Automatic Image Segmentation Research Articles

Investigation of radiomics models for predicting biochemical recurrence of advanced prostate cancer on pretreatment MR ADC maps based on automatic image segmentation.

To develop radiomics models based on automatic segmentation of the pretreatment apparent diffusion coefficient (ADC) maps for predicting the biochemical recurrence (BCR) of advanced prostate cancer (PCa). A total of 100 cases with pathologically confirmed PCa were retrospectively included in this study. These cases were randomly divided into training (n=70) and test (n=30) datasets. Two predictive models were constructed based on the combination of age, prostate specific antigen (PSA) level, Gleason score, and clinical staging before therapy and the prostate area (Model_1) or PCa area (Model_2). Another two predictive models were constructed based on only prostate area (Model_3) or PCa area (Model_4). The area under the receiver operating characteristic curve (ROC AUC) and precision-recall (PR) curve analysis were used to analyze the models' performance. Sixty-five patients without BCR (BCR-) and 35 patients with BCR (BCR+) were confirmed. The age, PSA, volume, diameter and ADC value of the prostate and PCa were not significantly different between the BCR- and BCR+ groups or between the training and test datasets (all p>0.05). The AUCs were 0.637 (95% CI: 0.434-0.838), 0.841 (95% CI: 0.695-0.940), 0.840 (95% CI: 0.698-0.983), and 0.808 (95% CI: 0.627-0.988) for Model_1 to Model_4 in the test dataset without significant difference. The 95% bootstrap confidence intervals for the areas under the PR curve of the four models were not statistically different. The radiomics models based on automatically segmented prostate and PCa areas on the pretreatment ADC maps developed in our study can be promising in predicting BCR of advanced PCa.

Evaluating synthetic neuroimaging data augmentation for automatic brain tumour segmentation with a deep fully-convolutional network

Gliomas observed in medical images require expert neuro-radiologist evaluation for treatment planning and monitoring, motivating development of intelligent systems capable of automating aspects of tumour evaluation. Deep learning models for automatic image segmentation rely on the amount and quality of training data. In this study we developed a neuroimaging synthesis technique to augment data for training fully-convolutional networks (U-nets) to perform automatic glioma segmentation. We used StyleGAN2-ada to simultaneously generate fluid-attenuated inversion recovery (FLAIR) magnetic resonance images and corresponding glioma segmentation masks. Synthetic data were successively added to real training data (n = 2751) in fourteen rounds of 1000 and used to train U-nets that were evaluated on held-out validation (n = 590) and test sets (n = 588). U-nets were trained with and without geometric augmentation (translation, zoom and shear), and Dice coefficients were computed to evaluate segmentation performance. We also monitored the number of training iterations before stopping, total training time, and time per iteration to evaluate computational costs associated with training each U-net. Synthetic data augmentation yielded marginal improvements in Dice coefficients (validation set +0.0409, test set +0.0355), whereas geometric augmentation improved generalization (standard deviation between training, validation and test set performances of 0.01 with, and 0.04 without geometric augmentation). Based on the modest performance gains for automatic glioma segmentation we find it hard to justify the computational expense of developing a synthetic image generation pipeline. Future work may seek to optimize the efficiency of synthetic data generation for augmentation of neuroimaging data.

New Artificial Intelligence Algorithms in Coronary CT Angiography

Artificial intelligence (AI) has been developed in coronary CT in many fields, such as image reconstruction, automatic calcium scoring calculation, cardio-vascular risk prediction, automatic image segmentation, automatic plaque quantification and now also image interpretation, including stenosis detection and ischemia prediction. New AI algorithms might facilitate the use of Coronary CT from acquisition to interpretation. These tools should be soon be available for daily use.

CPFTransformer: transformer fusion context pyramid medical image segmentation network.

