PCF-Net: Position and context information fusion attention convolutional neural network for skin lesion segmentation.

Skin lesion segmentation is a crucial step in the process of skin cancer diagnosis and treatment. The variation in position, shape, size and edges of skin lesion areas poses a challenge for accurate segmentation of skin lesion areas through dermoscopic images. To meet these challenges, in this paper, using UNet as the baseline model, a convolutional neural network based on position and context information fusion attention is proposed, called PCF-Net. A novel two-branch attention mechanism is designed to aggregate Position and Context information, called Position and Context Information Aggregation Attention Module (PCFAM). A global context information complementary module (GCCM) was developed to obtain long-range dependencies. A multi-scale grouped dilated convolution feature extraction module (MSEM) was proposed to capture multi-scale feature information and place it in the bottleneck of UNet. On the ISIC2018 dataset, a large volume of ablation experiments demonstrated the superiority of PCF-Net for dermoscopic image segmentation after adding PCFAM, GCCM and MSEM. Compared with other state-of-the-art methods, the performance of PCF-Net achieves a competitive result in all metrics.

iU-Net: a hybrid structured network with a novel feature fusion approach for medical image segmentation.

In recent years, convolutional neural networks (CNNs) have made great achievements in the field of medical image segmentation, especially full convolutional neural networks based on U-shaped structures and skip connections. However, limited by the inherent limitations of convolution, CNNs-based methods usually exhibit limitations in modeling long-range dependencies and are unable to extract large amounts of global contextual information, which deprives neural networks of the ability to adapt to different visual modalities. In this paper, we propose our own model, which is called iU-Net bacause its structure closely resembles the combination of i and U. iU-Net is a multiple encoder-decoder structure combining Swin Transformer and CNN. We use a hierarchical Swin Transformer structure with shifted windows as the primary encoder and convolution as the secondary encoder to complement the context information extracted by the primary encoder. To sufficiently fuse the feature information extracted from multiple encoders, we design a feature fusion module (W-FFM) based on wave function representation. Besides, a three branch up sampling method(Tri-Upsample) has developed to replace the patch expand in the Swin Transformer, which can effectively avoid the Checkerboard Artifacts caused by the patch expand. On the skin lesion region segmentation task, the segmentation performance of iU-Net is optimal, with Dice and Iou reaching 90.12% and 83.06%, respectively. To verify the generalization of iU-Net, we used the model trained on ISIC2018 dataset to test on PH2 dataset, and achieved 93.80% Dice and 88.74% IoU. On the lung feild segmentation task, the iU-Net achieved optimal results on IoU and Precision, reaching 98.54% and 94.35% respectively. Extensive experiments demonstrate the segmentation performance and generalization ability of iU-Net.

Dermoscopic image segmentation based on Pyramid Residual Attention Module.

We propose a stacked convolutional neural network incorporating a novel and efficient pyramid residual attention (PRA) module for the task of automatic segmentation of dermoscopic images. Precise segmentation is a significant and challenging step for computer-aided diagnosis technology in skin lesion diagnosis and treatment. The proposed PRA has the following characteristics: First, we concentrate on three widely used modules in the PRA. The purpose of the pyramid structure is to extract the feature information of the lesion area at different scales, the residual means is aimed to ensure the efficiency of model training, and the attention mechanism is used to screen effective features maps. Thanks to the PRA, our network can still obtain precise boundary information that distinguishes healthy skin from diseased areas for the blurred lesion areas. Secondly, the proposed PRA can increase the segmentation ability of a single module for lesion regions through efficient stacking. The third, we incorporate the idea of encoder-decoder into the architecture of the overall network. Compared with the traditional networks, we divide the segmentation procedure into three levels and construct the pyramid residual attention network (PRAN). The shallow layer mainly processes spatial information, the middle layer refines both spatial and semantic information, and the deep layer intensively learns semantic information. The basic module of PRAN is PRA, which is enough to ensure the efficiency of the three-layer architecture network. We extensively evaluate our method on ISIC2017 and ISIC2018 datasets. The experimental results demonstrate that PRAN can obtain better segmentation performance comparable to state-of-the-art deep learning models under the same experiment environment conditions.

MTPA_Unet: Multi-Scale Transformer-Position Attention Retinal Vessel Segmentation Network Joint Transformer and CNN.

