Arbitrary Order Total Variation for Deformable Image Registration

In this work, we investigate image registration in a variational framework and focus on regularization generality and solver efficiency. We first propose a variational model combining the state-of-the-art sum of absolute differences (SAD) and a new arbitrary order total variation regularization term. The main advantage is that this variational model preserves discontinuities in the resultant deformation while being robust to outlier noise. It is however non-trivial to optimize the model due to its non-convexity, non-differentiabilities, and generality in the derivative order. To tackle these, we propose to first apply linearization to the model to formulate a convex objective function and then break down the resultant convex optimization into several point-wise, closed-form subproblems using a fast, over-relaxed alternating direction method of multipliers (ADMM). With this proposed algorithm, we show that solving higher-order variational formulations is similar to solving their lower-order counterparts. Extensive experiments show that our ADMM is significantly more efficient than both the subgradient and primal-dual algorithms particularly when higher-order derivatives are used, and that our new models outperform state-of-the-art methods based on deep learning and free-form deformation. Our code implemented in both Matlab and Pytorch is publicly available at https://github.com/j-duan/AOTV

Semantics-Consistent Cross-domain Summarization via Optimal Transport Alignment

Multimedia summarization with multimodal output (MSMO) is a recently explored application in language grounding. It plays an essential role in real-world applications, i.e., automatically generating cover images and titles for news articles or providing introductions to online videos. However, existing methods extract features from the whole video and article and use fusion methods to select the representative one, thus usually ignoring the critical structure and varying semantics. In this work, we propose a Semantics-Consistent Cross-domain Summarization (SCCS) model based on optimal transport alignment with visual and textual segmentation. In specific, our method first decomposes both video and article into segments in order to capture the structural semantics, respectively. Then SCCS follows a cross-domain alignment objective with optimal transport distance, which leverages multimodal interaction to match and select the visual and textual summary. We evaluated our method on three recent multimodal datasets and demonstrated the effectiveness of our method in producing high-quality multimodal summaries

An improved contrastive learning network for semi-supervised multi-structure segmentation in echocardiography

Cardiac diseases have high mortality rates and are a significant threat to human health. Echocardiography is a commonly used imaging technique to diagnose cardiac diseases because of its portability, non-invasiveness and low cost. Precise segmentation of basic cardiac structures is crucial for cardiologists to efficiently diagnose cardiac diseases, but this task is challenging due to several reasons, such as: (1) low image contrast, (2) incomplete structures of cardiac, and (3) unclear border between the ventricle and the atrium in some echocardiographic images. In this paper, we applied contrastive learning strategy and proposed a semi-supervised method for echocardiographic images segmentation. This proposed method solved the above challenges effectively and made use of unlabeled data to achieve a great performance, which could help doctors improve the accuracy of CVD diagnosis and screening. We evaluated this method on a public dataset (CAMUS), achieving mean Dice Similarity Coefficient (DSC) of 0.898, 0.911, 0.916 with 1/4, 1/2 and full labeled data on two-chamber (2CH) echocardiography images, and of 0.903, 0.921, 0.928 with 1/4, 1/2 and full labeled data on four-chamber (4CH) echocardiography images. Compared with other existing methods, the proposed method had fewer parameters and better performance. The code and models are available at https://github.com/gpgzy/CL-Cardiac-segmentation

Verbal creativity correlates with the temporal variability of brain networks during the resting state

Creativity is the ability to see the world in new ways. Creative individuals exhibit the ability to switch between different modes of thinking and shift their mental focus. This suggests a connection between creativity and dynamic interactions of brain networks. We report here the first investigation into the relationship between the reconfiguration of dynamic brain networks during the resting state and verbal creativity using two fMRI datasets involving 574 subjects. We find that verbal creativity correlates with temporal variability of the functional-connectivity (FC) patterns of the lateral prefrontal cortex, the precuneus, and the parahippocampal gyrus. High variability of these regions indicates flexible connectivity patterns which may facilitate executive functions. Furthermore, verbal creativity correlates with the temporal variability of FC patterns within the default mode network (DMN), between the DMN and attention/sensorimotor network, and between control and sensory networks. High variability of FCs between the DMN and attention networks characterizes frequent adjustments of attention. Finally, dynamic interaction between the cerebellum and task control network also contributes to verbal creativity, suggesting a relationship between the cerebellum and creativity. This study reveals a close relationship between verbal creativity and high variability of cortical networks involved in spontaneous thought, attention and cognitive control

