Pixel level deep reinforcement learning for accurate and robust medical image segmentation.

Existing deep learning methods have achieved significant success in medical image segmentation. However, this success largely relies on stacking advanced modules and architectures, which has created a path dependency. This path dependency is unsustainable, as it leads to increasingly larger model parameters and higher deployment costs.

High performance of the diffusion magnetic resonance imaging biomarker diffusion-derived 'vessel density' (DDVD) for separating placentas associated with pre-eclampsia from placentas in normal pregnancy.

Background: Diffusion-derived 'vessel density' (DDVD) is a surrogate of the area of micro-vessels per unit tissue. DDVD is calculated according to: DDVD (b0b50) = Sb0/ROIarea0 - Sb50/ROIarea50, where Sb0 and Sb50 refer to the tissue signal when is 0 or 50 s/mm. Due to the complexity of pre-eclampsia (PE), even a combination of risk factors and available tests cannot accurately diagnose or predict PE.

Relationship between glymphatic system dysfunction and cognitive impairment in patients with mild-to-moderate chronic traumatic brain injury: an analysis of the analysis along the perivascular space (ALPS) index.

Background: Cognitive impairment associated with mild-to-moderate chronic traumatic brain injury (TBI) presents substantial challenges for which the functionality of the brain glymphatic system is a key area of interest. This study aimed to explore the functionality of the brain glymphatic system in patients with chronic cognitive impairment following mild-to-moderate TBI using diffusion tensor image analysis along the perivascular space (DTI-ALPS).

Methods: This was a prospective cross-sectional study.

The value of hippocampal sub-region imaging features for the diagnosis and severity grading of ASD in children.

Background: Hippocampal structural changes in Autism Spectrum Disorder (ASD) are inconsistent. This study investigates hippocampal subregion changes in ASD patients to reveal intrinsic hippocampal anomalies.

Methods: A retrospective study from Hainan Children's Hospital database (2020-2023) included ASD patients and matched controls.

Pulmonary melioidosis mimicking lung cancer: a diagnostic challenge.

Background: The overlapping clinical and radiographic features of pulmonary melioidosis and lung cancer present diagnostic challenges to healthcare providers in endemic settings.

Methods: We compared the clinical, laboratory and imaging characteristics of 19 pulmonary melioidosis cases with those of 15 cases of small cell lung cancer (SCLC) and 17 cases of non-small cell lung cancer (NSCLC).

Results: Compared with SCLC/NSCLC cases, those with pulmonary melioidosis were more likely to have diabetes, have fever, neutrophilia and leukocytosis on presentation (p<0.

RIRGAN: An end-to-end lightweight multi-task learning method for brain MRI super-resolution and denoising.

A common problem in the field of deep-learning-based low-level vision medical images is that most of the research is based on single task learning (STL), which is dedicated to solving one of the situations of low resolution or high noise. Our motivation is to design a model that can perform both SR and DN tasks simultaneously, in order to cope with the actual situation of low resolution and high noise in low-level vision medical images. By improving the existing single image super-resolution (SISR) network and introducing the idea of multi-task learning (MTL), we propose an end-to-end lightweight MTL generative adversarial network (GAN) based network using residual-in-residual-blocks (RIR-Blocks) for feature extraction, RIRGAN, which can concurrently accomplish super-resolution (SR) and denoising (DN) tasks.

Assessment of left atrial function and left atrioventricular coupling via cardiac magnetic resonance in individuals with prediabetes and diabetes.

Aims: Assessment of left atrial (LA) function and the left atrioventricular coupling index (LACI) have recently been increasingly recognized as important indices for cardiovascular diseases associated with the presence of prediabetes and diabetes. We aimed to evaluate LA function and the LACI in patients with prediabetes and diabetes via cardiac magnetic resonance (CMR).

Methods: In this retrospective study, we included 35 patients with prediabetes, 32 patients with diabetes, and 84 healthy control participants.

A new computed tomography score-based staging for melioidosis pneumonia to predict progression.

Background: Melioidosis pneumonia, caused by the bacterium , is a serious infectious disease prevalent in tropical regions. Chest computed tomography (CT) has emerged as a valuable tool for assessing the severity and progression of lung involvement in melioidosis pneumonia. However, there persists a need for the quantitative assessment of CT characteristics and staging methodologies to precisely anticipate disease progression.

