Search

Spectral X-ray computed tomography (SXCT) is able to provide richer spectral information than conventional X-ray CT, thereby improving the capability of discriminating materials in segmentation tasks. However, segmentation algorithms for conventional CT images often lose their performance in material segmentation with SXCT images due to the spectrum-dependent attenuation properties of X-rays. Recently, deep neural networks (DNNs) have demonstrated their effectiveness in segmentation tasks. However, processing SXCT with DNNs requires significant memory resources and may take significant computation time. To address these issues, this paper introduces a simulation study towards realising deep-learning-based SXCT image segmentation. The proposed method employs a convolutional neural network (CNN) that selectively utilises the significant spectrum channels for accurate material segmentation. The proposed method builds upon a prior study on adaptive CT reconstruction, but to further enhance the performance, we introduce a self-attention-based spectrum-selection module. During training, the selection module assigns importance weights to spectrum channels based on the attention scores. Once the network has been trained, the proposed method can obtain satisfactory segmentation results from projection images at one or a combination of several spectrum channels recommended by the selection module. The experimental results demonstrate that the proposed method achieves a significant reduction in computation time while maintaining the segmentation quality. [ABSTRACT FROM AUTHOR]

Background: The cost of labeling to collect training data sets using deep learning is especially high in medical applications compared to other fields. Furthermore, due to variances in images depending on the computed tomography (CT) devices, a deep learning based segmentation model trained with a certain device often does not work with images from a different device. Methods: In this study, we propose an efficient learning strategy for deep learning models in medical image segmentation. We aim to overcome the difficulties of segmentation in CT images by training a VNet segmentation model which enables rapid labeling of organs in CT images with the model obtained by transfer learning using a small number of manually labeled images, called SEED images. We established a process for generating SEED images and conducting transfer learning a model. We evaluate the performance of various segmentation models such as vanilla UNet, UNETR, Swin-UNETR and VNet. Furthermore, assuming a scenario that a model is repeatedly trained with CT images collected from multiple devices, in which is catastrophic forgetting often occurs, we examine if the performance of our model degrades. Results: We show that transfer learning can train a model that does a good job of segmenting muscles with a small number of images. In addition, it was confirmed that VNet shows better performance when comparing the performance of existing semi-automated segmentation tools and other deep learning networks to muscle and liver segmentation tasks. Additionally, we confirmed that VNet is the most robust model to deal with catastrophic forgetting problems. Conclusion: In the 2D CT image segmentation task, we confirmed that the CNN-based network shows better performance than the existing semi-automatic segmentation tool or latest transformer-based networks. [ABSTRACT FROM AUTHOR]

Abstract Background The cost of labeling to collect training data sets using deep learning is especially high in medical applications compared to other fields. Furthermore, due to variances in images depending on the computed tomography (CT) devices, a deep learning based segmentation model trained with a certain device often does not work with images from a different device. Methods In this study, we propose an efficient learning strategy for deep learning models in medical image segmentation. We aim to overcome the difficulties of segmentation in CT images by training a VNet segmentation model which enables rapid labeling of organs in CT images with the model obtained by transfer learning using a small number of manually labeled images, called SEED images. We established a process for generating SEED images and conducting transfer learning a model. We evaluate the performance of various segmentation models such as vanilla UNet, UNETR, Swin-UNETR and VNet. Furthermore, assuming a scenario that a model is repeatedly trained with CT images collected from multiple devices, in which is catastrophic forgetting often occurs, we examine if the performance of our model degrades. Results We show that transfer learning can train a model that does a good job of segmenting muscles with a small number of images. In addition, it was confirmed that VNet shows better performance when comparing the performance of existing semi-automated segmentation tools and other deep learning networks to muscle and liver segmentation tasks. Additionally, we confirmed that VNet is the most robust model to deal with catastrophic forgetting problems. Conclusion In the 2D CT image segmentation task, we confirmed that the CNN-based network shows better performance than the existing semi-automatic segmentation tool or latest transformer-based networks.

