Expectation-Maximization-Driven Geodesic Active Contour With Overlap Resolution (EMaGACOR): Application to Lymphocyte Segmentation on Breast Cancer Histopathology
Expectation-Maximization-Driven Geodesic Active Contour With Overlap Resolution (EMaGACOR): Application to Lymphocyte Segmentation on Breast Cancer Histopathology
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DOI：
10.1109/tbme.2010.2041232
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发表时间：
2010-07-01
影响因子：
4.6
通讯作者：
Madabhushi, Anant
中科院分区：
文献类型：
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作者：
Fatakdawala, Hussain;Xu, Jun;Madabhushi, Anant
淋巴细胞浸润 (LI) 的存在与 HER2+ 乳腺癌 (BC) 的淋巴结转移和肿瘤复发相关。自动检测和量化组织病理学图像上 LI 程度的能力可能会导致开发基于图像的人类表皮生长因子受体 2 (HER2+) BC 患者的预后工具。苏木精和伊红 (H&E) 染色的 BC 组织病理学图像中的淋巴细胞分割由于图像中淋巴细胞核和其他结构（例如癌核）之间的外观相似而变得复杂。其他挑战包括生物变异性、组织学伪影和重叠物体的高发生率。尽管活动轮廓在图像分割中被广泛采用，但它们分割重叠对象的能力有限并且对初始化敏感。在本文中，我们提出了一种新的分割方案，即具有重叠分辨率的期望最大化（EM）驱动的测地活动轮廓（EMaGACOR），我们将其应用于自动检测和分割 HER2+ BC 组织病理学图像上的淋巴细胞。 EMaGACOR 利用期望最大化算法自动初始化测地活动轮廓 (GAC)，并包括一种基于启发式分割轮廓的新颖方案，通过识别高凹点来解决重叠结构。 EMaGACOR 对总共 100 个 HER2+ 乳腺活检组织学图像进行了评估，发现检测灵敏度超过 86%，阳性预测值超过 64%。相比之下，EMaGAC 模型（无重叠分辨率）和 GAC 模型分别产生了 42% 和 19% 的相应检测灵敏度。此外，EMaGACOR 能够正确解决交叉淋巴细胞之间 90% 以上的重叠问题。 EMaGACOR 的豪斯多夫距离 (HD) 和平均绝对距离 (MAD) 分别为 2.1 和 0.9 像素，与 EMaGAC 和 GAC 模型的相应性能相比明显更好。 EMaGACOR 是一种高效、稳健、可重复且准确的分割技术，有可能应用于其他生物医学图像分析问题。
The presence of lymphocytic infiltration (LI) has been correlated with nodal metastasis and tumor recurrence in HER2+ breast cancer (BC). The ability to automatically detect and quantify extent of LI on histopathology imagery could potentially result in the development of an image based prognostic tool for human epidermal growth factor receptor-2 (HER2+) BC patients. Lymphocyte segmentation in hematoxylin and eosin (H&E) stained BC histopathology images is complicated by the similarity in appearance between lymphocyte nuclei and other structures (e. g., cancer nuclei) in the image. Additional challenges include biological variability, histological artifacts, and high prevalence of overlapping objects. Although active contours are widely employed in image segmentation, they are limited in their ability to segment overlapping objects and are sensitive to initialization. In this paper, we present a new segmentation scheme, expectation-maximization (EM) driven geodesic active contour with overlap resolution (EMaGACOR), which we apply to automatically detecting and segmenting lymphocytes on HER2+ BC histopathology images. EMaGACOR utilizes the expectation-maximization algorithm for automatically initializing a geodesic active contour (GAC) and includes a novel scheme based on heuristic splitting of contours via identification of high concavity points for resolving overlapping structures. EMaGACOR was evaluated on a total of 100 HER2+ breast biopsy histology images and was found to have a detection sensitivity of over 86% and a positive predictive value of over 64%. By comparison, the EMaGAC model (without overlap resolution) and GAC model yielded corresponding detection sensitivities of 42% and 19%, respectively. Furthermore, EMaGACOR was able to correctly resolve over 90% of overlaps between intersecting lymphocytes. Hausdorff distance (HD) and mean absolute distance (MAD) for EMaGACOR were found to be 2.1 and 0.9 pixels, respectively, and significantly better compared to the corresponding performance of the EMaGAC and GAC models. EMaGACOR is an efficient, robust, reproducible, and accurate segmentation technique that could potentially be applied to other biomedical image analysis problems.