AUTOMATED LEVEL SET SEGMENTATION OF HISTOPATHOLOGIC CELLS WITH SPARSE SHAPE PRIOR SUPPORT AND DYNAMIC OCCLUSION CONSTRAINT.
AUTOMATED LEVEL SET SEGMENTATION OF HISTOPATHOLOGIC CELLS WITH SPARSE SHAPE PRIOR SUPPORT AND DYNAMIC OCCLUSION CONSTRAINT.
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DOI：
10.1109/isbi.2017.7950620
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发表时间：
2017-04
期刊：
影响因子：
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通讯作者：
Kong J
中科院分区：
文献类型：
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作者：
Zhang P;Wang F;Teodoro G;Liang Y;Brat D;Kong J
本文提出了一种基于稀疏形状先验指导的变分水平集框架的病理组织图像细胞分割方法。我们通过检测种子来自动化细胞轮廓初始化，并通过最小化新的能量泛函来变形轮廓，该泛函包含涉及稀疏形状先验的形状项、自适应轮廓遮挡惩罚项和鼓励轮廓收敛到强边缘的边界项。因此，我们的方法能够适应相互遮挡并检测多个相交细胞的轮廓。我们将我们的算法应用于一组脑肿瘤切片的全切片组织病理学图像。所提出的方法与其他流行的方法相比，并表现出良好的准确性，细胞分割的定量措施，这表明其承诺，以支持生物医学图像为基础的调查。
In this paper, we propose a novel segmentation method for cells in histopathologic images based on a sparse shape prior guided variational level set framework. We automate the cell contour initialization by detecting seeds and deform contours by minimizing a new energy functional that incorporates a shape term involving sparse shape priors, an adaptive contour occlusion penalty term, and a boundary term encouraging contours to converge to strong edges. As a result, our approach is able to accommodate mutual occlusions and detect contours of multiple intersected cells. We apply our algorithm to a set of whole-slide histopathologic images of brain tumor sections. The proposed method is compared with other popular methods, and demonstrates good accuracy for cell segmentation by quantitative measures, suggesting its promise to support biomedical image-based investigations.