Background: Liver lesions mainly occur inside the liver parenchyma, which are difficult to locate and have complicated relationships with essential vessels. Thus, preoperative planning is crucial for the resection of liver lesions. Accurate segmentation of the hepatic and portal veins (PVs) on computed tomography (CT) images is of great importance for preoperative planning. However, manually labeling the mask of vessels is laborious and time‐consuming, and the labeling results of different clinicians are prone to inconsistencies. Hence, developing an automatic segmentation algorithm for hepatic and PVs on CT images has attracted the attention of researchers. Unfortunately, existing deep learning based automatic segmentation methods are prone to misclassifying peripheral vessels into wrong categories. Purpose: This study aims to provide a fully automatic and robust semantic segmentation algorithm for hepatic and PVs, guiding subsequent preoperative planning. In addition, to address the deficiency of the public dataset for hepatic and PV segmentation, we revise the annotations of the Medical Segmentation Decathlon (MSD) hepatic vessel segmentation dataset and add the masks of the hepatic veins (HVs) and PVs. Methods: We proposed a structure with a dual‐stream encoder combining convolution and Transformer block, named Dual‐stream Hepatic Portal Vein segmentation Network, to extract local features and long‐distance spatial information, thereby extracting anatomical information of hepatic and portal vein, avoiding misdivisions of adjacent peripheral vessels. Besides, a multi‐scale feature fusion block based on dilated convolution is proposed to extract multi‐scale features on expanded perception fields for local features, and a multi‐level fusing attention module is introduced for efficient context information extraction. Paired t‐test is conducted to evaluate the significant difference in dice between the proposed methods and the comparing methods. Results: Two datasets are constructed from the original MSD dataset. For each dataset, 50 cases are randomly selected for model evaluation in the scheme of 5‐fold cross‐validation. The results show that our method outperforms the state‐of‐the‐art Convolutional Neural Network‐based and transformer‐based methods. Specifically, for the first dataset, our model reaches 0.815, 0.830, and 0.807 at overall dice, precision, and sensitivity. The dice of the hepatic and PVs are 0.835 and 0.796, which also exceed the numeric result of the comparing methods. Almost all the p‐values of paired t‐tests on the proposed approach and comparing approaches are smaller than 0.05. On the second dataset, the proposed algorithm achieves 0.749, 0.762, 0.726, 0.835, and 0.796 for overall dice, precision, sensitivity, dice for HV, and dice for PV, among which the first four numeric results exceed comparing methods. Conclusions: The proposed method is effective in solving the problem of misclassifying interlaced peripheral veins for the HV and PV segmentation task and outperforming the comparing methods on the relabeled dataset. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a hybrid technique between optimization algorithms and neutrosophic theory. This new hybridization can deal with uncertainties in brain computed tomography (CT) images in three different memberships very effectively. To prove the real-time application of this theory, a new segmentation method for brain CT medical images is presented. The grayscale medical image suffers from uncertainties and inconsistencies in the gray levels due to their bad luminance. The proposed technique addressed this problem by performing neutrosophic operations on gray levels based on the S membership function. [ABSTRACT FROM AUTHOR]

The lungs of patients with COVID-19 exhibit distinctive lesion features in chest CT images. Fast and accurate segmentation of lesion sites from CT images of patients' lungs is significant for the diagnosis and monitoring of COVID-19 patients. To this end, we propose a progressive dense residual fusion network named PDRF-Net for COVID-19 lung CT segmentation. Dense skip connections are introduced to capture multi-level contextual information and compensate for the feature loss problem in network delivery. The efficient aggregated residual module is designed for the encoding-decoding structure, which combines a visual transformer and the residual block to enable the network to extract richer and minute-detail features from CT images. Furthermore, we introduce a bilateral channel pixel weighted module to progressively fuse the feature maps obtained from multiple branches. The proposed PDRF-Net obtains good segmentation results on two COVID-19 datasets. Its segmentation performance is superior to baseline by 11.6% and 11.1%, and outperforming other comparative mainstream methods. Thus, PDRF-Net serves as an easy-to-train, high-performance deep learning model that can realize effective segmentation of the COVID-19 lung CT images. [ABSTRACT FROM AUTHOR]

