Depth Transfer: Depth Extraction from Video Using Non-Parametric Sampling.

We describe a technique that automatically generates plausible depth maps from videos using non-parametric depth sampling. We demonstrate our technique in cases where past methods fail (non-translating cameras and dynamic scenes). Our technique is applicable to single images as well as videos. For videos, we use local motion cues to improve the inferred depth maps, while optical flow is used to ensure temporal depth consistency. For training and evaluation, we use a Kinect-based system to collect a large data set containing stereoscopic videos with known depths. We show that our depth estimation technique outperforms the state-of-the-art on benchmark databases. Our technique can be used to automatically convert a monoscopic video into stereo for 3D visualization, and we demonstrate this through a variety of visually pleasing results for indoor and outdoor scenes, including results from the feature film Charade.

Semi-automatic 3D segmentation of brain structures from MRI.

We present a semi-automatic 3D segmentation method for brain structures from Magnetic Resonance Imaging (MRI). There are three main contributions. First, our method combines boundary-based and region-based approaches but differs from previous hybrid methods in that we perform them in two separate phases. This allows for more efficient segmentation. Second, a probability map is generated and used throughout the segmentation to account for the brain structures with low-intensity contrast to the background. Third, we develop a set of tools for manual adjustment after the segmentation. This is particularly important in clinical research because the reliability of the results can be ensured. The experimental results and validations on different data sets are shown.

Detecting corpus callosum abnormalities in autism based on anatomical landmarks.

Autism is a severe developmental disorder whose neurological basis is largely unknown. The aim of this study was to identify the shape differences of the corpus callosum between patients with autism and control subjects. Anatomical landmarks were collected from midsagittal magnetic resonance images of 25 patients and 18 controls. Euclidean distance matrix analysis and thin-plate spline analyses were used to examine the landmark forms. Point-by-point shape comparison was performed both globally and locally. A new local shape comparison scheme was proposed which compared each part of the shape in its local coordinate system. Point correspondence was established among individual shapes based on the inherent landmark correspondence. No significant difference was found in the landmark form between patients and controls, but the distance between the interior genu and the posterior-most section was found to be significantly shorter in patients. Thin-plate spline analysis showed significant group differences between the landmark configurations in terms of the deformation from the overall mean configuration. Significant global shape differences were found in the anterior lower body and posterior bottom, and there was a local shape difference in the anterior bottom. This study can serve as both a clinical reference and a detailed procedural guideline for similar studies in the future.

Detecting 3D Corpus Callosum abnormalities in phenylketonuria.

Phenylketonuria (PKU) is a genetic disorder characterised by an inability to metabolise phenylalanine. Several studies have reported that the Corpus Callosum (CC) is one of the most severely affected structures with respect to volume loss in early treated PKU patients. In this work, we aim to detect the abnormalities of the CC in PKU from both global and local perspectives. 3D models of the CC are extracted from MRI data using a semiautomatic segmentation method. In the global analysis, raw and scaled volumes of the CC are compared between PKU patients and the controls. An oriented bounding box of the CC is constructed and its length, width and height are used as the MRI traits in our study. The raw and scaled values of these traits are compared between patients and controls. In the local analysis, shape differences at every surface point of the CC between PKU patients and the controls are computed using Hotelling T(2) two-sample metric followed by a permutation test. The height of the CC is found to be significantly shorter in the patients and significant shape abnormalities in the genu and splenium of the CC is also found in the patients.

Abnormalities in MRI traits of corpus callosum in autism subtype.

A number of studies have documented that autism has a neurobiological basis, but the anatomical extent of these neurobiological abnormalities is largely unknown. In this paper, we apply advanced computational techniques to extract 3D models of the corpus callosum (CC) and subsequently analyze the volumetric deficit of the total CC and its five sub-regions in a homogeneous group of autistic children. Moreover, we explore new MRI traits based on the oriented bounding rectangle of the CC, which are the length, width and aspect ratio of the bounding rectangle. These measurements as well as the volumes are compared between patients and controls using t-tests. The results reveal significant reduction in all sub-regions of the CC and some MRI traits in the patients.

Web based brain volume calculation for magnetic resonance images.

Brain volume calculations are crucial in modern medical research, especially in the study of neurodevelopmental disorders. In this paper, we present an algorithm for calculating two classifications of brain volume, total brain volume (TBV) and intracranial volume (ICV). Our algorithm takes MRI data as input, performs several preprocessing and intermediate steps, and then returns each of the two calculated volumes. To simplify this process and make our algorithm publicly accessible to anyone, we have created a web-based interface that allows users to upload their own MRI data and calculate the TBV and ICV for the given data. This interface provides a simple and efficient method for calculating these two classifications of brain volume, and it also removes the need for the user to download or install any applications.