100 research outputs found
Learning Raw Image Denoising with Bayer Pattern Unification and Bayer Preserving Augmentation
In this paper, we present new data pre-processing and augmentation techniques
for DNN-based raw image denoising. Compared with traditional RGB image
denoising, performing this task on direct camera sensor readings presents new
challenges such as how to effectively handle various Bayer patterns from
different data sources, and subsequently how to perform valid data augmentation
with raw images. To address the first problem, we propose a Bayer pattern
unification (BayerUnify) method to unify different Bayer patterns. This allows
us to fully utilize a heterogeneous dataset to train a single denoising model
instead of training one model for each pattern. Furthermore, while it is
essential to augment the dataset to improve model generalization and
performance, we discovered that it is error-prone to modify raw images by
adapting augmentation methods designed for RGB images. Towards this end, we
present a Bayer preserving augmentation (BayerAug) method as an effective
approach for raw image augmentation. Combining these data processing technqiues
with a modified U-Net, our method achieves a PSNR of 52.11 and a SSIM of 0.9969
in NTIRE 2019 Real Image Denoising Challenge, demonstrating the
state-of-the-art performance. Our code is available at
https://github.com/Jiaming-Liu/BayerUnifyAug.Comment: Accepted by CVPRW 201
Tractography-Based Parcellation of Cerebellar Dentate Nuclei via a Deep Nonnegative Matrix Factorization Clustering Method
As the largest human cerebellar nucleus, the dentate nucleus (DN) functions
significantly in the communication between the cerebellum and the rest of the
brain. Structural connectivity-based parcellation has the potential to reveal
the topography of the DN and enable the study of its subregions. In this paper,
we investigate a deep nonnegative matrix factorization clustering method
(DNMFC) for parcellation of the human DN based on its structural connectivity
using diffusion MRI tractography. We propose to describe the connectivity of
the DN using a set of curated tractography fiber clusters within the
cerebellum. Experiments are conducted on the diffusion MRI data of 50 healthy
adults from the Human Connectome Project. In comparison with state-of-the-art
clustering methods, DN parcellations resulting from DNMFC show better quality
and consistency of parcels across subjects
TractCloud: Registration-free tractography parcellation with a novel local-global streamline point cloud representation
Diffusion MRI tractography parcellation classifies streamlines into
anatomical fiber tracts to enable quantification and visualization for clinical
and scientific applications. Current tractography parcellation methods rely
heavily on registration, but registration inaccuracies can affect parcellation
and the computational cost of registration is high for large-scale datasets.
Recently, deep-learning-based methods have been proposed for tractography
parcellation using various types of representations for streamlines. However,
these methods only focus on the information from a single streamline, ignoring
geometric relationships between the streamlines in the brain. We propose
TractCloud, a registration-free framework that performs whole-brain
tractography parcellation directly in individual subject space. We propose a
novel, learnable, local-global streamline representation that leverages
information from neighboring and whole-brain streamlines to describe the local
anatomy and global pose of the brain. We train our framework on a large-scale
labeled tractography dataset, which we augment by applying synthetic transforms
including rotation, scaling, and translations. We test our framework on five
independently acquired datasets across populations and health conditions.
TractCloud significantly outperforms several state-of-the-art methods on all
testing datasets. TractCloud achieves efficient and consistent whole-brain
white matter parcellation across the lifespan (from neonates to elderly
subjects, including brain tumor patients) without the need for registration.
The robustness and high inference speed of TractCloud make it suitable for
large-scale tractography data analysis. Our project page is available at
https://tractcloud.github.io/.Comment: MICCAI 202
Superficial White Matter Analysis: An Efficient Point-cloud-based Deep Learning Framework with Supervised Contrastive Learning for Consistent Tractography Parcellation across Populations and dMRI Acquisitions
Diffusion MRI tractography is an advanced imaging technique that enables in
vivo mapping of the brain's white matter connections. White matter parcellation
classifies tractography streamlines into clusters or anatomically meaningful
tracts. It enables quantification and visualization of whole-brain
tractography. Currently, most parcellation methods focus on the deep white
matter (DWM), whereas fewer methods address the superficial white matter (SWM)
due to its complexity. We propose a novel two-stage deep-learning-based
framework, Superficial White Matter Analysis (SupWMA), that performs an
efficient and consistent parcellation of 198 SWM clusters from whole-brain
tractography. A point-cloud-based network is adapted to our SWM parcellation
task, and supervised contrastive learning enables more discriminative
representations between plausible streamlines and outliers for SWM. We train
our model on a large-scale tractography dataset including streamline samples
from labeled SWM clusters and anatomically implausible streamline samples, and
we perform testing on six independently acquired datasets of different ages and
health conditions (including neonates and patients with space-occupying brain
tumors). Compared to several state-of-the-art methods, SupWMA obtains highly