Aquaporin 1 overexpression may enhance glioma tumorigenesis by interacting with the transcriptional regulation networks of Foxo4, Maz, and E2F families.

Background: The glioblastoma has served as a valuable experimental model system for investigating the growth and invasive properties of glioblastoma. Aquaporin-1 (AQP1) in facilitating cell migration and potentially contributing to tumor progression. In this study, we analyzed the role of AQP1 overexpression in glioblastoma and elucidated the main mechanisms involved.

Cross-domain attention-guided generative data augmentation for medical image analysis with limited data.

Data augmentation is widely applied to medical image analysis tasks in limited datasets with imbalanced classes and insufficient annotations. However, traditional augmentation techniques cannot supply extra information, making the performance of diagnosis unsatisfactory. GAN-based generative methods have thus been proposed to obtain additional useful information to realize more effective data augmentation; but existing generative data augmentation techniques mainly encounter two problems: (i) Current generative data augmentation lacks of the capability in using cross-domain differential information to extend limited datasets.

Multi-scale Bottleneck Residual Network for Retinal Vessel Segmentation.

Precise segmentation of retinal vessels is crucial for the prevention and diagnosis of ophthalmic diseases. In recent years, deep learning has shown outstanding performance in retinal vessel segmentation. Many scholars are dedicated to studying retinal vessel segmentation methods based on color fundus images, but the amount of research works on Scanning Laser Ophthalmoscopy (SLO) images is very scarce.

Accurate and intelligent diagnosis of pediatric pneumonia using X-ray images and blood testing data.

Computer-aided diagnosis (CAD) methods such as the X-rays-based method is one of the cheapest and safe alternative options to diagnose the disease compared to other alternatives such as Computed Tomography (CT) scan, and so on. However, according to our experiments on X-ray public datasets and real clinical datasets, we found that there are two challenges in the current classification of pneumonia: existing public datasets have been preprocessed too well, making the accuracy of the results relatively high; existing models have weak ability to extract features from the clinical pneumonia X-ray dataset. To solve the dataset problems, we collected a new dataset of pediatric pneumonia with labels obtained through a comprehensive pathogen-radiology-clinical diagnostic screening.

μ-Net: Medical image segmentation using efficient and effective deep supervision.

Although the existing deep supervised solutions have achieved some great successes in medical image segmentation, they have the following shortcomings; (i) semantic difference problem: since they are obtained by very different convolution or deconvolution processes, the intermediate masks and predictions in deep supervised baselines usually contain semantics with different depth, which thus hinders the models' learning capabilities; (ii) low learning efficiency problem: additional supervision signals will inevitably make the training of the models more time-consuming. Therefore, in this work, we first propose two deep supervised learning strategies, U-Net-Deep and U-Net-Auto, to overcome the semantic difference problem. Then, to resolve the low learning efficiency problem, upon the above two strategies, we further propose a new deep supervised segmentation model, called μ-Net, to achieve not only effective but also efficient deep supervised medical image segmentation by introducing a tied-weight decoder to generate pseudo-labels with more diverse information and also speed up the convergence in training.

PAC-Net: Multi-pathway FPN with position attention guided connections and vertex distance IoU for 3D medical image detection.

Automatic medical image detection aims to utilize artificial intelligence techniques to detect lesions in medical images accurately and efficiently, which is one of the most important tasks in computer-aided diagnosis (CAD) systems, and can be embedded into portable imaging devices for intelligent Point of Care (PoC) Diagnostics. The Feature Pyramid Networks (FPN) based models are widely used deep-learning-based solutions for automatic medical image detection. However, FPN-based medical lesion detection models have two shortcomings: the object position offset problem and the degradation problem of IoU-based loss.

Artificial intelligence-assisted multistrategy image enhancement of chest X-rays for COVID-19 classification.

Background: The coronavirus disease 2019 (COVID-19) led to a dramatic increase in the number of cases of patients with pneumonia worldwide. In this study, we aimed to develop an AI-assisted multistrategy image enhancement technique for chest X-ray (CXR) images to improve the accuracy of COVID-19 classification.

Methods: Our new classification strategy consisted of 3 parts.

Painless and accurate medical image analysis using deep reinforcement learning with task-oriented homogenized automatic pre-processing.

