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首页> 外文会议>Image Processing pt.3; Progress in Biomedical Optics and Imaging; vol.8,no.31; Proceedings of SPIE-The International Society for Optical Engineering; vol.6512 pt.3 >Automated segmentation of mammary gland regions in non-contrast torso CT images based on probabilistic atlas
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Automated segmentation of mammary gland regions in non-contrast torso CT images based on probabilistic atlas

机译：基于概率图谱的非对比躯干CT图像中的乳腺区域自动分割

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摘要

The identification of mammary gland regions is a necessary processing step during the anatomical structure recognition of human body and can be expected to provide the useful information for breast tumor diagnosis. This paper proposes a fully-automated scheme for segmenting the mammary gland regions in non-contrast torso CT images. This scheme calculates the probability for each voxel belonging to the mammary gland or other regions (for example pectoralis major muscles) in CT images and decides the mammary gland regions automatically. The probability is estimated from the location of the mammary gland and pectoralis major muscles in CT images. The location (named as a probabilistic atlas) is investigated from the pre-segmentation results in a number of different CT scans and the CT number distribution is approximated using a Gaussian function. We applied this scheme to 66 patient cases (female, age: 40-80) and evaluated the accuracy by using the coincidence rate between the segmented result and gold standard that is generated manually by a radiologist for each CT case. The mean value of the coincidence rate was 0.82 with the standard deviation of 0.09 for 66 CT cases.
机译：乳腺区域的识别是人体解剖结构识别过程中的必要步骤，有望为乳腺肿瘤的诊断提供有用的信息。本文提出了一种自动方案，用于对非对比CT图像中的乳腺区域进行分割。此方案可计算CT图像中属于乳腺或其他区域（例如胸大肌）的每个体素的概率，并自动确定乳腺区域。根据CT图像中乳腺和胸大肌的位置估计概率。根据分段前的结果对位置（称为概率图集）进行了许多不同的CT扫描，并使用高斯函数对CT数量分布进行了近似。我们将该方案应用于66例患者（女性，年龄：40-80），并使用分割结果与放射线医师为每个CT病例手动生成的金标准之间的符合率来评估准确性。 66例CT病例的符合率平均值为0.82，标准偏差为0.09。

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