Real-Time Quality Assessment of Pediatric MRI via Semi-Supervised Deep Nonlocal Residual Neural Networks

Multifaceted atlases of the human brain in its infancy

Brain atlases are spatial references for integrating, processing, and analyzing brain features gathered from different individuals, sources, and scales. Here we introduce a collection of joint surface-volume atlases that chart postnatal development of the human brain in a spatiotemporally dense manner from two weeks to two years of age. Our month-specific atlases chart normative patterns and capture key traits of early brain development and are therefore conducive to identifying aberrations from normal developmental trajectories. These atlases will enhance our understanding of early structural and functional development by facilitating the mapping of diverse features of the infant brain to a common reference frame for precise multifaceted quantification of cortical and subcortical changes.

No-reference image quality assessment for confocal endoscopy images with perceptual local descriptor

SIGNIFICANCE

Confocal endoscopy images often suffer distortions, resulting in image quality degradation and information loss, increasing the difficulty of diagnosis and even leading to misdiagnosis. It is important to assess image quality and filter images with low diagnostic value before diagnosis.

AIM

We propose a no-reference image quality assessment (IQA) method for confocal endoscopy images based on Weber's law and local descriptors. The proposed method can detect the severity of image degradation by capturing the perceptual structure of an image.

APPROACH

We created a new dataset of 642 confocal endoscopy images to validate the performance of the proposed method. We then conducted extensive experiments to compare the accuracy and speed of the proposed method with other state-of-the-art IQA methods.

RESULTS

Experimental results demonstrate that the proposed method achieved an SROCC of 0.85 and outperformed other IQA methods.

CONCLUSIONS

Given its high consistency in subjective quality assessment, the proposed method can screen high-quality images in practical applications and contribute to diagnosis.

Pesticide detection combining the Wasserstein generative adversarial network and the residual neural network based on terahertz spectroscopy

Feature extraction is a key factor to detect pesticides using terahertz spectroscopy. Compared to traditional methods, deep learning is able to obtain better insights into complex data features at high levels of abstraction. However, reports about the application of deep learning in THz spectroscopy are rare. The main limitation of deep learning to analyse terahertz spectroscopy is insufficient learning samples. In this study, we proposed a WGAN-ResNet method, which combines two deep learning networks, the Wasserstein generative adversarial network (WGAN) and the residual neural network (ResNet), to detect carbendazim based on terahertz spectroscopy. The Wasserstein generative adversarial network and pretraining model technology were employed to solve the problem of insufficient learning samples for training the ResNet. The Wasserstein generative adversarial network was used for generating more new learning samples. At the same time, pretraining model technology was applied to reduce the training parameters, in order to avoid residual neural network overfitting. The results demonstrate that our proposed method achieves a 91.4% accuracy rate, which is better than those of support vector machine, k-nearest neighbor, naïve Bayes model and ensemble learning. In summary, our proposed method demonstrates the potential application of deep learning in pesticide residue detection, expanding the application of THz spectroscopy.

Artifact- and content-specific quality assessment for MRI with image rulers

In clinical practice MR images are often first seen by radiologists long after the scan. If image quality is inadequate either patients have to return for an additional scan, or a suboptimal interpretation is rendered. An automatic image quality assessment (IQA) would enable real-time remediation. Existing IQA works for MRI give only a general quality score, agnostic to the cause of and solution to low-quality scans. Furthermore, radiologists' image quality requirements vary with the scan type and diagnostic task. Therefore, the same score may have different implications for different scans. We propose a framework with multi-task CNN model trained with calibrated labels and inferenced with image rulers. Labels calibrated by human inputs follow a well-defined and efficient labeling task. Image rulers address varying quality standards and provide a concrete way of interpreting raw scores from the CNN. The model supports assessments of two of the most common artifacts in MRI: noise and motion. It achieves accuracies of around 90%, 6% better than the best previous method examined, and 3% better than human experts on noise assessment. Our experiments show that label calibration, image rulers, and multi-task training improve the model's performance and generalizability.

Hierarchical Nonlocal Residual Networks for Image Quality Assessment of Pediatric Diffusion MRI With Limited and Noisy Annotations

Fast and automated image quality assessment (IQA) of diffusion MR images is crucial for making timely decisions for rescans. However, learning a model for this task is challenging as the number of annotated data is limited and the annotation labels might not always be correct. As a remedy, we will introduce in this paper an automatic image quality assessment (IQA) method based on hierarchical non-local residual networks for pediatric diffusion MR images. Our IQA is performed in three sequential stages, i.e., 1) slice-wise IQA, where a nonlocal residual network is first pre-trained to annotate each slice with an initial quality rating (i.e., pass/questionable/fail), which is subsequently refined via iterative semi-supervised learning and slice self-training; 2) volume-wise IQA, which agglomerates the features extracted from the slices of a volume, and uses a nonlocal network to annotate the quality rating for each volume via iterative volume self-training; and 3) subject-wise IQA, which ensembles the volumetric IQA results to determine the overall image quality pertaining to a subject. Experimental results demonstrate that our method, trained using only samples of modest size, exhibits great generalizability, and is capable of conducting rapid hierarchical IQA with near-perfect accuracy.

Single image super-resolution via Image Quality Assessment-Guided Deep Learning Network

In recent years, deep learning (DL) networks have been widely used in super-resolution (SR) and exhibit improved performance. In this paper, an image quality assessment (IQA)-guided single image super-resolution (SISR) method is proposed in DL architecture, in order to achieve a nice tradeoff between perceptual quality and distortion measure of the SR result. Unlike existing DL-based SR algorithms, an IQA net is introduced to extract perception features from SR results, calculate corresponding loss fused with original absolute pixel loss, and guide the adjustment of SR net parameters. To solve the problem of heterogeneous datasets used by IQA and SR networks, an interactive training model is established via cascaded network. We also propose a pairwise ranking hinge loss method to overcome the shortcomings of insufficient samples during training process. The performance comparison between our proposed method with recent SISR methods shows that the former achieves a better tradeoff between perceptual quality and distortion measure than the latter. Extensive benchmark experiments and analyses also prove that our method provides a promising and opening architecture for SISR, which is not confined to a specific network model.