While modern deep learning methods have made significant progress in medical image segmentation, some challenges remain, including accurately capturing features at multiple scales, limited ability to detect critical regions, and susceptibility to noise and background interference. To address these challenges, a new neural network called HMSAM-UNet is introduced in this work. A novel module, the Hierarchical Multi-Scale Attention Module (HMSAM), was designed in HMSAM-UNet to improve the precision and accuracy of CT image segmentation significantly. Specifically, HMSAM integrates the Hierarchical Attention Mechanism and Inception Module via residual connections. The Hierarchical Attention Mechanism can highlight important regions by learning attention weights, dramatically enhancing the model’s ability to perceive critical areas for more accurate localization and segmentation of target structures in CT images. Meanwhile, incorporating the Inception module effectively strengthens the network’s capacity to capture multi-scale features, substantially improving the model’s ability to comprehend the structural characteristics of CT images. The results show that the average loss achieved by the proposed model has a 50.04% reduction compared to the original U-Net architecture. Furthermore, compared to other deep learning models such as FCN, DeepLabV3, PSPNet, Unet, UNet++, and SegNet, the model proposed in this work attains an average Dice coefficient of 98.72, and an average IoU score of 97.46 on the three datasets, both of which are the highest among all compared models.

Background: Pulmonary embolism is a kind of cardiovascular disease that threatens human life and health. Since pulmonary embolism exists in the pulmonary artery, improving the segmentation accuracy of pulmonary artery is the key to the diagnosis of pulmonary embolism. Traditional medical image segmentation methods have limited effectiveness in pulmonary artery segmentation. In recent years, deep learning methods have been gradually adopted to solve complex problems in the field of medical image segmentation. Purpose: Due to the irregular shape of the pulmonary artery and the adjacent‐complex tissues, the accuracy of the existing pulmonary artery segmentation methods based on deep learning needs to be improved. Therefore, the purpose of this paper is to develop a segmentation network, which can obtain higher segmentation accuracy and further improve the diagnosis effect. Methods: In this study, the pulmonary artery segmentation performance from the network model and loss function is improved, proposing a pulmonary artery segmentation network (PA‐Net) to segment the pulmonary artery region from 2D CT images. Reverse Attention and edge attention are used to enhance the expression ability of the boundary. In addition, to better use feature information, the channel attention module is introduced in the decoder to highlight the important channel features and suppress the unimportant channels. Due to blurred boundaries, pixels near the boundaries of the pulmonary artery may be difficult to segment. Therefore, a new contour loss function based on the active contour model is proposed in this study to segment the target region by assigning dynamic weights to false positive and false negative regions and accurately predict the boundary structure. Results: The experimental results show that the segmentation accuracy of this proposed method is significantly improved in comparison with state‐of‐the‐art segmentation methods, and the Dice coefficient is 0.938 ± 0.035, which is also confirmed from the 3D reconstruction results. Conclusions: Our proposed method can accurately segment pulmonary artery structure. This new development will provide the possibility for further rapid diagnosis of pulmonary artery diseases such as pulmonary embolism. Code is available at https://github.com/Yuanyan19/PA‐Net. [ABSTRACT FROM AUTHOR]