Neural and genetic determinants of creativity

Creative thinking plays a vital role in almost all aspects of human life. However, little is known about the neural and genetic mechanisms underlying creative thinking. Based on a cross-validation based predictive framework, we searched from the whole-brain connectome (34,716 functional connectivities) and whole genome data (309,996 SNPs) in two datasets (all collected by Southwest University, Chongqing) consisting of altogether 236 subjects, for a better understanding of the brain and genetic underpinning of creativity. Using the Torrance Tests of Creative Thinking score, we found that high figural creativity is mainly related to high functional connectivity between the executive control, attention, and memory retrieval networks (strong top-down effects); and to low functional connectivity between the default mode network, the ventral attention network, and the subcortical and primary sensory networks (weak bottom-up processing) in the first dataset (consisting of 138 subjects). High creativity also correlates significantly with mutations of genes coding for both excitatory and inhibitory neurotransmitters. Combining the brain connectome and the genomic data we can predict individuals' creativity scores with an accuracy of 78.4%, which is significantly better than prediction using single modality data (gene or functional connectivity), indicating the importance of combining multi-modality data. Our neuroimaging prediction model built upon the first dataset was cross-validated by a completely new dataset of 98 subjects (r = 0.267, p = 0.0078) with an accuracy of 64.6%. In addition, the creativity–related functional connectivity network we identified in the first dataset was still significantly correlated with the creativity score in the new dataset (p<). In summary, our research demonstrates that strong top-down control versus weak bottom-up processes underlie creativity, which is modulated by competition between the glutamate and GABA neurotransmitter systems. Our work provides the first insights into both the neural and the genetic bases of creativity

Effects of Different Tillage and Fertilization Methods on the Yield and Nitrogen Leaching of Fragrant Rice

Conservation tillage and deep-side fertilization both hold the potential to reduce nitrogen leaching and improve grain yield and nitrogen use efficiency in fragrant rice cultivation practices. However, the combined impact of different tillage practices with deep-side fertilization on nitrogen leaching remains uncertain. Therefore, this study conducted on-site experiments for four rice-growing seasons in both early and late seasons in 2018 and 2019 using the fragrant rice varieties “Meixiangzhan 2” (MX) and “Xiangyaxiangzhan” (XY). The four experimental treatments included the following: conventional tillage with regular fertilization (T1), conventional tillage with simultaneous deep fertilization (T2), reduced tillage with simultaneous deep fertilization (T3), and no-tillage with simultaneous deep fertilization (T4). Our results indicate that the T4 treatment exhibited higher nitrogen leaching rates and potential nitrogen losses throughout the entire rice growth cycle, with a 4.51% increase in total mineral nitrogen leaching (TMNL) and a 1.86% increase in potential nitrogen leaching compared to T1 treatment. In contrast, the T2 treatment demonstrated the lowest nitrogen leaching rate, resulting in a 6.01% reduction in TMNL and a 9.57% decrease in potential nitrogen leaching compared to T1, demonstrating the most optimal performance. It is important to note that a reduction in nitrogen leaching does not directly translate into an increase in rice yield. Our study involved the cultivation of two fragrant rice varieties, ‘Meixiangzhan2’ (MX) and ‘Xiangyaxiangzhan’ (XY), and the results revealed some interesting insights. For MX, the T1 treatment resulted in lower daily grain outputs compared to the other treatments, with disparities ranging from 5.35% to 9.94%. Similarly, for XY, the T1 treatment yielded significantly lower daily grain outputs compared to the other treatments, with discrepancies ranging from 6.26% to 10.81% during the late season of 2019. Therefore, this study suggests that conventional tillage combined with deep fertilizer application can be considered as an effective agricultural strategy to reduce nitrogen leaching and enhance fragrant rice yields