Fully Automatic Volume Measurement of the Spleen at CT Using Deep Learning
Fully Automatic Volume Measurement of the Spleen at CT Using Deep Learning
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DOI：
10.1148/ryai.2020190102
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发表时间：
2020-07-01
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影响因子：
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通讯作者：
Jacobs, Colin
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文献类型：
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作者：
Humpire-Mamani, Gabriel E.;Bukala, Joris;Jacobs, Colin
目的：开发一种完全自动化的脾脏分割算法，并在大型数据集中评估该算法的性能。材料和方法：在这项回顾性研究中，我们开发了一个三维深度学习网络来分割胸腹CT扫描上的脾脏。从2014年至2017年接受肿瘤治疗的患者中提取扫描结果。本研究共使用了1100名患者的1100次扫描，其中400次被选中用于开发算法。为了进行测试，对50个扫描数据集进行注释以评估分割准确性，并与脾指数方程进行比较。在一项定性观察实验中，使用了一组丰富的100对扫描对来评估该算法是否可以帮助放射科医生评估脾体积变化。参考标准是由另外两位独立的放射科医生达成共识制定的。采用Mann-Whitney U检验来检验算法与独立观察者之间是否存在性能差异。结果：算法与独立观察者在50次扫描的测试集上获得了相当的Dice分数（P=.834），分别为0.962和0.964。在对体积变化进行视觉分类后，放射科医生与参考标准的一致性达到81%(100分之81)，在算法的帮助下，这一比例增加到92%（100分之92）。结论：基于深度学习的脾脏分割方法可以在CT扫描上准确分割脾脏，有助于放射科医生发现脾脏异常体积和脾脏体积变化。(c) rsna, 2020
Purpose: To develop a fully automated algorithm for spleen segmentation and to assess the performance of this algorithm in a large dataset.Materials and Methods: In this retrospective study, a three-dimensional deep learning network was developed to segment the spleen on thorax-abdomen CT scans. Scans were extracted from patients undergoing oncologic treatment from 2014 to 2017. A total of 1100 scans from 1100 patients were used in this study, and 400 were selected for development of the algorithm. For testing, a dataset of 50 scans was annotated to assess the segmentation accuracy and was compared against the splenic index equation. In a qualitative observer experiment, an enriched set of 100 scan-pairs was used to evaluate whether the algorithm could aid a radiologist in assessing splenic volume change. The reference standard was set by the consensus of two other independent radiologists. A Mann-Whitney U test was conducted to test whether there was a performance difference between the algorithm and the independent observer.Results: The algorithm and the independent observer obtained comparable Dice scores (P=.834) on the test set of 50 scans of 0.962 and 0.964, respectively. The radiologist had an agreement with the reference standard in 81% (81 of 100) of the cases after a visual classification of volume change, which increased to 92% (92 of 100) when aided by the algorithm.Conclusion: A segmentation method based on deep learning can accurately segment the spleen on CT scans and may help radiologists to detect abnormal splenic volumes and splenic volume changes. (C) RSNA, 2020