FCP-Net: A Feature-Compression-Pyramid Network Guided by Game-Theoretic Interactions for Medical Image Segmentation

Medical image segmentation is a crucial step in diagnosis and analysis of diseases for clinical applications. Deep neural network methods such as DeepLabv3+ have successfully been applied for medical image segmentation, but multi-level features are seldom integrated seamlessly into different attention mechanisms, and few studies have explored the interactions between medical image segmentation and classification tasks. Herein, we propose a feature-compression-pyramid network (FCP-Net) guided by game-theoretic interactions with a hybrid loss function (HLF) for the medical image segmentation. The proposed approach consists of segmentation branch, classification branch and interaction branch. In the encoding stage, a new strategy is developed for the segmentation branch by applying three modules, e.g., embedded feature ensemble, dilated spatial mapping and channel attention (DSMCA), and branch layer fusion. These modules allow effective extraction of spatial information, efficient identification of spatial correlation among various features, and fully integration of multireceptive field features from different branches. In the decoding stage, a DSMCA module and a multi-scale feature fusion module are used to establish multiple skip connections for enhancing fusion features. Classification and interaction branches are introduced to explore the potential benefits of the classification information task to the segmentation task. We further explore the interactions of segmentation and classification branches from a game theoretic view, and design an HLF. Based on this HLF, the segmentation, classification and interaction branches can collaboratively learn and teach each other throughout the training process, thus applying the conjoint information between the segmentation and classification tasks and improving the generalization performance. The proposed model has been evaluated using several datasets, including ISIC2017, ISIC2018, REFUGE, Kvasir-SEG, BUSI, and PH2, and the results prove its competitiveness compared with other state-of-the-art techniques

Flexible thin-film acoustic wave devices with off-axis bending characteristics for multisensing applications

Flexible surface acoustic wave (SAW) devices have recently attracted tremendous attention for their widespread applications in sensing and microfluidics. However, for these applications, the SAW devices often need to be bent into off-axis deformations between the acoustic-wave propagation direction and bending direction. Currently there are few studies on this topic, and the bending mechanisms under off-axis bending deformations have remained unexplored for multi-sensing applications. Herein, we fabricated aluminum nitride (AlN) flexible SAW devices by using high quality AlN films deposited on flexible glass substrates and systematically investigated their complex deformation behaviors. A theoretical model was firstly developed using coupling wave equations and boundary condition method to analyze the device’s characteristics with bending and off-axis deformation under elastic strains. The relationships between frequency shifts of the SAW device with bending strain and off-axis angle were obtained which showed the identical results with those from the theoretical calculations. Finally, we performed proof-of-concept demonstrations of multi-sensing applications by monitoring human wrist movements at various off-axis angles and detecting UV light intensities on a curved surface, thus paving the ways for versatile flexible electronics applications

High Performance Acoustic Wave Nitrogen Dioxide Sensor with Ultraviolet Activated 3D Porous Architecture of Ag-Decorated Reduced Graphene Oxide and Polypyrrole Aerogel

Surface acoustic wave (SAW) devices have been widely explored for real-time monitoring of toxic and irritant chemical gases such as nitrogen oxide (NO2), but they often have issues such as a complicated process of the sensing layer, low sensitivity, long response time, irreversibility, and/or requirement of high temperatures to enhance sensitivity. Herein, we report a sensing material design for room-temperature NO2 detection based on a 3D porous architecture of Ag-decorated reduced graphene oxide-polypyrrole hybrid aerogels (rGO-PPy/Ag) and apply UV activation as an effective strategy to further enhance the NO2 sensing performance. The rGO-PPy/Ag-based SAW sensor with the UV activation exhibits high sensitivity (127.68 Hz/ppm), fast response/recovery time (36.7 s/58.5 s), excellent reproducibility and selectivity, and fast recoverability. Its enhancement mechanisms for highly sensitive and selective detection of NO2 are based on a 3D porous architecture, Ag-decorated rGO-PPy, p-p heterojunction in rGO-PPy/Ag, and UV photogenerated carriers generated in the sensing layer. The scientific findings of this work will provide the guidance for future exploration of next-generation acoustic-wave-based gas sensors

Development and validation of a radiomics signature for clinically significant portal hypertension in cirrhosis (CHESS1701): a prospective multicenter studyResearch in context

Clinically significant portal hypertension (CSPH) is associated with an incremental risk of esophageal varices and overt clinical decompensations. However, hepatic venous pressure gradient (HVPG) measurement, the gold standard for defining CSPH (HVPG≥10 mm Hg) is invasive and therefore not suitable for routine clinical practice. This study aims to develop and validate a radiomics-based model as a noninvasive method for accurate detection of CSPH in cirrhosis.The prospective multicenter diagnostic trial (CHESS1701, ClinicalTrials.gov identifier: NCT03138915) involved 385 patients with cirrhosis from five liver centers in China between August 2016 and September 2017. Patients who had both HVPG measurement and contrast-enhanced CT within 14 days prior to the catheterization were collected. The noninvasive radiomics model, termed rHVPG for CSPH was developed based on CT images in a training cohort consisted of 222 consecutive patients and the diagnostic performance was prospectively assessed in 163 consecutive patients in four external validation cohorts.rHVPG showed a good performance in detection of CSPH with a C-index of 0·849 (95%CI: 0·786–0·911). Application of rHVPG in four external prospective validation cohorts still gave excellent performance with the C-index of 0·889 (95%CI: 0·752–1·000, 0·800 (95%CI: 0·614–0·986), 0·917 (95%CI: 0·772–1·000), and 0·827 (95%CI: 0·618–1·000), respectively. Intraclass correlation coefficients for inter- and intra-observer agreement were 0·92–0·99 and 0·97–0·99, respectively.A radiomics signature was developed and prospectively validated as an accurate method for noninvasive detection of CSPH in cirrhosis. The tool of rHVPG assessment can facilitate the identification of CSPH rapidly when invasive transjugular procedure is not available. Keywords: Portal hypertension, Liver cirrhosis, Hepatic venous pressure gradient, Radiomics, Noninvasiv