The application of U-shaped convolutional neural network (CNN) methods in medical image segmentation tasks has yielded impressive results. However, this structure's single-level context information extraction capability can lead to problems such as boundary blurring, so it needs to be improved. Additionally, the convolution operation's inherent locality restricts its ability to capture global and long-distance semantic information interactions effectively. Conversely, the transformer model excels at capturing global information. Given these considerations, this paper presents a transformer fusion context pyramid medical image segmentation network (CPFTransformer). The CPFTransformer utilizes the Swin Transformer to integrate edge perception for segmentation edges. To effectively fuse global and multi-scale context information, we introduce an Edge-Aware module based on a context pyramid, which specifically emphasizes local features like edges and corners. Our approach employs a layered Swin Transformer with a shifted window mechanism as an encoder to extract contextual features. A decoder based on a symmetric Swin Transformer is employed for upsampling operations, thereby restoring the resolution of feature maps. The encoder and decoder are connected by an Edge-Aware module for the extraction of local features such as edges and corners. Experimental evaluations on the Synapse multi-organ segmentation task and the ACDC dataset demonstrate the effectiveness of our method, yielding a segmentation accuracy of 79.87% (DSC) and 20.83% (HD) in the Synapse multi-organ segmentation task. The method proposed in this paper, which combines the context pyramid mechanism and Transformer, enables fast and accurate automatic segmentation of medical images, thereby significantly enhancing the precision and reliability of medical diagnosis. Furthermore, the approach presented in this study can potentially be extended to image segmentation of other organs in the future.

Railway Remote Sensing Image Segmentation Technology in Big Data-oriented Urban Rail Transit Safety Evaluation System

This paper discusses the application of satellite remote sensing images in urban rail transit safety evaluation. First of all, the article expounds the significance of satellite remote sensing images in the rail transit safety evaluation system, which can provide the overall information of the rail transit network, be used for traffic flow analysis, land use monitoring, etc., and provide a basis for planning decision-making. Then, the article introduces the application of artificial intelligence technology in the segmentation of railway remote sensing images. The deep learning model can realize automatic and efficient image segmentation and improve the segmentation accuracy. Then, the article designs a railway remote sensing image segmentation system based on PaddleSeg, and uses Google Earth remote sensing images for training and testing. Finally, the article analyzes the role of U-Net in the railway segmentation task, and the reflection of different evaluation indicators on the performance of the model. Research shows that artificial intelligence technology can greatly improve the segmentation efficiency and accuracy of railway remote sensing images, and provide strong support for rail transit safety evaluation.

Hand bone extraction and segmentation based on a convolutional neural network

Automatic segmentation of hand bone X-ray images is an important process in skeletal bone age assessment (BAA). The segmentation process is extremely difficult and complicated due to the complex structure and tiny size of hand bone features, which may lead to inaccurate feature localization and incomplete segmentation. A two-stage segmentation method is proposed in this paper. In the first stage, the OSA-YOLOv5 network is used to extract hand bones, and in the second stage, GRU-UNet is used to separate extracted hand bones. The hand bone X-ray dataset consists of a mixed dataset of 1000 left-hand bone X-ray images of adolescents from licenced local hospitals and 1000 left-hand bone X-ray images from the RSNA public dataset. The training efficiency of the OSA-YOLOv5 network is 28 % higher than that of the traditional network, and the proposed network exhibits with a higher extraction accuracy. The segmentation accuracy of the GRU-Unet segmentation model 14.70 % higher than that if Unet. The spatial sequence features with spatial rules are found to have a positive correlation in GRU-Unet. Experimental results show that the proposed segmentation method has good generalization and robustness, which provides a good foundation for subsequent BAA.

LMNS-Net: Lightweight Multiscale Novel Semantic-Net deep learning approach used for automatic pancreas image segmentation in CT scan images

LMNS-Net: Lightweight Multiscale Novel Semantic-Net deep learning approach used for automatic pancreas image segmentation in CT scan images

Deep learning muscle segmentation model for CT images in DICOM format

This work solves the problem of automatic segmentation of medical images in DICOM format using machine learning methods. A new developed tool is used in the form of a separate module for labeling medical data in the DICOM format. The trained model, proposed in the paper, can be useful in the tasks of muscle segmentation. One can apply it in different ways, but some of the most common include assessment of diseases related to muscles, and sarcopenia is one of them. The further applications of the muscle segmentation model may include examining various medical cases with patients, that tend to have muscle-related diseases. For instance, detecting cachexia may be one of the extensions of the model’s application field.

Automatic image segmentation and online survival prediction model of medulloblastoma based on machine learning.

