A Marked Poisson Process Driven Latent Shape Model for 3D Segmentation of Reflectance Confocal Microscopy Image Stacks of Human Skin.
A Marked Poisson Process Driven Latent Shape Model for 3D Segmentation of Reflectance Confocal Microscopy Image Stacks of Human Skin.
复制标题
一个明显的泊松过程驱动的潜在模型，用于3D反射率共聚焦显微镜图像堆的3D分割。
DOI：
10.1109/tip.2016.2615291
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发表时间：
2017-01
期刊：
影响因子：
--
通讯作者：
Dy JG
中科院分区：
文献类型：
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作者：
Ghanta S;Jordan MI;Kose K;Brooks DH;Rajadhyaksha M;Dy JG
从 3D 数据集中分割感兴趣的对象是生物数据中遇到的常见问题。小视场和固有的生物变异性，加上图像中强度、分辨率和低对比度的光学细微变化，使得分割任务变得困难，特别是对于未染色的活体或新切除的厚组织的显微镜检查。除了感兴趣对象的外观之外，结合形状信息通常有助于提高分割性能。然而，组织中物体的形状可能变化很大，设计包含这些变化的灵活形状模型具有挑战性。为了解决此类复杂的分割问题，我们提出了一个统一的概率框架，该框架可以纳入与复杂形状、可变外观和未知位置相关的不确定性。激发该框架开发的驱动应用是一个生物学上重要的分割问题：在人体皮肤的 3D 反射共焦显微镜 (RCM) 图像中自动检测和分割真皮表皮交界处 (DEJ) 的任务。 RCM 成像可以无创地观察细胞、核和形态细节。 DEJ 是一个重要的形态学特征，因为它是紊乱、疾病和癌症通常开始的地方。检测 DEJ 具有挑战性，因为它是 3D 体积中的 2D 表面，具有强烈但数量高度可变的不规则间隔和形状各异的“峰和谷”。此外，RCM 成像分辨率、对比度和强度随深度而变化。因此，先前的模型需要结合内在结构，同时允许基本上所有参数的可变性。我们提出了一种模型，该模型可以利用领域知识构建适当的模型先验，在无监督的环境中将具有复杂形状和可变外观的感兴趣对象结合起来。我们对这种结构进行建模的新颖策略将空间泊松过程与形状先验相结合，并使用吉布斯采样进行推理。实验结果表明，所提出的无监督模型能够自动检测 DEJ，其生理相关精度在 10 – 20μm 范围内。
Segmenting objects of interest from 3D datasets is a common problem encountered in biological data. Small field of view and intrinsic biological variability combined with optically subtle changes of intensity, resolution and low contrast in images make the task of segmentation difficult, especially for microscopy of unstained living or freshly excised thick tissues. Incorporating shape information in addition to the appearance of the object of interest can often help improve segmentation performance. However, shapes of objects in tissue can be highly variable and design of a flexible shape model that encompasses these variations is challenging. To address such complex segmentation problems, we propose a unified probabilistic framework that can incorporate the uncertainty associated with complex shapes, variable appearance and unknown locations. The driving application which inspired the development of this framework is a biologically important segmentation problem: the task of automatically detecting and segmenting the dermal-epidermal junction (DEJ) in 3D reflectance confocal microscopy (RCM) images of human skin. RCM imaging allows noninvasive observation of cellular, nuclear and morphological detail. The DEJ is an important morphological feature as it is where disorder, disease and cancer usually start. Detecting the DEJ is challenging because it is a 2D surface in a 3D volume which has strong but highly variable number of irregularly spaced and variably shaped “peaks and valleys”. In addition, RCM imaging resolution, contrast and intensity vary with depth. Thus a prior model needs to incorporate the intrinsic structure while allowing variability in essentially all its parameters. We propose a model which can incorporate objects of interest with complex shapes and variable appearance in an unsupervised setting by utilizing domain knowledge to build appropriate priors of the model. Our novel strategy to model this structure combines a spatial Poisson process with shape priors and performs inference using Gibbs sampling. Experimental results show that the proposed unsupervised model is able to automatically detect the DEJ with physiologically relevant accuracy in the range 10 – 20µm.