A New Multistage Medical Segmentation Method Based on Superpixel and Fuzzy Clustering

The medical image segmentation is the key approach of image processing for brain MRI images. However, due to the visual complex appearance of image structures and the imaging characteristic, it is still challenging to automatically segment brain MRI image. A new multi-stage segmentation method based on superpixel and fuzzy clustering (MSFCM) is proposed to achieve the good brain MRI segmentation results. The MSFCM utilizes the superpixels as the clustering objects instead of pixels, and it can increase the clustering granularity and overcome the influence of noise and bias effectively. In the first stage, the MRI image is parsed into several atomic areas, namely, superpixels, and a further parsing step is adopted for the areas with bigger gray variance over setting threshold. Subsequently, designed fuzzy clustering is carried out to the fuzzy membership of each superpixel, and an iterative broadcast method based on the Butterworth function is used to redefine their classifications. Finally, the segmented image is achieved by merging the superpixels which have the same classification label. The simulated brain database from BrainWeb site is used in the experiments, and the experimental results demonstrate that MSFCM method outperforms the traditional FCM algorithm in terms of segmentation accuracy and stability for MRI image

MRChexNet: Multi-modal bridge and relational learning for thoracic disease recognition in chest X-rays

While diagnosing multiple lesion regions in chest X-ray (CXR) images, radiologists usually apply pathological relationships in medicine before making decisions. Therefore, a comprehensive analysis of labeling relationships in different data modes is essential to improve the recognition performance of the model. However, most automated CXR diagnostic methods that consider pathological relationships treat different data modalities as independent learning objects, ignoring the alignment of pathological relationships among different data modalities. In addition, some methods that use undirected graphs to model pathological relationships ignore the directed information, making it difficult to model all pathological relationships accurately. In this paper, we propose a novel multi-label CXR classification model called MRChexNet that consists of three modules: a representation learning module (RLM), a multi-modal bridge module (MBM) and a pathology graph learning module (PGL). RLM captures specific pathological features at the image level. MBM performs cross-modal alignment of pathology relationships in different data modalities. PGL models directed relationships between disease occurrences as directed graphs. Finally, the designed graph learning block in PGL performs the integrated learning of pathology relationships in different data modalities. We evaluated MRChexNet on two large-scale CXR datasets (ChestX-Ray14 and CheXpert) and achieved state-of-the-art performance. The mean area under the curve (AUC) scores for the 14 pathologies were 0.8503 (ChestX-Ray14) and 0.8649 (CheXpert). MRChexNet effectively aligns pathology relationships in different modalities and learns more detailed correlations between pathologies. It demonstrates high accuracy and generalization compared to competing approaches. MRChexNet can contribute to thoracic disease recognition in CXR

Semi-Cycled Generative Adversarial Networks for Real-World Face Super-Resolution

Real-world face super-resolution (SR) is a highly ill-posed image restoration task. The fully-cycled Cycle-GAN architecture is widely employed to achieve promising performance on face SR, but prone to produce artifacts upon challenging cases in real-world scenarios, since joint participation in the same degradation branch will impact final performance due to huge domain gap between real-world and synthetic LR ones obtained by generators. To better exploit the powerful generative capability of GAN for real-world face SR, in this paper, we establish two independent degradation branches in the forward and backward cycle-consistent reconstruction processes, respectively, while the two processes share the same restoration branch. Our Semi-Cycled Generative Adversarial Networks (SCGAN) is able to alleviate the adverse effects of the domain gap between the real-world LR face images and the synthetic LR ones, and to achieve accurate and robust face SR performance by the shared restoration branch regularized by both the forward and backward cycle-consistent learning processes. Experiments on two synthetic and two real-world datasets demonstrate that, our SCGAN outperforms the state-of-the-art methods on recovering the face structures/details and quantitative metrics for real-world face SR. The code will be publicly released at https://github.com/HaoHou-98/SCGAN