The COVID-19 epidemic has threatened the human being. The automatic and accurate segmentation for the infected area of the COVID-19 CT images can help doctors to make correct diagnosis and treatment in time. However, it is very challenging to achieve perfect segmentation due to the diffuse infections of the COVID-19 to the patient lungs and irregular shapes of the infected areas and very similar infected areas to other lung tissues. To tackle these challenges, the XR-MSF-Unet model is proposed in this paper for segmenting the COVID-19 lung CT images of patients. The XR (X ResNet) convolution module is proposed in this model to replace the two-layer convolution operations of U-Net, so as to extract more informative features for achieving good segmentation results by multiple branches of XR. The plug and play attention mechanism module MSF (multi-scale features fusion module) is proposed in XR-MSF-Unet to fuse multi-scale features from different scales of reception fields, global, local and spatial features of CT images, so as to strengthen the detail segmentation effect of the model. Extensive experiments on the public COVID-19 CT images demonstrate that the proposed XR module can strengthen the capability of the XR-MSF-Unet model to extract effective features, and the MSF module plus XR module can effectively improve the segmentation capability of the XR-MSF-Unet model for the infected areas of the COVID-19 lung CT images. The proposed XR-MSF-Unet model obtains good segmentation results. Its segmentation perfor-mance is superior to that of the original U-Net model by 3.21, 5.96, 1.22 and 4.83 percentage points in terms of Dice, IOU, F1-Score and Sensitivity, and it defeats other same type of models, realizing automatic segmentation to the COVID-19 lung CT images.

Radiotherapy, a standard and common form of cancer treatment, is used in about half of all cancer cases. It is critical to segment organs at risk (OARs) accurately and promptly for radiation treatment planning to be efficient and high-quality. In this paper, an adversarial training strategy is used in our deep learning network. This model is called the Multiple Organ Segmentation Generative Adversarial Network (MOS-GAN). The U-Net++ is our generator to create a multi-organ segmentation image by learning the end-to-end mapping from CT images to OARs segmentation. A convolutional neural network (CNN) acts as the discriminator to identify the difference between the manual contour and the segmented images created by the generator. A 2.5D method is employed by us to extract the spatial information among slices. We applied the proposed method to segment the five organs from the 2017 AAPM segmentation challenge dataset. Segmentation of the bilateral lungs, heart, and spinal cord were successfully achieved, as well as the outline of the esophagus. In terms of dice similarity coefficients, the aforementioned five OARs have average values of 0.97, 0.97, 0.92, 0.91 and 0.77, respectively. • A new segmentation model combines U-Net++ and GAN is developed. • Spatial information between CT slices are extracted using the 2.5D method. • Proposed approach is used to segment organs of chest CT images. • Higher DSC value is achieved using proposed approach. [ABSTRACT FROM AUTHOR]

Medical image segmentation is one of the important steps in clinical diagnosis, and accurate segmentation of lesions is of great significance to clinical treatment. Therefore, a CT image segmentation algorithm based on depth learning is proposed to solve the problems of poor robustness, weak anti noise ability and low segmentation accuracy of existing image segmentation algorithms.. Firstly, we improve the u-net network structure, increase the batch standardization layer to improve the robustness of the network model, and introduce the attention mechanism to focus on specific things according to the needs, improve the recognition ability of the model. Then, the improved U-Net network structure is applied to CT image segmentation, and the cross entropy loss function is used to reduce the possibility of insufficient segmentation and segmentation leakage, and improve the accuracy of image segmentation. Finally, on the basis of data preprocessing, the segmentation network is trained to get the image segmentation model based on deep learning, and the prediction is made on the test set to get the segmentation results. The experimental results show that compared with other algorithms, the proposed method achieves 0.9594 in the common evaluation standard Dice coefficient, and has strong robustness. It can accurately segment the lung organs in CT images, which is helpful for doctors to obtain pathological information and assist in the diagnosis of lung diseases. [ABSTRACT FROM AUTHOR]