To develop a dynamic nomogram containing radiomics signature and clinical features for estimating the overall survival (OS) of patients with medulloblastoma (MB) and design an automatic image segmentation model to reduce labor and time costs. Data from 217 medulloblastoma (MB) patients over the past 4years were collected and separated into a training set and a test set. Intraclass correlation coefficient (ICC), random survival forest (RSF), and least absolute shrinkage and selection operator (LASSO) regression methods were employed to select variables in the training set. Univariate and multivariate Cox proportional hazard models, as well as Kaplan-Meier analysis, were utilized to determine the relationship among the radiomics signature, clinical features, and overall survival. A dynamic nomogram was developed. Additionally, a 3D-Unet deep learning model was used to train the automatic tumor delineation model. Higher Rad-scores were significantly associated with worse OS in both the training and validation sets (p < 0.001 and p = 0.047, respectively). The Cox model combined clinical and radiomics signatures ([IBS = 0.079], [C-index = 0.747, SE = 0.045]) outperformed either radiomics signatures alone ([IBS = 0.081], [C-index = 0.738, SE = 0.041]) or clinical features alone ([IBS = 0.085], [C-index = 0.565, SE = 0.041]). The segmentation model had mean Dice coefficients of 0.80, 0.82, and 0.78 in the training, validation, and test sets respectively. A deep learning-based tumor segmentation model was built with Dice coefficients of 0.8372, 0.8017, and 0.7673 on the training set, validation set, and test set, respectively. A combination of radiomics features and clinical characteristics enhances the accuracy of OS prediction in medulloblastoma patients. Additionally, building an MRI image automatic segmentation model reduces labor and time costs. A survival prognosis model based on radiomics and clinical characteristics could improve the accuracy of prognosis estimation for medulloblastoma patients, and an MRI-based automatic tumor segmentation model could reduce the cost of time. • A model that combines radiomics and clinical features can predict the survival prognosis of patients with medulloblastoma. • Online nomogram and image automatic segmentation model can help doctors better judge the prognosis of medulloblastoma and save working time. • The developed AI system can help doctors judge the prognosis of diseases and promote the development of precision medicine.

Deep learning image segmentation for the reliable porosity measurement of high-capacity Ni-based oxide cathode secondary particles

The optimization of geometrical pore control in high-capacity Ni-based cathode materials is required to enhance the cyclic performance of lithium-ion batteries. Enhanced porosity improves lithium-ion mobility by increasing the electrode–electrolyte contact area and reducing the number of ion diffusion pathways. However, excessive porosity can diminish capacity, thus necessitating optimizing pore distribution to compromise the trade-off relation. Accordingly, a statistically meaningful porosity estimation of electrode materials is required to engineer the local pore distribution inside the electrode particles. Conventional scanning electron microscopy (SEM) image-based porosity measurement can be used for this purpose. However, it is labor-intensive and subjected to human bias for low-contrast pore images, thereby potentially lowering measurement accuracy. To mitigate these difficulties, we propose an automated image segmentation method for the reliable porosity measurement of cathode materials using deep convolutional neural networks specifically trained for the analysis of porous cathode materials. Combined with the preprocessed SEM image datasets, the model trained for 100 epochs exhibits an accuracy of > 97% for feature segmentation with regard to pore detection on the input datasets. This automated method considerably reduces manual effort and human bias related to the digitization of pore features in serial section SEM image datasets used in 3D electron tomography.Graphical abstract

Estimating ground surface visibility on thermal images from drone wildlife surveys in forests

A thermal drone – an unmanned aerial vehicle equipped with a thermal camera – is a new tool in wildlife research. It enables effective surveys of animal species difficult to monitor with conventional methods. Over open terrain the detection of warm-blooded large animals with a thermal drone is nearly certain. However, in forest surveys, individuals hidden under the tree canopy may remain undetected which can lead to biased estimates of population size. Thus, it is important to estimate the percentage of surveyed area not obscured by tree canopy where animals can be easily detected. This figure may be used as a simple correction factor for animal counts from thermal images (thermograms) or as one of the covariates in more advanced statistical models of population density. We tested four methods of automatic image segmentation: 1) Otsu global thresholding, 2) local thresholding with Gaussian kernel, 3) Chan-Vese active contour model without edges, and 4) logistic regression and assessed each of their performances in differentiating between canopy and forest floor on a sample of 100 thermograms acquired with a drone during wildlife surveys in a range of forest habitats. We used segmentation results to calculate the estimated percentage of visible ground surface on images and compared it to the percentages assessed by manual classification. The Otsu global thresholding gave results that were in best agreement with the manual classification, had the fastest computing time and was superior to the other three segmentation methods. Visual inspection of the Otsu segmentation results showed that this method accurately separates tree canopy and ground surface on thermograms obtained across all forest types as well as in partially open areas. However, landscape features such as roads or water bodies that have a temperature similar to that of trees may cause segmentation errors and lead to underestimation of the ground visibility. While the use of Otsu segmentation to automatically assess percentage of visible ground surface on thermal images from drone surveys has certain limitations, the method is fast and can be readily applied by wildlife researchers. This approach can aid in correcting bias in estimates of animal population density and contribute to a better understanding of the use of thermal drones for wildlife surveys in forests.

