基于贝叶斯标志的肿瘤病理图像形状分析
Bayesian Landmark-based Shape Analysis of Tumor Pathology Images
摘要：
医学成像是一种技术形式，在过去的一个世纪里彻底改变了医学领域。除了肿瘤组织的放射学成像外，以高空间分辨率捕获组织学细节的数字病理学成像正迅速成为癌症诊断支持和治疗计划的常规临床程序。深度学习方法的最新发展有助于从数字病理图像中几乎在细胞水平上分割肿瘤区域。在放射学中开发用于表征肿瘤边界粗糙度的传统形状特征不适用。迫切需要可靠的统计方法来建模病理图像中的肿瘤形状。在本文中，我们考虑的问题建模的肿瘤边界与一个封闭的多边形链。提出了一种基于贝叶斯标志的形状分析（BayesLASA）模型，将多边形链划分为互斥的段，以分段量化边界粗糙度。我们的完全贝叶斯推断框架提供了地标的数量和位置的不确定性估计。贝叶斯LASA优于最近开发的平面弹性曲线的地标检测模型的准确性和效率。我们通过对143名非小细胞肺癌患者的246张病理图像的案例研究，展示了这种基于模型的分析如何比普通方法更清晰地推断。病例研究表明，肿瘤边界粗糙度的异质性预测患者预后（p值< 0.001）。这种统计方法学不仅提出了一种新的模型，通过使用其标志来表征数字化对象的形状特征，而且还提供了一个新的视角来理解肿瘤表面在癌症进展中的作用。
英文摘要：
Medical imaging is a form of technology that has revolutionized the medical field in the past century. In addition to radiology imaging of tumor tissues, digital pathology imaging, which captures histological details in high spatial resolution, is fast becoming a routine clinical procedure for cancer diagnosis support and treatment planning. Recent developments in deep-learning methods facilitate the segmentation of tumor regions at almost the cellular level from digital pathology images. The traditional shape features that were developed for characterizing tumor boundary roughness in radiology are not applicable. Reliable statistical approaches to modeling tumor shape in pathology images are in urgent need. In this paper, we consider the problem of modeling a tumor boundary with a closed polygonal chain. A Bayesian landmark-based shape analysis (BayesLASA) model is proposed to partition the polygonal chain into mutually exclusive segments to quantify the boundary roughness piecewise. Our fully Bayesian inference framework provides uncertainty estimates of both the number and locations of landmarks. The BayesLASA outperforms a recently developed landmark detection model for planar elastic curves in terms of accuracy and efficiency. We demonstrate how this model-based analysis can lead to sharper inferences than ordinary approaches through a case study on the 246 pathology images from 143 non-small cell lung cancer patients. The case study shows that the heterogeneity of tumor boundary roughness predicts patient prognosis (p-value < 0.001). This statistical methodology not only presents a new model for characterizing a digitized object's shape features by using its landmarks, but also provides a new perspective for understanding the role of tumor surface in cancer progression.
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