The segmentation of the left ventricle endocardium (LV
) and the left ventricle epicardium (LV
) in echocardiography plays an important role in clinical diagnosis. Recently, deep neural networks have ...been the most commonly used approach for echocardiography segmentation. However, the performance of a well-trained segmentation network may degrade in unseen domain datasets due to the distribution shift of the data. Adaptation algorithms can improve the generalization of deep neural networks to different domains. In this paper, we present a multi-space adaptation-segmentation-joint framework, named MACS, for cross-domain echocardiography segmentation. It adopts a generative adversarial architecture; the generator fulfills the segmentation task and the multi-space discriminators align the two domains on both the feature space and output space. We evaluated the MACS method on two echocardiography datasets from different medical centers and vendors, the publicly available CAMUS dataset and our self-acquired dataset. The experimental results indicated that the MACS could handle unseen domain datasets well, without requirements for manual annotations, and improve the generalization performance by 2.2% in the Dice metric.
Coronary angiography (CAG) as a typical imaging modality for the diagnosis of coronary diseases hasbeen widely employed in clinical practices. For CAG-based computer-aided diagnosis systems, accurate ...vessel segmentation plays a fundamental role. However, patients with bradycardia usually have a pacemaker which frequently interferes the vessel segmentation. In this case, the segmentation of vessels will be hard. To mitigate interferences of pacemakers and then extract main vessels more effectively in CAG images, we propose an approach. At first, a pseudo CAG (pCAG) image is generated through a part of a CAG sequence, in which the pacemaker exists. Then, a local feature descriptor is employed to register the relative location of pacemaker between the pCAG image and the target CAG image. Finally, combining the registration result and segmentation results of main vessels and pacemaker, interferences of pacemaker are removed and the segmentation of main vessels is improved. The proposed method is evaluated based o
Reliable detection of the media-adventitia border (MAB) and the lumen-intima border (LIB) in intravascular ultrasound (IVUS) images remains a challenging task that is of high clinical interest. In ...this paper, we propose a superpixel-wise fuzzy clustering technique modified by edges, followed by level set evolution (SFCME-LSE), for automatic border extraction in 40 MHz IVUS images. The contributions are three-fold. First, the usage of superpixels suppresses the influence of speckle noise in ultrasound images on the clustering results. Second, we propose a region of interest (ROI) assignment scheme to prevent the segmentation from being distracted by pathological structures and artifacts. Finally, the contour is converged towards the target boundary through LSE with an appropriately improved edge indicator. Quantitative evaluations on two IVUS datasets by the Jaccard measure (JM), the percentage of area difference (PAD), and the Hausdorff distance (HD) demonstrate the effectiveness of the proposed SFCME-LSE method. SFCME-LSE achieves the minimal HD of 1.20 ± 0.66 mm and 1.18 ± 0.70 mm for the MAB and LIB, respectively, among several state-of-the-art methods on a publicly available dataset.
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•Influent wastewater changed substrate bacterial community composition seasonally.•16S rRNA, nitrification and denitrification genes were stable between seasons.•Microbial systems ...changed from heterotrophic state to autotrophic state.•Micro- eukaryotes were more sensitive to wastewater fluctuation than bacteria.
How microbial communities respond to wastewater fluctuations is poorly understood. Full-scale surface flow constructed wetlands (SFCWs) were constructed for investigating microbial communities. Results showed that influent wastewater changed sediment bacterial community composition seasonally, indicating that a single bacterial taxonomic group had low resistance (especially, Actinobacteriota and Gammaproteobacteria). However, copy numbers of 16S rRNA, ammonia oxidizing archaea, ammonia oxidizing bacteria, nirS and nirK in the first stage SFCWs were 2.49 × 1010, 3.48 × 109, 5.76 × 106, 8.77 × 108 and 9.06 × 108 g−1 dry sediment, respectively, which remained stable between seasons. Moreover, decreases in the nitrogen concentration in wastewater, changed microbial system state from heterotrophic to autotrophic. Micro-eukaryotic communities were more sensitive to wastewater fluctuations than bacterial communities. Overall, results revealed that microbial communities responded to spatio-temporal fluctuations in wastewater through state changes and species asynchrony. This highlighted complex processes of wastewater treatment by microbial components in SFCWs.
