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首页> 外文会议>SIBGRAPI Conference on Graphics, Patterns and Images >Interactive Medical Image Segmentation by Statistical Seed Models
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Interactive Medical Image Segmentation by Statistical Seed Models

机译：统计种子模型的交互式医学图像分割

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Interactive 3D object segmentation is an important and challenging activity in medical imaging, although it is tedious and error-prone to be done. Automatic segmentation methods aim to replace the user altogether, but require user interaction to produce training data sets of segmented masks and to make error corrections. We propose a complete framework for interactive medical image segmentation, which reduces user effort by automatically providing an initial segmentation result. We develop a Statistical Seed Model (SSM) to this end, that improves from seed sets selected by robot users when reconstructing masks of previously segmented images. The SSM outputs a seed set that may be used to automatically delineate a new test image. The seeds provide both an implicit object shape constraint and a flexible way of interactively correcting segmentation. We demonstrate that our framework decreases the amount of user interaction by a factor of three, when segmenting MR-images of the cerebellum.
机译：交互式3D对象分割是医学成像中一项重要且具有挑战性的活动，尽管它很繁琐且容易出错。自动分割方法旨在完全取代用户，但需要用户交互才能生成分割蒙版的训练数据集并进行错误校正。我们提出了用于交互式医学图像分割的完整框架，该框架通过自动提供初始分割结果来减少用户的工作量。为此，我们开发了一种统计种子模型（SSM），在重建先前分割的图像的蒙版时，它会从机器人用户选择的种子集中得到改进。 SSM输出可用于自动描绘新测试图像的种子集。种子提供了隐式的对象形状约束和交互式地校正分段的灵活方式。我们证明了，当分割小脑的MR图像时，我们的框架将用户交互的量减少了三倍。

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