This paper proposes a high level latent and shared feature representation from neuroimaging modalities (MRI and PET) via deep learning for the diagnosis of Alzheimer’s Disease (AD) and its prodromal stage, Mild Cognitive Impairment (MCI). In contrast to the previous works where the multimodal features were combined by concatenating into long vectors or transforming into a high dimensional kernel space, the authors propose using a Deep Boltzmann Machine (DBM) to find a latent hierarchical representation from a 3D patch, and then come up with a method for “a joint feature representation from the paired patches of MRI and PET with a multimodal DBM.” ...

This paper proposes an end-to-end trained fully convolutional neural network model to process 3D image volumes. Unlike previous works that processed the input volumes slice-wise or patch-wise, the authors propose to use volumetric convolutions. Moreover, a new objective function formulated using the Dice coefficient is proposed to be optimized, and the authors demonstrate the fast and superior performance of the algorithm on the segmentation of prostate MRI volumes. ...

Given two shapes, $N$ samples are drawn from the edge elements of the shape. There are no specific constraints on these points - they can be either on the internal or the external contour of the object. Moreover, they also need not correspond to keypoints for the shape (such as maxima of curvature, inflection points, etc.), and although desired that the samples be uniform in spacing, this too is not a rigid criterion. ...

The point set registration problem can be described as: for two finite size point sets ${M, S}$, where $M$ represents the moving “model” set and $S$ represents the fixed “scene” set, and both $M$ and $S$ are assumed to be subsets of a finite dimensional real vector space $\mathbb{R}^d$, and they can be of different sizes. The registration task involves estimating a mapping from $\mathbb{R}^d$ to $\mathbb{R}^d$ which yields the best alignment between the two sets $M$ and $S$. An important consideration here is that apart from the set of points being treated as a collection of isolated unstructured points, there is no assumption of additional information about the points (such as mesh structure, labels, features, etc.). ...

The authors propose using an implicit shape representation for the source and the target shapes. The Euclidean signed distance transform is used to model the shape of interest as the zero level set of a distance function. Let $\Phi: \Omega \to R^+$ denote the distance transform of a shape $S$. The shape defines a partition of the image domain $\Omega$ - the region enclosed by $S$ is denoted by $[R_S]$ and the background region is denoted by $[\Omega - R_S]$. Using signed Euclidean distance transform, we have ...