This paper presents a generic probabilistic framework for estimating the statistical dependency and finding the anatomical correspondences among an arbitrary number of medical images. The method builds on a novel formulation of the $N$-dimensional joint intensity distribution by representing the common anatomy as latent variables and estimating the appearance model with nonparametric estimators. Through connection to maximum likelihood and the expectation-maximization algorithm, an information\hyp{}theoretic metric called $\mathcal{X}$-metric and a co-registration algorithm named $\mathcal{X}$-CoReg are induced, allowing groupwise registration of the $N$ observed images with computational complexity of $\mathcal{O}(N)$. Moreover, the method naturally extends for a weakly-supervised scenario where anatomical labels of certain images are provided. This leads to a combined\hyp{}computing framework implemented with deep learning, which performs registration and segmentation simultaneously and collaboratively in an end-to-end fashion. Extensive experiments were conducted to demonstrate the versatility and applicability of our model, including multimodal groupwise registration, motion correction for dynamic contrast enhanced magnetic resonance images, and deep combined computing for multimodal medical images. Results show the superiority of our method in various applications in terms of both accuracy and efficiency, highlighting the advantage of the proposed representation of the imaging process.
翻译:本文为估算统计依赖性和在任意数量的医用图像中找到解剖对应数据提供了一个通用的概率框架。该方法基于将普通解剖值作为潜在变量并用非参数估测器估算外观模型。通过与最大可能性和预期最大化算法的联系,信息\hp ⁇ theoretic 度量算法($\mathcal{X}美元)和名为$\mathcal{X}-CoReg的共同注册算法($=mathcal{X}-CoReg)的诱发,使美元观测到的图像以计算复杂度($\mathcal{O}(N)美元)的方式分组登记。此外,该方法自然延伸至于一种薄弱的超常性情景,即提供某些图像的解剖面标签。这导致在深层次学习中实施一个同时进行登记和分解并合作使用美元-Cog-Regtal 的混合注册算法。进行了广泛的实验,以展示我们模型的多式图像的多变图像的易易性和适用性,包括多式图像的模型的模型的模型的模型的模拟模拟模拟模拟模拟模拟化,同时展示。