In hip arthroplasty, preoperative planning is fundamental to reaching a successful surgery. Nowadays, several software tools for computed tomography (CT) image processing are available. However, research studies comparing segmentation tools for hip surgery planning for patients affected by osteoarthritic diseases or osteoporotic fractures are still lacking. The present work compares three different software from the geometric, dimensional, and usability perspectives to identify the best three-dimensional (3D) modelling tool for the reconstruction of pathological femoral heads. Syngo.via Frontier (by Siemens Healthcare) is a medical image reading and post-processing software that allows low-skilled operators to produce prototypes. Materialise (by Mimics) is a commercial medical modelling software. 3D Slicer (by slicer.org) is an open-source development platform used in medical and biomedical fields. The 3D models reconstructed starting from the in vivo CT images of the pathological femoral head are compared with the geometries obtained from the laser scan of the in vitro bony specimens. The results show that Mimics and 3D Slicer are better for dimensional and geometric accuracy in the 3D reconstruction, while syngo.via Frontier is the easiest to use in the hospital setting. [ABSTRACT FROM AUTHOR]

Up to now, the correlation between soil evaporation and cracking within a multi-scale perspective remains unclear so far. For this purpose, X-ray computed tomography was used to dynamically capture the 3D cracking process of a compacted clayey soil with evaporation. The proposed image processing methods combining CT image segmentation and digital image correlation (DIC) allowed multi-scale quantitative analysis of the temporal evolution of soil water distribution, regional deformation and crack network upon evaporation. Different mechanisms are defined to explain the development of regional crack networks. Evaporation-mobilised tensile stress leads to cracking in the top part of the soil, with a single-peaked change along drying time in the cracking rate. A similar variation appears in the cracking process of the bottom soil, attributed to the basal friction activated by soil desiccation shrinkage. Besides, the cracking rate of this region is nearly independent of the regional evaporation. As for the middle part of the soil, a double-peaked pattern dominates the cracking rate, as the curling contributes to the formation of new inner cracks and the bottom soil cracks propagate upwards. Furthermore, desiccation cracks developing to a certain extent facilitate the water loss of neighbouring soil bodies, whereas this phenomenon is difficult to present over the whole soil scale. The current outcomes are expected to advance insights into the spatial–temporal cracking mechanism of soils under evaporation. [ABSTRACT FROM AUTHOR]

Lung cancer is among the leading cause of death among men and women. Early detection of lung cancers can increase the possibility of survival amongst patients. The preferred 5-years survival rate for lung most cancers sufferers will increase from 16% to 50% if the disease is detected on time. Computerized tomography (CT) is frequently used for diagnosis and is more efficient than X-ray. However, the images need to be reviewed by a qualified physician who specializes in interpreting the CT scan. This may lead to misinterpretation and conflicting reports among physicians. Therefore, a lung cancer detection system that uses image processing methods to categorize lung cancer in CT images will be more consistent and precise. This paper presents a lung cancer detection system using the Artificial Intelligence (AI) method. The study uses Median, Gaussian, and Watershed segments to reduce noisy and shredded CT images. Then, the Weight Optimization Neural Network method was used to improve accuracy and reduce the computational time. The results were compared with previous works and shows higher accuracy and shorter computational time.

Image segmentation is used to analyze medical images quantitatively for diagnosis and treatment planning. Since manual segmentation requires considerable time and effort from experts, research to automatically perform segmentation is in progress. Recent studies using deep learning have improved performance but need many labeled data. Although there are public datasets for research, manual labeling is required in an area where labeling is not performed to train a model. We propose a deep-learning-based tool that can easily create training data to alleviate this inconvenience. The proposed tool receives a CT image and the pixels of organs the user wants to segment as inputs and extract the features of the CT image using a deep learning network. Then, pixels that have similar features are classified to the identical organ. The advantage of the proposed tool is that it can be trained with a small number of labeled data. After training with 25 labeled CT images, our tool shows competitive results when it is compared to the state-of-the-art segmentation algorithms, such as UNet and DeepNetV3. [ABSTRACT FROM AUTHOR]