Supervised convolutional neural networks (CNNs) have demonstrated state-of-art performance in medical image segmentation tasks. However, the performance of a well-trained CNN on an independent ...dataset (e.g., different vendors, sequences) relies strongly on the distribution similarity, and may drop unexpectedly in case of distribution shift. To obtain a large amount of annotation from each new dataset for re-training the CNN is expensive and impractical. Adaptation algorithms to improve the CNN generalizability from source domain to target domain has significant practical value. In this work, we propose a highly efficient end-to-end domain adaptation approach, with left ventricle segmentation from cine MRI sequences as an example. We propose to perform domain adaptation in the output space where different domains share the strongest similarities. The core of this algorithm is a flexible and light output adaption module based on adversarial learning. Moreover, Canny edge detector is introduced to enhance model's attention to edges during adversarial learning. Comparative experiments were carried out using images from three major MR vendors (Philips, Siemens, and GE) as three domains. Our results demonstrated that the proposed method substantially improved the generalization of the trained CNN model from one vendor to other vendors without any additional annotation. Moreover, the ablation study proved that introducing Canny edge detector further refined the edge detection in segmentation. The proposed adaption is generic can be extended to other medical image segmentation problems.
A bio-matrix material (BMM) system is used to pretreat swine wastewater and reduce the nitrogen (N) concentration to the tolerance range of plants in constructed wetlands. In this study, rice straw ...(RS), wheat straw (WS), and corn stalk (CS) were applied to treat pollutants from swine wastewater, respectively. This one year-long field experiment make up for the lack of long-term experiments and mechanistic investigations of BMM. The pollutant removal efficiency, degradation process of crop straw, and the abundance of nitrogen cycling genes were determined in different BMM systems. The results showed that the removal efficiency of COD, TN, NH
4
+
, and NO
3
−
was the best in the initial 6 months. Furthermore, RS and WS exhibited favorable annual removal efficiency of TN and NH
4
+
, which were 32.81% and 32.99%, 35.3% and 34.97%, respectively. Moreover, the removal efficiency of COD was 30.81% in three BMM systems. Meanwhile, it was found that the dry matter (DM) degradation of crop straws was fast in the first 4–5 months. The degradation rates of cellulose, hemicellulose, and lignin were 94.19%, 94.36%, and 87.32%, respectively, in 1 year. The abundance of nitrogen cycling genes significantly increased by adding BMM, compared with CK (
P
< 0.05). This showed the abundance of the
hzsB
gene in RS was the highest, while
nirK
,
nirS
, AOA, and AOB were the highest in WS. The addition of RS and WS was better than that of CS in promoting the abundance of nitrogen cycling microorganisms. The results indicated that adding BMM could enhance the anaerobic ammonia oxidation, nitrification, and denitrification. This study not only extends our comprehension of BMM mechanisms in swine wastewater treatment but also serves as a guiding light for numerous farms in similar climate regions.
Purpose
Most published methods directly achieve vessel membrane border detection on cross‐sectional intravascular ultrasound (IVUS) images. The vascular structural continuity that exists in entire ...IVUS image sequences has been overlooked. However, this continuity can have a helpful role in the delineation of vessel membrane contours. To achieve the vessel membrane segmentation more effectively through employing this continuity, a strategy, referred to as multiangle reconstruction, segmentation, and recovery (RSR), is proposed in this paper.
Methods
Four main steps are contained in the multiangle‐RSR: first, a combination of sampling and interpolation is employed to reconstruct long‐axis‐model IVUS frames, in which continuity information becomes available. Second, a clustering algorithm is conducted on long‐axis‐model IVUS frames to roughly extract the media‐adventitia (MA) and lumen‐intima (LI) boundaries. Third, the segmentation results of cross‐sectional IVUS frames are recovered based on the rough results of long‐axis‐model IVUS frames, and an optimization process that combines downsampling, fitting and smoothing is designed to reduce the interference of bifurcation and side vessels.
Results
Multiangle‐RSR is tested on a public dataset, and the Hausdorff distance (HD), Jaccard measure (JM), and percentage of area difference (PAD) are utilized as quantitative evaluation metrics. Mean HDs of 0.34 and 0.29 mm are obtained for MA border detection and LI border detection, respectively, which decrease by 43.3% and 9.4%, respectively, compared with their counterparts in previously published approaches. Furthermore, the mean JM is 0.87 for both MA border detection and LI border detection. The mean PADs of the MA contour extraction and the LI contour extraction are 0.10 and 0.11, respectively.
Conclusion
The results indicate that the proposed strategy effectively introduces vascular structural continuity by reconstructing long‐axis‐model IVUS frames and achieves more precise extraction of MA and LI borders.