Automatic image segmentation model for indirect land use change with deep convolutional neural network

Automatic image segmentation model for indirect land use change with deep convolutional neural network

A New Image Segmentation Method Based on the YOLO5 and Fully Connected CRF

When manually polishing blades, skilled workers can quickly machine a blade by observing the characteristics of the polishing sparks. To help workers better recognize spark images, we used an industrial charge-coupled device (CCD) camera to capture the spark images. Firstly, the spark image region detected by yolo5, then segment from the background. Secondly, the target region was further segmented and refined in a fully connected conditional random field (CRF), from which the complete spark image obtained. Experimental results showed that this method could quickly and accurately segment whole spark image. The test results showed that this method was better than other image segmentation algorithms. Our method could better segment irregular image, improve recognition and segmentation efficiency of spark image, achieve automatic image segmentation, and replace human observation.

A few-shot learning-based ischemic stroke segmentation system using weighted MRI fusion

Stroke, particularly ischemic stroke, is a major cause of disability and one of the leading causes of adult mortality worldwide. Early and prompt management of stroke patients can reduce the severity of the disease. Doctors usually determine the severity of a stroke by focusing on the region of interest (ROI) in the MRI or CT scan images. An accurate and effective automatic image segmentation system can assist medical professionals as well as automatic detection and classification systems. Deep learning is the current advanced approach for dealing with machine learning and artificial intelligence. However, conventional deep learning requires a large amount of data for training, and the amount of labeled data in the medical field is limited. In this paper, we propose a few-shot learning strategy and integrate it with a base convolutional neural network model, which utilizes a self-attention mechanism to segment MRI for ischemic stroke. By combining the base model with self-attention, we can focus more on the ROI and disregard less important features. Additionally, the proposed system only selects slices with lesions and ignores unlesioned slices. This helps to improve efficiency and reduce the computational load by eliminating the need to tune unnecessary parameters. To achieve even better results, the system also combines two weighted images, FLAIR and DWI, as an early fusion process. Experiments have shown that this approach leads to higher performance compared to using the same system without fusion. The proposed system is evaluated using a publicly available dataset, ISLES 2015 SSIS, and compared with other state-of-the-art (SOTA) systems. It achieves a dice coefficient score of 0.68, which is significantly better than that of other SOTA systems.

MFF-Net: Multiscale feature fusion semantic segmentation network for intracranial surgical instruments.

In robot-assisted surgery, automatic segmentation of surgical instrument images is crucial for surgical safety. The proposed method addresses challenges in the craniotomy environment, such as occlusion and illumination, through an efficient surgical instrument segmentation network. The network uses YOLOv8 as the target detection framework and integrates a semantic segmentation head to achieve detection and segmentation capabilities. A concatenation of multi-channel feature maps is designed to enhance model generalisation by fusing deep and shallow features. The innovative GBC2f module ensures the lightweight of the network and the ability to capture global information. Experimental validation of the intracranial glioma surgical instrument dataset shows excellent performance: 94.9% MPA score, 89.9% MIoU value, and 126.6 FPS. According to the experimental results, the segmentation model proposed in this study has significant advantages over other state-of-the-art models. This provides a valuable reference for the further development of intelligent surgical robots.