Features of visibility variation at Great Wall Station, Antarctica

The variation of visibility at Great Wall Station (GWS) was analyzed using manual observational data for the period of 1986 to 2012. Results show that the frequencies of occurrence of high (≥10 km) and low visibility (0―1 km) are 61.0% and 8.0%, respectively. Visibility at GWS shows an evident seasonal variation: The highest visibility between November and March, and the lowest visibility from June to October. Sea fog and precipitation are the main factors for low visibility during summer, whereas frequent adverse weather, such as falling snow, blowing snow, or blizzards, are responsible for low visibility in winter. The frequency of occurrence of low visibility has decreased significantly from 1986 to 2012. Conversely, the frequency of occurrence of high visibility has shown a significant increasing trend, especially during winter. The decreasing tendencies of fog, blowing snow, and snowfall have contributed to the increasing trend of high visibility during winter. Visibility at GWS exhibits significant synoptic-scale (2.1 to 8.3 d), annual, and inter-annual periods (2 a, 4.1 a, and 6.9 a to 8.2 a), among which the most significant period is 4.1 a. The visibility observed during 2012 indicates that instrumental observation can be applied in the continuous monitoring of visibility at GWS

Aberrant intrinsic functional brain topology in methamphetamine-dependent individuals after six-months of abstinence

Our aim was to explore the aberrant intrinsic functional topology in methamphetamine-dependent individuals after six months of abstinence using resting-state functional magnetic imaging (rs-fMRI). Eleven methamphetamines (MA) abstainers who have abstained for six months and eleven healthy controls (HC) were recruited for rs-fMRI examination. The graph theory and functional connectivity (FC) analysis were employed to investigate the aberrant intrinsic functional brain topology between the two groups at multiple levels. Compared with the HC group, the characteristic shortest path length (${L}_{p}$) showed a significant decrease at the global level, while the global efficiency (${E}_{glob}$) and local efficiency (${E}_{loc}$) showed an increase considerably. After FDR correction, we found significant group differences in nodal degree and nodal efficiency at the regional level in the ventral attentional network (VAN), dorsal attentional network (DAN), somatosensory network (SMN), visual network (VN) and default mode network (DMN). In addition, the NBS method presented the aberrations in edge-based FC, including frontoparietal network (FPN), subcortical network (SCN), VAN, DAN, SMN, VN and DMN. Moreover, the FC of large-scale functional brain networks revealed a decrease within the VN and SCN and between the networks. These findings suggest that some functions, e.g., visual processing skills, object recognition and memory, may not fully recover after six months of withdrawal. This leads to the possibility of relapse behavior when confronted with MA-related cues, which may contribute to explaining the relapse mechanism. We also provide an imaging basis for revealing the neural mechanism of MA-dependency after six months of abstinence

Abnormal static and dynamic amplitude of low-frequency fluctuations in multiple brain regions of methamphetamine abstainers

Methamphetamine (meth) addiction is a significant social and public health problem worldwide. The relapse rate of meth abstainers is significantly high, but the underlying physiological mechanisms are unclear. Therefore, in this study, we performed resting-state functional magnetic resonance imaging (rs-fMRI) analysis to detect differences in the spontaneous neural activity between the meth abstainers and the healthy controls, and identify the physiological mechanisms underlying the high relapse rate among the meth abstainers. The fluctuations and time variations in the blood oxygenation level-dependent (BOLD) signal of the local brain activity was analyzed from the pre-processed rs-fMRI data of 11 meth abstainers and 11 healthy controls and estimated the amplitude of low-frequency fluctuations (ALFF) and the dynamic ALFF (dALFF). In comparison with the healthy controls, meth abstainers showed higher ALFF in the anterior central gyrus, posterior central gyrus, trigonal-inferior frontal gyrus, middle temporal gyrus, dorsolateral superior frontal gyrus, and the insula, and reduced ALFF in the paracentral lobule and middle occipital gyrus. Furthermore, the meth abstainers showed significantly reduced dALFF in the supplementary motor area, orbital inferior frontal gyrus, middle frontal gyrus, medial superior frontal gyrus, middle occipital gyrus, insula, middle temporal gyrus, anterior central gyrus, and the cerebellum compared to the healthy controls ( P < 0.05 ). These data showed abnormal spontaneous neural activity in several brain regions related to the cognitive, executive, and other social functions in the meth abstainers and potentially represent the underlying physiological mechanisms that are responsible for the high relapse rate. In conclusion, a combination of ALFF and dALFF analytical methods can be used to estimate abnormal spontaneous brain activity in the meth abstainers and make a more reasonable explanation for the high relapse rate of meth abstainers