Multimodal analysis of placental ultrasound (US) and microflow imaging (MFI) could greatly aid in the early diagnosis and interventional treatment of placental insufficiency (PI), ensuring a normal ...pregnancy. Existing multimodal analysis methods have weaknesses in multimodal feature representation and modal knowledge definitions and fail on incomplete datasets with unpaired multimodal samples. To address these challenges and efficiently leverage the incomplete multimodal dataset for accurate PI diagnosis, we propose a novel graph-based manifold regularization learning (MRL) framework named GMRLNet. It takes US and MFI images as input and exploits their modality-shared and modality-specific information for optimal multimodal feature representation. Specifically, a graph convolutional-based shared and specific transfer network (GSSTN) is designed to explore intra-modal feature associations, thus decoupling each modal input into interpretable shared and specific spaces. For unimodal knowledge definitions, graph-based manifold knowledge is introduced to describe the sample-level feature representation, local inter-sample relations, and global data distribution of each modality. Then, an MRL paradigm is designed for inter-modal manifold knowledge transfer to obtain effective cross-modal feature representations. Furthermore, MRL transfers the knowledge between both paired and unpaired data for robust learning on incomplete datasets. Experiments were conducted on two clinical datasets to validate the PI classification performance and generalization of GMRLNet. State-of-the-art comparisons show the higher accuracy of GMRLNet on incomplete datasets. Our method achieves 0.913 AUC and 0.904 balanced accuracy (bACC) for paired US and MFI images, as well as 0.906 AUC and 0.888 bACC for unimodal US images, illustrating its application potential in PI CAD systems.
The poor generalizability of intravascular ultrasound (IVUS) analysis methods caused by the great diversity of IVUS datasets is hopefully addressed by the domain adaptation strategy. However, ...existing domain adaptation models underperform in intravascular structural preservation, because of the complex pathology and low contrast in IVUS images. Losing structural information during the domain adaptation would lead to inaccurate analyses of vascular states. In this paper, we propose a Multilevel Structure-Preserved Generative Adversarial Network (MSP-GAN) for transferring IVUS domains while maintaining intravascular structures. On the generator-discriminator baseline, the MSP-GAN integrates the transformer, contrastive restraint, and self-ensembling strategy, for effectively preserving structures in multi-levels, including global, local, and fine levels. For the global-level pathology maintenance, the generator explores long-range dependencies in IVUS images via an incorporated vision transformer. For the local-level anatomy consistency, a region-to-region correspondence is forced between the translated and source images via a superpixel-wise multiscale contrastive (SMC) constraint. For reducing distortions of fine-level structures, a self-ensembling mean teacher generates the pixel-wise pseudo-label and restricts the translated image via an uncertainty-aware teacher-student consistency (TSC) constraint. Experiments were conducted on 20 MHz and 40 MHz IVUS datasets from different medical centers. Ablation studies illustrate that each innovation contributes to intravascular structural preservation. Comparisons with representative domain adaptation models illustrate the superiority of the MSP-GAN in the structural preservation. Further comparisons with the state-of-the-art IVUS analysis accuracy demonstrate that the MSP-GAN is effective in enlarging the generalizability of diverse IVUS analysis methods and promoting accurate vessel and lumen segmentation and stenosis-related parameter quantification.
•We provide a method to transfer IVUS domains while preserving intravascular structures.•Making data-specific analysis methods attain state-of-the-art accuracy on domain-different IVUS datasets.•Incorporating vision transformer, contrastive learning and mean-teacher maintains multilevel structures.
. Automatic extraction of external elastic membrane border (EEM) and lumen-intima border (LIB) in intravascular ultrasound (IVUS) sequences aids atherosclerosis diagnosis. Existing IVUS segmentation ...networks ignored longitudinal relations among sequential images and neglected that IVUS images of different vascular conditions vary largely in intricacy and informativeness. As a result, they suffered from performance degradation in complicated parts in IVUS sequences.
. In this paper, we develop a 3D Pyramidal Densely-connected Network (PDN) with Adaptive learning and post-Correction guided by a novel cross-frame uncertainty (CFU). The proposed method is named PDN-AC. Specifically, the PDN enables the longitudinal information exploitation and the effective perception of size-varied vessel regions in IVUS samples, by pyramidally connecting multi-scale 3D dilated convolutions. Additionally, the CFU enhances the robustness of the method to complicated pathology from the frame-level (f-CFU) and pixel-level (p-CFU) via exploiting cross-frame knowledge in IVUS sequences. The f-CFU weighs the complexity of IVUS frames and steers an adaptive sampling during the PDN training. The p-CFU visualizes uncertain pixels probably misclassified by the PDN and guides an active contour-based post-correction.
. Human and animal experiments were conducted on IVUS datasets acquired from atherosclerosis patients and pigs. Results showed that the f-CFU weighted adaptive sampling reduced the Hausdorff distance (HD) by 10.53%/7.69% in EEM/LIB detection. Improvements achieved by the p-CFU guided post-correction were 2.94%/5.56%.
. The PDN-AC attained mean Jaccard values of 0.90/0.87 and HD values of 0.33/0.34 mm in EEM/LIB detection, preferable to state-of-the-art IVUS segmentation methods.
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