Retinal vessel segmentation is extremely important for risk prediction and treatment of many major diseases. Therefore, accurate segmentation of blood vessel features from retinal images can help assist physicians in diagnosis and treatment. Convolutional neural networks are good at extracting local feature information, but the convolutional block receptive field is limited. Transformer, on the other hand, performs well in modeling long-distance dependencies. Therefore, in this paper, a new network model MTPA_Unet is designed from the perspective of extracting connections between local detailed features and making complements using long-distance dependency information, which is applied to the retinal vessel segmentation task. MTPA_Unet uses multi-resolution image input to enable the network to extract information at different levels. The proposed TPA module not only captures long-distance dependencies, but also focuses on the location information of the vessel pixels to facilitate capillary segmentation. The Transformer is combined with the convolutional neural network in a serial approach, and the original MSA module is replaced by the TPA module to achieve finer segmentation. Finally, the network model is evaluated and analyzed on three recognized retinal image datasets DRIVE, CHASE DB1, and STARE. The evaluation metrics were 0.9718, 0.9762, and 0.9773 for accuracy; 0.8410, 0.8437, and 0.8938 for sensitivity; and 0.8318, 0.8164, and 0.8557 for Dice coefficient. Compared with existing retinal image segmentation methods, the proposed method in this paper achieved better vessel segmentation in all of the publicly available fundus datasets tested performance and results.

SwinBTS: A Method for 3D Multimodal Brain Tumor Segmentation Using Swin Transformer.

Brain tumor semantic segmentation is a critical medical image processing work, which aids clinicians in diagnosing patients and determining the extent of lesions. Convolutional neural networks (CNNs) have demonstrated exceptional performance in computer vision tasks in recent years. For 3D medical image tasks, deep convolutional neural networks based on an encoder-decoder structure and skip-connection have been frequently used. However, CNNs have the drawback of being unable to learn global and remote semantic information well. On the other hand, the transformer has recently found success in natural language processing and computer vision as a result of its usage of a self-attention mechanism for global information modeling. For demanding prediction tasks, such as 3D medical picture segmentation, local and global characteristics are critical. We propose SwinBTS, a new 3D medical picture segmentation approach, which combines a transformer, convolutional neural network, and encoder-decoder structure to define the 3D brain tumor semantic segmentation job as a sequence-to-sequence prediction challenge in this research. To extract contextual data, the 3D Swin Transformer is utilized as the network's encoder and decoder, and convolutional operations are employed for upsampling and downsampling. Finally, we achieve segmentation results using an improved Transformer module that we built for increasing detail feature extraction. Extensive experimental results on the BraTS 2019, BraTS 2020, and BraTS 2021 datasets reveal that SwinBTS outperforms state-of-the-art 3D algorithms for brain tumor segmentation on 3D MRI scanned images.

Influence of solution pH on degradation of atrazine during UV and UV/H2O2 oxidation: kinetics, mechanism, and degradation pathways.

The kinetics, degradation mechanism and degradation pathways of atrazine (ATZ) during sole-UV and UV/H2O2 processes under various pH conditions were investigated; the effects of UV irradiation time and H2O2 dose were also evaluated. A higher reaction rate was observed under neutral pH conditions in the UV only process. For the UV/H2O2 process, a higher reaction rate was observed in acidic solution and the degradation rate of ATZ firstly increased with the increase of concentration of H2O2 and then decreased when H2O2 concentration exceeded 5 mg L-1. In addition, qualitative and quantitative analyses of oxidation intermediates of ATZ in aqueous solution during the sole-UV and UV/H2O2 processes were conducted using UPLC-ESI-MS/MS. Ten kinds of dechlorinated intermediates were detected during sole-UV treatment under all five pH conditions. In contrast, the speciation of intermediates in the UV/H2O2 process varied dramatically with solution pH. Based on the analysis of ATZ oxidation intermediates, ATZ degradation pathways under different pH conditions were proposed for the sole-UV and UV/H2O2 processes. The results showed that the main degradation reactions of ATZ included dechlorination-hydroxylation, dechlorination-dealkylation, de-alkylation, deamination-hydroxylation, alkylic-oxidation of lateral chains, dehydrogenation-olefination, dechlorination-hydrogenation, dechlorination-methoxylation and dehydroxylation.

Influence of genetic and non-genetic factors on the plasma concentrations of the clopidogrel metabolite (SR26334) among Chinese patients.

The objective of this study was to investigate the contribution of genetic polymorphism of cytochrome P450 2C19 gene (CYP2C19) and non-genetic factors to clopidogrel in Chinese patients by using the plasma concentrations of SR26334 as a surrogate. A total of 150 patients who received clopidogrel therapy were enrolled in this study. Genotyping was carried out to identify the alleles of CYP2C19*2 and CYP2C19*3 by using PCR-based restriction enzyme tests. The plasma concentrations of SR26334 were determined using a rapid LC method with UV detection. CYP2C19 genotyping showed that 68 patients were extensive metabolizers (EMs), 68 were intermediate metabolizers (IMs) and 14 were poor metabolizers (PMs). Multiple linear regression models incorporating genetic polymorphism of CYP2C19 and non-genetic factors, such as blood collection time, smoking status and clopidogrel doses were developed, and explained up to 63.1% of the total variation (adjusted R(2) of 0.631) in the plasma concentrations of SR26334 in Chinese patients. Blood collection time, smoking status, genetic polymorphism of CYP2C19 and clopidogrel doses were found to affect the plasma concentrations of SR26334 significantly.