Mostapha Mahmoud, Vicory Jared, Styner Martin, Pizer Stephen
Department of Computer Science, University of North Carolina at Chapel Hill, USA.
Department of Psychiatry, University of North Carolina at Chapel Hill, USA.
Proc SPIE Int Soc Opt Eng. 2017;10133. doi: 10.1117/12.2250023. Epub 2017 Feb 24.
Segmentation is a key task in medical image analysis because its accuracy significantly affects successive steps. Automatic segmentation methods often produce inadequate segmentations, which require the user to manually edit the produced segmentation slice by slice. Because editing is time-consuming, an editing tool that enables the user to produce accurate segmentations by only drawing a sparse set of contours would be needed. This paper describes such a framework as applied to a single object. Constrained by the additional information enabled by the manually segmented contours, the proposed framework utilizes object shape statistics to transform the failed automatic segmentation to a more accurate version. Instead of modeling the object shape, the proposed framework utilizes shape change statistics that were generated to capture the object deformation from the failed automatic segmentation to its corresponding correct segmentation. An optimization procedure was used to minimize an energy function that consists of two terms, an external contour match term and an internal shape change regularity term. The high accuracy of the proposed segmentation editing approach was confirmed by testing it on a simulated data set based on 10 infant magnetic resonance brain data sets using four similarity metrics. Segmentation results indicated that our method can provide efficient and adequately accurate segmentations (Dice segmentation accuracy increase of 10%), with very sparse contours (only 10%), which is promising in greatly decreasing the work expected from the user.
分割是医学图像分析中的一项关键任务，因为其准确性会显著影响后续步骤。自动分割方法往往会产生不充分的分割结果，这就需要用户逐片手动编辑生成的分割结果。由于编辑过程耗时，因此需要一种编辑工具，使用户仅通过绘制一组稀疏的轮廓就能生成准确的分割结果。本文描述了这样一个应用于单个对象的框架。在手动分割轮廓所提供的附加信息的约束下，所提出的框架利用对象形状统计信息将失败的自动分割转换为更准确的版本。所提出的框架不是对对象形状进行建模，而是利用为捕捉从失败的自动分割到其相应正确分割的对象变形而生成的形状变化统计信息。使用了一种优化程序来最小化一个能量函数，该能量函数由两项组成，一项是外部轮廓匹配项，另一项是内部形状变化规律性项。通过在基于10个婴儿磁共振脑数据集的模拟数据集上使用四种相似性度量对所提出的分割编辑方法进行测试，证实了其高准确性。分割结果表明，我们的方法能够提供高效且足够准确的分割结果（骰子分割准确率提高10%），且轮廓非常稀疏（仅10%），这有望大大减少用户预期的工作量。
