiltrates were used for training deep learning algorithms to segment lung regions and vessels. Seventy-two serial scans from 24 COVID-19 subjects were used to develop and test algorithms to detect and quantify the presence and progression of infiltrates associated with COVID-19. The algorithm included (1) automated lung boundary and vessel segmentation, (2) registration of the lung boundary between serial scans, (3) computerized identification of the pneumonitis regions, and (4) assessment of disease progression. Agreement between radiologist manually delineated regions and computer-detected regions was assessed using the Dice coefficient. Serial scans were registered and used to generate a heatmap visualizing the change between scans. Two radiologists, using a five-point Likert scale, subjectively rated heatmap accuracy in representing progression. There was strong agreement between computer detection and the manual delineation of pneumonic regions with a Dice coefficient of 81% (CI 76–86%). In detecting large pneumonia regions (> 200 mm3), the algorithm had a sensitivity of 95% (CI 94–97%) and specificity of 84% (CI 81–86%). Radiologists rated 95% (CI 72 to 99) of heatmaps at least “acceptable” for representing disease progression. The preliminary results suggested the feasibility of using computer software to detect and quantify pneumonic regions associated with COVID-19 and to generate heatmaps that can be used to visualize and assess progression. • Both computer vision and deep learning technology were used to develop computer software to quantify the presence and progression of pneumonia associated with COVID-19 depicted on CT images. • The computer software was tested using both quantitative experiments and subjective assessment. • The computer software has the potential to assist in the detection of the pneumonic regions, monitor disease progression, and assess treatment efficacy related to COVID-19. The online version of this article (10.1007/s00330-020-07156-2) contains supplementary material, which is available to authorized users.
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DOI：
10.1109/tvcg.2010.56
发表时间：
2011-01
影响因子：
5.2
作者：
Pu J;Paik DS;Meng X;Roos JE;Rubin GD
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