Automatic cochlear multimodal 3D image segmentation and analysis using atlas–model-based method

Objectives To propose an automated fast cochlear segmentation, length, and volume estimation method from clinical 3D multimodal images which has a potential role in the choice of cochlear implant type, surgery planning, and robotic surgeries. Methods: Two datasets from different countries were used. These datasets include 219 clinical 3D images of cochlea from 3 modalities: CT, CBCT, and MR. The datasets include different ages, genders, and types of cochlear implants. We propose an atlas–model-based method for cochlear segmentation and measurement based on high-resolution μCT model and A-value. The method was evaluated using 3D landmarks located by two experts. Results: The average error was 0.61 ± 0.22 mm and the average time required to process an image was 5.21 ± 0.93 seconds (P<0.001). The volume of the cochlea ranged from 73.96 mm 3 to 106.97 mm 3 , the cochlear length ranged from 36.69 to 45.91 mm at the lateral wall and from 29.12 to 39.05 mm at the organ of Corti. Discussion: We propose a method that produces nine different automated measurements of the cochlea: volume of scala tympani, volume of scala vestibuli, central lengths of the two scalae, the scala tympani lateral wall length, and the organ of Corti length in addition to three measurements related to A-value. Conclusion: This automatic cochlear image segmentation and analysis method can help clinician process multimodal cochlear images in approximately 5 seconds using a simple computer. The proposed method is publicly available for free download as an extension for 3D Slicer software.

GRAF: Gap region aware framework for Al-Si alloy microscopic image segmentation

The shape of the primary silicon in Al-Si alloys is one of the main factors determining the properties of the alloy, which makes the microscopic image of Al-Si alloys become a crucial reference material in the study of the alloy. Accurate segmentation of silicon particles in Al-Si alloy micrographs is a necessary prerequisite for their quantitative analysis. The traditional manual annotation method requires high time and labor costs and will be affected by subjective factors of the annotator. Although automatic image segmentation methods can avoid these problems, there are silicon particles of different shapes and sizes in the Al-Si alloy microscopic image, and it is hard to separate adjacent silicon particles. These characteristics bring the automatic segmentation method severe challenge. Therefore, to address the issue of deviation in the annotation range when there is agglomeration of primary silicon particles in the microstructure images of Al-Si alloys, We propose a Residual Position-Aware Attention Network (RPAA-Net) for the segmentation of primary silicon particles. The RPAA-Net method enhances segmentation accuracy and robustness by incorporating a residual position-aware attention mechanism, enabling the network to better distinguish silicon particles from the surrounding non-silicon regions. To address the challenge of separating adjacent silicon particles in the image, we introduce a novel Gap Region Weighting Strategy (GRWS). GRWS enhances the model's focus on the gap regions between adjacent objects by applying additional weights during training. By combining RPAA-Net with GRWS, we construct a Gap Region Awareness Framework (GRAF) and experiment with different weights. We find that the weight with the most significant impact is 10, leading to the framework being named GRAF-10. Finally, we apply GRAF-10 to the Aluminum-Silicon Alloy Microstructure (ASAM) dataset. The results show that GRAF-10 outperforms the current state-of-the-art methods, improving the object-level Dice index(Dobj) by 1.56%. Additionally, it reduces the Merge Error (ME) metric by 6.74 and the Variation of Information (VI) metric by 5.43, effectively segmenting aluminum–silicon alloy microstructure images.

S-Net: a multiple cross aggregation convolutional architecture for automatic segmentation of small/thin structures for cardiovascular applications.

With the success of U-Net or its variants in automatic medical image segmentation, building a fully convolutional network (FCN) based on an encoder-decoder structure has become an effective end-to-end learning approach. However, the intrinsic property of FCNs is that as the encoder deepens, higher-level features are learned, and the receptive field size of the network increases, which results in unsatisfactory performance for detecting low-level small/thin structures such as atrial walls and small arteries. To address this issue, we propose to keep the different encoding layer features at their original sizes to constrain the receptive field from increasing as the network goes deeper. Accordingly, we develop a novel S-shaped multiple cross-aggregation segmentation architecture named S-Net, which has two branches in the encoding stage, i.e., a resampling branch to capture low-level fine-grained details and thin/small structures and a downsampling branch to learn high-level discriminative knowledge. In particular, these two branches learn complementary features by residual cross-aggregation; the fusion of the complementary features from different decoding layers can be effectively accomplished through lateral connections. Meanwhile, we perform supervised prediction at all decoding layers to incorporate coarse-level features with high semantic meaning and fine-level features with high localization capability to detect multi-scale structures, especially for small/thin volumes fully. To validate the effectiveness of our S-Net, we conducted extensive experiments on the segmentation of cardiac wall and intracranial aneurysm (IA) vasculature, and quantitative and qualitative evaluations demonstrated the superior performance of our method for predicting small/thin structures in medical images.