Pre-processing is widely applied in medical image analysis to remove the interference information. However, the existing pre-processing solutions mainly encounter two problems: (i) it is heavily relied on the assistance of clinical experts, making it hard for intelligent CAD systems to deploy quickly; (ii) due to the personnel and information barriers, it is difficult for medical institutions to conduct the same pre-processing operations, making a deep model that performs well on a specific medical institution difficult to achieve similar performances on the same task in other medical institutions. To overcome these problems, we propose a deep-reinforcement-learning-based task-oriented homogenized automatic pre-processing (DRL-HAPre) framework to overcome these two problems.

Diagnostic Classification of Patients with Dilated Cardiomyopathy Using Ventricular Strain Analysis Algorithm.

Dilated cardiomyopathy (DCM) is a cardiomyopathy with left ventricle or double ventricle enlargement and systolic dysfunction. It is an important cause of sudden cardiac death and heart failure and is the leading indication for cardiac transplantation. Major heart diseases like heart muscle damage and valvular problems are diagnosed using cardiac MRI.

Whole-Body MRI vs. PET/CT for the Detection of Bone Metastases in Patients With Prostate Cancer: A Systematic Review and Meta-Analysis.

A recent meta-analysis in patients with non-small cell lung cancer showed no difference between whole-body magnetic resonance imaging (WBMRI) and positron emission tomography/computed tomography (PET/CT), but no such study is available for prostate cancer (PCa). This study aimed to compare WBMRI and PET/CT for bone metastasis detection in patients with PCa. PubMed, Embase, and the Cochrane library were searched for papers published up to April 2020.

AQP4-siRNA alleviates traumatic brain edema by altering post-traumatic AQP4 polarity reversal in TBI rats.

The spatial and temporal distribution of aquaporin-4 (AQP4) expression in rat brain following brain trauma and AQP4-siRNA treatment, as well as corresponding pathological changes, were studied to explore the mechanism underlying the effect of AQP4-siRNA treatment on traumatic brain injury (TBI). The rats in the sham operation group had normal structure, with AQP4 located in the perivascular end-foot membranes and astrocytic membranes in a polarized pattern. The accelerated polarity reversal was observed in the TBI group in 1-12 h after TBI.

Effect of 5-Line Signs in the Prediction of Staging, Progression, and Prognosis of Peripheral Lung Carcinoma: Preliminary Observation Report.

Background: The single line of the normal interlobar fissure shown on a thin section image can be reconstructed as a 5-line sign on axial maximal intensity projection. The line between the lung nodule and the pleura is called the pleural tail sign on thin image. On the axial maximal intensity projection, it can also be reconstructed as the 5-line sign or fewer than 5 lines.

Carbogen gas-challenge BOLD fMRI in assessment of liver hypoxia after portal microcapsules implantation.

Background: Hypoxia is one of the key factors affecting the survival of islet cells transplanted via the portal vein. Blood oxygen level dependent functional magnetic resonance imaging (BOLD-fMRI) is the only imaging technique that can detect the level of blood oxygen level in vivo. However, so far no study has indicated that BOLD-fMRI can be applied to monitor the liver oxygen level after islet transplantation.

Secreting-lux/pT-ClyA engineered bacteria suppresses tumor growth via interleukin-1β in two pathways.

Engineered Salmonella typhimurium (S.t-ΔpG) and attenuated Salmonella typhimurium (SL: Salmonella typhimurium with a defect in the synthesis of guanine 5'-diphosphate-3'-diphosphate) exhibit similar tumor targeting capabilities (Kim et al. in Theranostics 5:1328-1342, 2015; Jiang et al.

Effects of dexamethasone on C6 cell proliferation, migration and invasion through the upregulation of AQP1.

Dexamethasone (Dex) is commonly used to treat glioma; however, the mechanism underlying the action of Dex remains unclear. In the present study, the hypothesis that aquaporin-1 (AQP1) may participate in tumor cell proliferation, apoptosis, migration and invasion was tested using small interfering RNA (siRNA). The results of the current study indicated that Dex could inhibit the proliferation, in addition to promoting the migration, of C6 cells.

Neuromelioidosis: a series of seven cases in Hainan province, China.

Melioidosis, which is caused by Burkholderia pseudomallei, is predominately a disease of tropical climates and is especially widespread in south-east Asia and northern Australia. Melioidosis affecting the central nervous system has a low incidence but a high mortality. We present seven cases of neuromelioidosis and analyze the disease characteristics and imaging features.