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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 >Signaling Local Non-credibility in an Automatic Segmentation Pipeline
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Signaling Local Non-credibility in an Automatic Segmentation Pipeline

机译：在自动分段管道中用信号通知本地非可信度

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摘要

The advancing technology for automatic segmentation of medical images should be accompanied by techniques to inform the user of the local credibility of results. To the extent that this technology produces clinically acceptable segmentations for a significant fraction of cases, there is a risk that the clinician will assume every result is acceptable. In the less frequent case where segmentation fails, we are concerned that unless the user is alerted by the computer, she would still put the result to clinical use. By alerting the user to the location of a likely segmentation failure, we allow her to apply limited validation and editing resources where they are most needed. We propose an automated method to signal suspected non-credible regions of the segmentation, triggered by statistical outliers of the local image match function. We apply this test to m-rep segmentations of the bladder and prostate in CT images using a local image match computed by PCA on regional intensity quantile functions.We validate these results by correlating the non-credible regions with regions that have surface distance greater than 5.5mm to a reference segmentation for the bladder. A 6mm surface distance was used to validate the prostate results. Varying the outlier threshold level produced a receiver operating characteristic with area under the curve of 0.89 for the bladder and 0.92 for the prostate. Based on this preliminary result, our method has been able to predict local segmentation failures and shows potential for validation in an automatic segmentation pipeline.
机译：用于自动分割医学图像的先进技术应伴随着一些技术，以告知用户结果的本地可信度。在一定程度上，该技术会在相当多的情况下产生临床上可接受的细分，因此临床医生可能会认为所有结果都是可以接受的。在分割失败的频率较低的情况下，我们担心除非用户通过计算机发出警报，否则她仍会将结果用于临床。通过提醒用户可能发生细分失败的位置，我们允许她在最需要的地方应用有限的验证和编辑资源。我们提出了一种自动方法来发出可疑的非可信区域分割信号，该信号由局部图像匹配函数的统计异常值触发。我们使用PCA在区域强度分位数函数上计算出的局部图像匹配结果，将该测试应用于CT图像中膀胱和前列腺的m-rep分割。我们通过将非可信赖区域与表面距离大于5.5mm至膀胱参考分割。 6mm的表面距离用于验证前列腺结果。改变离群值阈值水平会产生接收器工作特性，其曲线下的面积对于膀胱为0.89，对于前列腺为0.92。基于此初步结果，我们的方法已经能够预测局部分割失败，并显示了在自动分割管道中进行验证的潜力。

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