TGDAUNet: Transformer and GCNN based dual-branch attention UNet for medical image segmentation

Accurate and automatic segmentation of medical images is a key step in clinical diagnosis and analysis. Currently, the successful application of Transformers’ model in the field of computer vision, researchers have begun to gradually explore the application of Transformers in medical segmentation of images, especially in combination with convolutional neural networks with coding–decoding structure, which have achieved remarkable results in the field of medical segmentation. However, most studies have combined Transformers with CNNs at a single scale or processed only the highest-level semantic feature information, ignoring the rich location information in the lower-level semantic feature information. At the same time, for problems such as blurred structural boundaries and heterogeneous textures in images, most existing methods usually simply connect contour information to capture the boundaries of the target. However, these methods cannot capture the precise outline of the target and ignore the potential relationship between the boundary and the region. In this paper, we propose the TGDAUNet, which consists of a dual-branch backbone network of CNNs and Transformers and a parallel attention mechanism, to achieve accurate segmentation of lesions in medical images. Firstly, high-level semantic feature information of the CNN backbone branches is fused at multiple scales, and the high-level and low-level feature information complement each other’s location and spatial information. We further use the polarised self-attentive (PSA) module to reduce the impact of redundant information caused by multiple scales, to better couple with the feature information extracted from the Transformers backbone branch, and to establish global contextual long-range dependencies at multiple scales. In addition, we have designed the Reverse Graph-reasoned Fusion (RGF) module and the Feature Aggregation (FA) module to jointly guide the global context. The FA module aggregates high-level semantic feature information to generate an original global predictive segmentation map. The RGF module captures non-significant features of the boundaries in the original or secondary global prediction segmentation graph through a reverse attention mechanism, establishing a graph reasoning module to explore the potential semantic relationships between boundaries and regions, further refining the target boundaries. Finally, to validate the effectiveness of our proposed method, we compare our proposed method with the current popular methods in the CVC-ClinicDB, Kvasir-SEG, ETIS, CVC-ColonDB, CVC-300,datasets as well as the skin cancer segmentation datasets ISIC-2016 and ISIC-2017. The large number of experimental results show that our method outperforms the currently popular methods. Source code is released at https://github.com/sd-spf/TGDAUNet.

Improved UNet with Attention for Medical Image Segmentation.

Medical image segmentation is crucial for medical image processing and the development of computer-aided diagnostics. In recent years, deep Convolutional Neural Networks (CNNs) have been widely adopted for medical image segmentation and have achieved significant success. UNet, which is based on CNNs, is the mainstream method used for medical image segmentation. However, its performance suffers owing to its inability to capture long-range dependencies. Transformers were initially designed for Natural Language Processing (NLP), and sequence-to-sequence applications have demonstrated the ability to capture long-range dependencies. However, their abilities to acquire local information are limited. Hybrid architectures of CNNs and Transformer, such as TransUNet, have been proposed to benefit from Transformer's long-range dependencies and CNNs' low-level details. Nevertheless, automatic medical image segmentation remains a challenging task due to factors such as blurred boundaries, the low-contrast tissue environment, and in the context of ultrasound, issues like speckle noise and attenuation. In this paper, we propose a new model that combines the strengths of both CNNs and Transformer, with network architectural improvements designed to enrich the feature representation captured by the skip connections and the decoder. To this end, we devised a new attention module called Three-Level Attention (TLA). This module is composed of an Attention Gate (AG), channel attention, and spatial normalization mechanism. The AG preserves structural information, whereas channel attention helps to model the interdependencies between channels. Spatial normalization employs the spatial coefficient of the Transformer to improve spatial attention akin to TransNorm. To further improve the skip connection and reduce the semantic gap, skip connections between the encoder and decoder were redesigned in a manner similar to that of the UNet++ dense connection. Moreover, deep supervision using a side-output channel was introduced, analogous to BASNet, which was originally used for saliency predictions. Two datasets from different modalities, a CT scan dataset and an ultrasound dataset, were used to evaluate the proposed UNet architecture. The experimental results showed that our model consistently improved the prediction performance of the UNet across different datasets.