Decent whole-heart segmentation from computed tomography (CT) can greatly contribute to the diagnosis and treatment of cardiovascular diseases. However, due to the difficulties such as blurred boundaries between neighbouring tissues and a large number of background voxels in medical images, automated whole-heart segmentation is still a challenging task. In this paper, we proposed three modified attention models, including simple negative example mining (SNEM), attention gate (AG) and U-CliqueNet (UCNet), to lead the deep learning network to focus on more salient information. These three attention modules were further implemented into a deeply-supervised 3D UNET separately and jointly, showing different degrees of improvement on the whole-heart segmentation task. Our experiments advised that SNEM was the most simple and effective attention mechanism for medical image processing among the three and the UCNet could reach the best performance. The combination of the attention mechanisms cannot always synergistically increase the accuracy, but joint models would have a positive influence in most cases. Finally, our network achieved a Dice score of 0.9112, which was a substantially higher performance than most of the state-of-the-art methods.

Automatic segmentation of the organ's tumor and lesion on biomedical imaging is an essential initiative towards clinical study, treatment planning and digital biomedical research. However, precise tumor segmentation on medical imaging is still an open challenge due to the presence of noise in the imaging sequence, the similar tumor pixel intensity with its neighboring tissues, and heterogeneity among human anatomy. Although most of the state-of-the-art algorithms are architecturally dependent on deep convolution networks (DCNs), like 2D and 3D U-Net, they act as a foundation for many biomedical image segmentation. However, 2D DCNs are incompetent to leverage context information from inter-slice completely. At the same time, 3D DCNs can accumulate inter-slice contextual information over the sizeable receptive texture in the organ, but it consumes a considerable amount of GPU memory and burdens with the high execution cost. In order to achieve a promising solution, we proposed a segmentation network called Cascaded Atrous Dual-Attention U-Net. First, our network structure concatenates features from 3D liver segmentation to 2D tumor segmentation for preserving volumetric information as well as enlarging resolution with segmentation accuracy. Second, we embedded dual attention gate in each skip connection layer of the 2D segmentation model, which determines to concentrate on certain discriminative features in order to find tumor segmentation in different organs. Finally, we adopted atrous encoder which extracts wider context features from computed tomography as compared to normal encoder. Furthermore, we tested the proposed method on four different datasets, including liver tumor segmentation benchmark (LiTS), MSD liver, pancreas tumor segmentation and Kidney tumor segmentation (KiTS). Experimental results were compared with the other state-of-the-art segmentation methods. Our proposed approach performs remarkably better than existing methods with around 4 ∼ 6 % improvement on each benchmark. [ABSTRACT FROM AUTHOR]

Obtaining precise whole-heart segmentation from computed tomography (CT) or other imaging techniques is prerequisite to clinically analyze the cardiac status, which plays an important role in the treatment of cardiovascular diseases. However, the whole-heart segmentation is still a challenging task due to the characteristic of medical images, such as far more background voxels than foreground voxels and the indistinct boundaries of adjacent tissues. In this paper, we first present a new deeply supervised 3D UNET which applies multi-depth fusion to the original network for a better extract context information. Then, we apply focal loss to the field of image segmentation and expand its application to multi-category tasks. Finally, the focal loss is incorporated into the Dice loss function (which can be used to solve category imbalance problem) to form a new loss function, which we call hybrid loss. We evaluate our new pipeline on the MICCAI 2017 whole-heart CT dataset, and it obtains a Dice score of 90.73%, which is better than most of the state-of-the-art methods.

