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首页> 外文会议>Conference on Biomedical Applications in Molecular, Structural, and Functional Imaging >Development of a Semi-Automated Combined PET and CT Lung Lesion Segmentation Framework
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Development of a Semi-Automated Combined PET and CT Lung Lesion Segmentation Framework

机译：开发半自动组合PET和CT肺病灶分割框架

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Segmentation is one of the most important steps in automated medical diagnosis applications, which affects the accuracy of the overall system. In this paper, we propose a semi-automated segmentation method for extracting lung lesions from thoracic PET/CT images by combining low level processing and active contour techniques. The lesions are first segmented in PET images which are first converted to standardised uptake values (SUVs). The segmented PET images then serve as an initial contour for subsequent active contour segmentation of corresponding CT images. To evaluate its accuracy, the Jaccard Index (JI) was used as a measure of the accuracy of the segmented lesion compared to alternative segmentations from the QIN lung CT segmentation challenge, which is possible by registering the whole body PET/CT images to the corresponding thoracic CT images. The results show that our proposed technique has acceptable accuracy in lung lesion segmentation with JI values of around 0.8, especially when considering the variability of the alternative segmentations.
机译：分割是自动化医学诊断应用中最重要的步骤之一，这影响了整个系统的准确性。在本文中，我们提出了一种半自动分段方法，通过组合低级处理和主动轮廓技术来提取来自胸腔PET / CT图像的肺病变。首先在PET图像中分段的病变首先将其转换成标准化摄取值（SUV）。然后，分段的PET图像用作用于随后的相应CT图像的活动轮廓分割的初始轮廓。为了评估其准确性，与秦肺CT分割挑战的替代分割相比，Jaccard指数（JI）用作分段病变的准确性的量度，这是通过将整个身体PET / CT图像注册到对应的胸CT图像。结果表明，我们所提出的技术在肺病灶分割中具有可接受的准确性，JI值约为0.8，特别是在考虑替代分割的可变性时。

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