Department of Radiology, Seoul National University Hospital, Seoul, Korea.
Department of Radiology, Seoul National University College of Medicine, Seoul, Korea.
Eur Radiol. 2023 Jun;33(6):4292-4302. doi: 10.1007/s00330-022-09347-5. Epub 2022 Dec 26.
OBJECTIVES: To develop a fully automated deep learning model for adrenal segmentation and to evaluate its performance in classifying adrenal hyperplasia. METHODS: This retrospective study evaluated automated adrenal segmentation in 308 abdominal CT scans from 48 patients with adrenal hyperplasia and 260 patients with normal glands from 2010 to 2021 (mean age, 42 years; 156 women). The dataset was split into training, validation, and test sets at a ratio of 6:2:2. Contrast-enhanced CT images and manually drawn adrenal gland masks were used to develop a U-Net-based segmentation model. Predicted adrenal volumes were obtained by fivefold splitting of the dataset without overlapping the test set. Adrenal volumes and anthropometric parameters (height, weight, and sex) were utilized to develop an algorithm to classify adrenal hyperplasia, using multilayer perceptron, support vector classification, a random forest classifier, and a decision tree classifier. To measure the performance of the developed model, the dice coefficient and intraclass correlation coefficient (ICC) were used for segmentation, and area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were used for classification. RESULTS: The model for segmenting adrenal glands achieved a Dice coefficient of 0.7009 for 308 cases and an ICC of 0.91 (95% CI, 0.90-0.93) for adrenal volume. The models for classifying hyperplasia had the following results: AUC, 0.98-0.99; accuracy, 0.948-0.961; sensitivity, 0.750-0.813; and specificity, 0.973-1.000. CONCLUSION: The proposed segmentation algorithm can accurately segment the adrenal glands on CT scans and may help clinicians identify possible cases of adrenal hyperplasia. KEY POINTS: • A deep learning segmentation method can accurately segment the adrenal gland, which is a small organ, on CT scans. • The machine learning algorithm to classify adrenal hyperplasia using adrenal volume and anthropometric parameters (height, weight, and sex) showed good performance. • The proposed segmentation algorithm may help clinicians identify possible cases of adrenal hyperplasia.
目的：开发一种完全自动化的深度学习模型，用于肾上腺分割，并评估其在肾上腺增生分类中的性能。
方法：这项回顾性研究评估了 2010 年至 2021 年间 48 例肾上腺增生患者和 260 例正常腺体患者的 308 例腹部 CT 扫描的自动肾上腺分割。数据集按 6:2:2 的比例分为训练集、验证集和测试集。使用增强 CT 图像和手动绘制的肾上腺掩模来开发基于 U-Net 的分割模型。通过不重叠测试集的数据集五折分割来获得预测的肾上腺体积。使用多层感知器、支持向量分类、随机森林分类器和决策树分类器，利用肾上腺体积和人体测量参数（身高、体重和性别）来开发一种分类肾上腺增生的算法。为了衡量开发模型的性能，使用 Dice 系数和组内相关系数（ICC）进行分割，使用接收者操作特征曲线下的面积（AUC）、准确性、敏感性和特异性进行分类。
结果：该模型对 308 例病例的肾上腺分割获得了 0.7009 的 Dice 系数和 0.91（95%CI，0.90-0.93）的 ICC。用于分类增生的模型结果如下：AUC，0.98-0.99；准确性，0.948-0.961；敏感性，0.750-0.813；特异性，0.973-1.000。
结论：提出的分割算法可以准确地对 CT 扫描上的肾上腺进行分割，这有助于临床医生识别可能的肾上腺增生病例。
要点：
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