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The extraction of 3D shape from texture and shading in the human brain.

ABSTRACT: We used functional magnetic resonance imaging to investigate the human cortical areas involved in processing 3-dimensional (3D) shape from texture (SfT) and shading. The stimuli included monocular images of randomly shaped 3D surfaces and a wide variety of 2-dimensional (2D) controls. The results of both passive and active experiments reveal that the extraction of 3D SfT involves the bilateral caudal inferior temporal gyrus (caudal ITG), lateral occipital sulcus (LOS) and several bilateral sites along the intraparietal sulcus. These areas are largely consistent with those involved in the processing of 3D shape from motion and stereo. The experiments also demonstrate, however, that the analysis of 3D shape from shading is primarily restricted to the caudal ITG areas. Additional results from psychophysical experiments reveal that this difference in neuronal substrate cannot be explained by a difference in strength between the 2 cues. These results underscore the importance of the posterior part of the lateral occipital complex for the extraction of visual 3D shape information from all depth cues, and they suggest strongly that the importance of shading is diminished relative to other cues for the analysis of 3D shape in parietal regions.

SUBMITTER: Georgieva SS

PROVIDER: S-EPMC2536698 | biostudies-literature | 2008 Oct

REPOSITORIES: biostudies-literature

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The extraction of 3D shape from texture and shading in the human brain.

Georgieva Svetlana S SS Todd James T JT Peeters Ronald R Orban Guy A GA

Cerebral cortex (New York, N.Y. : 1991) 20080214 10

We used functional magnetic resonance imaging to investigate the human cortical areas involved in processing 3-dimensional (3D) shape from texture (SfT) and shading. The stimuli included monocular images of randomly shaped 3D surfaces and a wide variety of 2-dimensional (2D) controls. The results of both passive and active experiments reveal that the extraction of 3D SfT involves the bilateral caudal inferior temporal gyrus (caudal ITG), lateral occipital sulcus (LOS) and several bilateral sites ...[more]

PMID: 18281304

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Project description:IntroductionCognitive impairment in patients with end-stage renal disease (ESRD) is associated with brain structural damage. However, no prior studies have investigated the relationship between brain texture features and the cognitive function in ESRD patients. This study aimed to investigate changes in brain texture features in ESRD patients and their relationships with cognitive function using voxel-based 3D brain texture analysis (TA), and further predict individual cognitive-related brain damage in ESRD patients.MethodsForty-seven ESRD patients and 45 control subjects underwent whole-brain high-resolution 3D T1-weighted imaging scans and neuropsychological assessments. The voxel-based 3D brain TA was performed to examine inter-group differences in brain texture features. Additionally, within the ESRD group, the relationships of altered texture features with neuropsychological function and clinical indicators were analyzed. Finally, receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive ability of brain texture features for cognitive-related brain damage in ESRD patients.ResultsCompared to the control group, the ESRD group exhibited altered texture features in several brain regions, including the insula, temporal lobe, striatum, cerebellum, and fusiform gyrus (p < 0.05, Gaussian random-field correction). Some of these altered texture features were associated with scores from the Digit Symbol Substitution Test and the Trail Making Test Parts A (p < 0.05), and showed significant correlations with serum creatinine and calcium levels within the ESRD group (p < 0.05). Notably, ROC curve analysis revealed that the texture features in the right insula and left middle temporal gyrus could accurately predict cognitive-related brain damage in ESRD patients, with the area under the curve values exceeding 0.90.ConclusionAberrant brain texture features may be involved in the neuropathological mechanism of cognitive decline, and have high accuracy in predicting cognitive-related brain damage in ESRD patients. TA offers a novel neuroimaging marker to explore the neuropathological mechanisms of cognitive impairment in ESRD patients, and may be a valuable tool to predict cognitive decline.