consistent and accurate SWM parcellation results on all datasets, showing good
generalization across the lifespan in health and disease. In addition, the
computational speed of SupWMA is much faster than other methods.Comment: 12 pages, 7 figures. Extension of our ISBI 2022 paper
(arXiv:2201.12528) (Best Paper Award Finalist
TractGeoNet: A geometric deep learning framework for pointwise analysis of tract microstructure to predict language assessment performance
We propose a geometric deep-learning-based framework, TractGeoNet, for
performing regression using diffusion magnetic resonance imaging (dMRI)
tractography and associated pointwise tissue microstructure measurements. By
employing a point cloud representation, TractGeoNet can directly utilize
pointwise tissue microstructure and positional information from all points
within a fiber tract. To improve regression performance, we propose a novel
loss function, the Paired-Siamese Regression loss, which encourages the model
to focus on accurately predicting the relative differences between regression
label scores rather than just their absolute values. In addition, we propose a
Critical Region Localization algorithm to identify highly predictive anatomical
regions within the white matter fiber tracts for the regression task. We
evaluate the effectiveness of the proposed method by predicting individual
performance on two neuropsychological assessments of language using a dataset
of 20 association white matter fiber tracts from 806 subjects from the Human
Connectome Project. The results demonstrate superior prediction performance of
TractGeoNet compared to several popular regression models. Of the twenty tracts
studied, we find that the left arcuate fasciculus tract is the most highly
predictive of the two studied language performance assessments. The localized
critical regions are widespread and distributed across both hemispheres and all
cerebral lobes, including areas of the brain considered important for language
function such as superior and anterior temporal regions, pars opercularis, and
precentral gyrus. Overall, TractGeoNet demonstrates the potential of geometric
deep learning to enhance the study of the brain's white matter fiber tracts and
to relate their structure to human traits such as language performance.Comment: 28 pages, 7 figure
A Novel Deep Clustering Framework for Fine-Scale Parcellation of Amygdala Using dMRI Tractography
The amygdala plays a vital role in emotional processing and exhibits
structural diversity that necessitates fine-scale parcellation for a
comprehensive understanding of its anatomico-functional correlations. Diffusion
MRI tractography is an advanced imaging technique that can estimate the brain's
white matter structural connectivity to potentially reveal the topography of
the amygdala for studying its subdivisions. In this work, we present a deep
clustering pipeline to perform automated, fine-scale parcellation of the
amygdala using diffusion MRI tractography. First, we incorporate a newly
proposed deep learning approach to enable accurate segmentation of the amygdala
directly on the dMRI data. Next, we design a novel streamline clustering-based
structural connectivity feature for a robust representation of voxels within
the amygdala. Finally, we improve the popular joint dimensionality reduction
and k-means clustering approach to enable amygdala parcellation at a finer
scale. With the proposed method, we obtain nine unique amygdala parcels.
Experiments show that these parcels can be consistently identified across
subjects and have good correspondence to the widely used coarse-scale amygdala
parcellation
Genetic variations in APPL2 are associated with overweight and obesity in a Chinese population with normal glucose tolerance
<p>Abstract</p> <p>Background</p> <p>APPL1 and APPL2 are two adaptor proteins, which can mediate adiponectin signaling via binding to N terminus of adiponectin receptors in muscle cells. Genes encoding adiponectin and adiponectin receptors contribute to insulin resistance and the risk of obesity, and genetic variants of <it>APPL1 </it>are associated with body fat distribution. However, the association between genetic variations of <it>APPL2 </it>and metabolic traits remains unknown. In the current study, we aimed to test the impacts of <it>APPL2 </it>genetic variants on obesity in a Chinese population with normal glucose tolerance.</p> <p>Methods</p> <p>We genotyped six single nucleotide polymorphisms (SNPs) in <it>APPL2 </it>in 1,808 non-diabetic subjects. Overweight and obesity were defined by body mass index (BMI). Obesity-related anthropometric parameters were measured, including height, weight, waist circumference, hip circumference. BMI and waist-hip ratio (WHR) were calculated.</p> <p>Results</p> <p>We found significant evidence of association with overweight/obesity for rs2272495 and rs1107756. rs2272495 C allele and rs1107756 T allele both conferred a higher risk of being overweight and obese (OR 1.218, 95% CI 1.047-1.416, <it>p </it>= 0.011 for rs2272495; OR 1.166, 95% CI 1.014-1.341, <it>p </it>= 0.031 for rs1107756). After adjusting multiple comparisons, only the effect of rs2272495 on overweight/obesity remained to be significant (empirical <it>p </it>= 0.043). Moreover, we investigated the effects of these SNPs on obesity-related quantitative traits in all participants. rs2272495 was associated with BMI (<it>p </it>= 0.015), waist circumference (<it>p </it>= 0.006), hip circumference (<it>p </it>= 0.025) as well as WHR (<it>p </it>= 0.047) under a recessive model. Similar associations were found for rs1107756 except for WHR.</p> <p>Conclusion</p> <p>This study suggests that genetic variations in <it>APPL2 </it>are associated with overweight and obesity in Chinese population with normal glucose tolerance.</p
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