Knowing the Distance: Understanding the Gap Between Synthetic and Real Data For Face Parsing

The use of synthetic data for training computer vision algorithms has become increasingly popular due to its cost-effectiveness, scalability, and ability to provide accurate multi-modality labels. Although recent studies have demonstrated impressive results when training networks solely on synthetic data, there remains a performance gap between synthetic and real data that is commonly attributed to lack of photorealism. The aim of this study is to investigate the gap in greater detail for the face parsing task. We differentiate between three types of gaps: distribution gap, label gap, and photorealism gap. Our findings show that the distribution gap is the largest contributor to the performance gap, accounting for over 50% of the gap. By addressing this gap and accounting for the labels gap, we demonstrate that a model trained on synthetic data achieves comparable results to one trained on a similar amount of real data. This suggests that synthetic data is a viable alternative to real data, especially when real data is limited or difficult to obtain. Our study highlights the importance of content diversity in synthetic datasets and challenges the notion that the photorealism gap is the most critical factor affecting the performance of computer vision models trained on synthetic data

Generalised coherent point drift for group-wise multi-dimensional analysis of diffusion brain MRI data

A probabilistic framework for registering generalised point sets comprising multiple voxel-wise data features such as positions, orientations and scalar-valued quantities, is proposed. It is employed for the analysis of magnetic resonance diffusion tensor image (DTI)-derived quantities, such as fractional anisotropy (FA) and fibre orientation, across multiple subjects. A hybrid Student’s t-Watson-Gaussian mixture model-based non-rigid registration framework is formulated for the joint registration and clustering of voxel-wise DTI-derived data, acquired from multiple subjects. The proposed approach jointly estimates the non-rigid transformations necessary to register an unbiased mean template (represented as a 7-dimensional hybrid point set comprising spatial positions, fibre orientations and FA values) to white matter regions of interest (ROIs), and approximates the joint distribution of voxel spatial positions, their associated principal diffusion axes, and FA. Specific white matter ROIs, namely, the corpus callosum and cingulum, are analysed across healthy control (HC) subjects (K = 20 samples) and patients diagnosed with mild cognitive impairment (MCI) (K = 20 samples) or Alzheimer’s disease (AD) (K = 20 samples) using the proposed framework, facilitating inter-group comparisons of FA and fibre orientations. Group-wise analyses of the latter is not afforded by conventional approaches such as tract-based spatial statistics (TBSS) and voxel-based morphometry (VBM)

Improving alignment in Tract-based spatial statistics:Evaluation and optimization of image registration

<p>Anatomical alignment in neuroimaging studies is of such importance that considerable effort is put into improving the registration used to establish spatial correspondence. Tract-based spatial statistics (TBSS) is a popular method for comparing diffusion characteristics across subjects. TBSS establishes spatial correspondence using a combination of nonlinear registration and a "skeleton projection" that may break topological consistency of the transformed brain images. We therefore investigated feasibility of replacing the two-stage registration-projection procedure in TBSS with a single, regularized, high-dimensional registration.</p><p>To optimize registration parameters and to evaluate registration performance in diffusion MRI, we designed an evaluation framework that uses native space probabilistic tractography for 23 white matter tracts, and quantifies tract similarity across subjects in standard space. We optimized parameters for two registration algorithms on two diffusion datasets of different quality. We investigated reproducibility of the evaluation framework, and of the optimized registration algorithms. Next, we compared registration performance of the regularized registration methods and TBSS. Finally, feasibility and effect of incorporating the improved registration in TBSS were evaluated in an example study.</p><p>The evaluation framework was highly reproducible for both algorithms (R-2 0.993; 0.931). The optimal registration parameters depended on the quality of the dataset in a graded and predictable manner. At optimal parameters, both algorithms outperformed the registration of TBSS, showing feasibility of adopting such approaches in TBSS. This was further confirmed in the example experiment. (C) 2013 Elsevier Inc. All rights reserved.</p>

Automatic Atlas-Based Segmentation of Brain White Matter in Neonates at Risk for Neurodevelopmental Disorders

Very preterm infants, &lt; 32 weeks gestation, are at high risk for brain injury. Cognitive deficits are often diagnosed at a later stage, since there are no available predictive biomarkers in the neonatal period. The maturation of specific white matter (WM) brain structures is considered a promising early-stage biomarker. With Diffusion Tensor Imaging (DTI) tractography, an in vivo and non-invasive evaluation of these anatomical structures is possible. We developed an automatic tractography segmentation pipeline, which allows for maturation assessment of the different segmented WM structures. Our segmentation pipeline is atlas-based, specifically designed for premature neonates at term equivalent age. In order to better make use of global information from tractography, all processing is done in the fiber domain. Segmented fiber bundles are further automatically quantified with respect to volume and anisotropy. Of the 24 automatically segmented neonatal tractographies, only three contained more than 30% mislabeled fibers. Results show no dependency to WM pathology. By automatically segmenting WM, we reduced the user-dependency and bias characteristic of manual methods. This study assesses the structure of the neonatal brain based on an automatic WM segmentation in the fiber domain method using DTI tractography data