Department of Electrical Engineering, University of Maryland Baltimore County, 1000 Hilltop Circle, Baltimore, MD, 21250, USA,
J Digit Imaging. 2013 Oct;26(5):891-7. doi: 10.1007/s10278-012-9554-7.
Adrenal abnormalities are commonly identified on computed tomography (CT) and are seen in at least 5 % of CT examinations of the thorax and abdomen. Previous studies have suggested that evaluation of Hounsfield units within a region of interest or a histogram analysis of a region of interest can be used to determine the likelihood that an adrenal gland is abnormal. However, the selection of a region of interest can be arbitrary and operator dependent. We hypothesize that segmenting the entire adrenal gland automatically without any human intervention and then performing a histogram analysis can accurately detect adrenal abnormality. We use the random forest classification framework to automatically perform a pixel-wise classification of an entire CT volume (abdomen and pelvis) into three classes namely right adrenal, left adrenal, and background. Once we obtain this classification, we perform histogram analysis to detect adrenal abnormality. The combination of these methods resulted in a sensitivity and specificity of 80 and 90 %, respectively, when analyzing 20 adrenal glands seen on volumetric CT datasets for abnormality.
肾上腺异常在计算机断层扫描（CT）中很常见，至少在胸部和腹部 CT 检查的 5%中可见。先前的研究表明，评估感兴趣区域内的 Hounsfield 单位或感兴趣区域的直方图分析可用于确定肾上腺异常的可能性。然而，感兴趣区域的选择可能是任意的，并且取决于操作人员。我们假设，无需任何人工干预即可自动对整个肾上腺进行分割，然后进行直方图分析，可以准确检测肾上腺异常。我们使用随机森林分类框架自动对整个 CT 容积（腹部和骨盆）进行逐像素分类，分为右肾上腺、左肾上腺和背景三个类别。一旦获得这种分类，我们就进行直方图分析以检测肾上腺异常。当分析 20 个容积 CT 数据集的异常肾上腺时，这些方法的组合分别实现了 80%和 90%的灵敏度和特异性。