Project description:Brain extraction is a critical pre-processing step in brain magnetic resonance imaging (MRI) analytical pipelines. In rodents, this is often achieved by manually editing brain masks slice-by-slice, a time-consuming task where workloads increase with higher spatial resolution datasets. We recently demonstrated successful automatic brain extraction via a deep-learning-based framework, U-Net, using 2D convolutions. However, such an approach cannot make use of the rich 3D spatial-context information from volumetric MRI data. In this study, we advanced our previously proposed U-Net architecture by replacing all 2D operations with their 3D counterparts and created a 3D U-Net framework. We trained and validated our model using a recently released CAMRI rat brain database acquired at isotropic spatial resolution, including T2-weighted turbo-spin-echo structural MRI and T2*-weighted echo-planar-imaging functional MRI. The performance of our 3D U-Net model was compared with existing rodent brain extraction tools, including Rapid Automatic Tissue Segmentation, Pulse-Coupled Neural Network, SHape descriptor selected External Regions after Morphologically filtering, and our previously proposed 2D U-Net model. 3D U-Net demonstrated superior performance in Dice, Jaccard, center-of-mass distance, Hausdorff distance, and sensitivity. Additionally, we demonstrated the reliability of 3D U-Net under various noise levels, evaluated the optimal training sample sizes, and disseminated all source codes publicly, with a hope that this approach will benefit rodent MRI research community. Significant Methodological Contribution: We proposed a deep-learning-based framework to automatically identify the rodent brain boundaries in MRI. With a fully 3D convolutional network model, 3D U-Net, our proposed method demonstrated improved performance compared to current automatic brain extraction methods, as shown in several qualitative metrics (Dice, Jaccard, PPV, SEN, and Hausdorff). We trust that this tool will avoid human bias and streamline pre-processing steps during 3D high resolution rodent brain MRI data analysis. The software developed herein has been disseminated freely to the community.

Project description:IntroductionPalatal rugae are used as anatomical landmarks on the hard palate, in various clinical applications; in forensics, for insertion of mini-screws, and for superimposition. There is ambiguous evidence on whether they change during orthodontic treatment and to what extent. Therefore, we investigated changes in the shape, complexity, and area occupied by palatal rugae following orthodontic treatment with and without extractions.Materials and methodsPre- and post-treatment plaster models of maxillae of 28 cases involving first premolar extractions (17 females and 11 males) and 33 non-extraction cases (19 females and 14 males) were scanned and analysed. All participants were selected from a parent sample via discriminant analysis and represent borderline cases. We applied mesh cropping, ball pivoting, distance mapping, contour cropping of rugae, best-fit superimposition, fractal dimension (FD) analysis, and creation of rugae's convex hull area with Viewbox 4 software. The average distance between the closest points of the outlines of pre- and post-treatment palatal rugae (indicating shape change in the set of rugae), disparity in their pre- and post-treatment FDs (reflecting the complexity of their shapes), area occupied by rugae, arch depth, and size of palatal surface were then computed.ResultsThe medians of the average distance between pre- and post-treatment outlines after best-fit superimposition were 0.39 mm (interquartile range [IQR]: 0.34-0.51) and 0.27 mm (IQR: 0.22-0.34) mm for the extraction and non-extraction groups, respectively (P < 0.001). The median pre-treatment FDs were 1.497 (IQR: 1.481-1.521) for the extraction group and 1.481 (IQR: 1.456-1.509) for the non-extraction group, whereas their median post-treatment FDs were 1.502 (IQR: 1.472-1.532) and 1.489 (IQR: 1.469-1.501), respectively. The differences between pre- and post-treatment fractal dimensions were not found to be significant, neither within each group, nor across the groups. On the other hand, the surface area occupied by rugae showed a median increase of 14.7 mm2 (IQR: 0.0-46.5) (P = 0.003) following non-extraction treatment only.ConclusionPalatal rugae change shape during orthodontic treatment, but their shape complexity, as measured by fractal dimensions, remains unaltered. Extraction treatment exerts a more pronounced effect in shape change compared to treatment without extractions. Nevertheless, non-extraction orthodontic treatment increases the surface on which rugae lie, as measured by means of the convex hull. Although the alterations may appear minor, it is necessary to exercise caution and prudence when employing rugae for superimposition and forensic dentistry purposes.

Dataset Information

The extraction of 3D shape from texture and shading in the human brain.

Publications

The extraction of 3D shape from texture and shading in the human brain.

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