In hip arthroplasty, preoperative planning is fundamental to reaching a successful surgery. Nowadays, several software tools for computed tomography (CT) image processing are available. However, research studies comparing segmentation tools for hip surgery planning for patients affected by osteoarthritic diseases or osteoporotic fractures are still lacking. The present work compares three different software from the geometric, dimensional, and usability perspectives to identify the best three-dimensional (3D) modelling tool for the reconstruction of pathological femoral heads. Syngo.via Frontier (by Siemens Healthcare) is a medical image reading and post-processing software that allows low-skilled operators to produce prototypes. Materialise (by Mimics) is a commercial medical modelling software. 3D Slicer (by slicer.org) is an open-source development platform used in medical and biomedical fields. The 3D models reconstructed starting from the in vivo CT images of the pathological femoral head are compared with the geometries obtained from the laser scan of the in vitro bony specimens. The results show that Mimics and 3D Slicer are better for dimensional and geometric accuracy in the 3D reconstruction, while syngo.via Frontier is the easiest to use in the hospital setting.

Accurate segmentation of CT images is crucial for clinical diagnosis and preoperative evaluation of robotic surgery, but challenges arise from fuzzy boundaries and small-sized targets. In response, a novel 2D segmentation network named Context Fusing Attentional Network (CFANet) is proposed. CFANet incorporates three key modules to address these challenges, namely pyramid fusing module (PFM), parallel dilated convolution module (PDCM) and scale attention module (SAM). Integration of these modules into the encoder-decoder structure enables effective utilization of multi-level and multi-scale features. Compared with advanced segmentation method, the Dice score improved by 2.14% on the dataset of liver tumor. This improvement is expected to have a positive impact on the preoperative evaluation of robotic surgery and to support clinical diagnosis, especially in early tumor detection., Competing Interests: Declaration of competing interest None Declared, (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Image segmentation is used to analyze medical images quantitatively for diagnosis and treatment planning. Since manual segmentation requires considerable time and effort from experts, research to automatically perform segmentation is in progress. Recent studies using deep learning have improved performance but need many labeled data. Although there are public datasets for research, manual labeling is required in an area where labeling is not performed to train a model. We propose a deep-learning-based tool that can easily create training data to alleviate this inconvenience. The proposed tool receives a CT image and the pixels of organs the user wants to segment as inputs and extract the features of the CT image using a deep learning network. Then, pixels that have similar features are classified to the identical organ. The advantage of the proposed tool is that it can be trained with a small number of labeled data. After training with 25 labeled CT images, our tool shows competitive results when it is compared to the state-of-the-art segmentation algorithms, such as UNet and DeepNetV3.

We propose a novel non-invasive tool, using deep learning and imaging, for delineating COVID-19 infection in lungs. The Ensembling of selective Focus-based Multi-resolution Convolution network (E F M C), employing Leave-One-Patient-Out (L O P O) training, exhibits high sensitivity and precision in outlining infected regions along with assessment of severity. The selective focus mechanism combines contextual with local information, at multiple resolutions, for accurate segmentation. Ensemble learning integrates heterogeneity of decision through different base classifiers. The superiority of E F M C , even with severe class imbalance, is established through comparison with existing state-of-the-art learning models over four publicly-available COVID-19 datasets. The results are suggestive of the relevance of deep learning in providing assistive intelligence to medical practitioners, when they are overburdened with patients as in pandemics. • Use of limited data for training the deep learning model. • A multi-resolution selective-focus framework for image segmentation. • Ensembling of classifiers through a new strategy LOPO, with good generalization. • Results demonstrating its efficiency and accuracy over state-of-the-art methods. • Accurate analysis of the severity of infection based on segmentation results. [ABSTRACT FROM AUTHOR]

Objective: Hydrocephalus is a medical condition in which there is an abnormal accumulation of cerebrospinal fluid (CSF) in the brain. Segmentation of brain imagery into brain tissue and CSF [before and after surgery, i.e., preoperative (pre-op) versus postoperative (post-op)] plays a crucial role in evaluating surgical treatment. Segmentation of pre-op images is often a relatively straightforward problem and has been well researched. However, segmenting post-op computational tomographic (CT) scans becomes more challenging due to distorted anatomy and subdural hematoma collections pressing on the brain. Most intensity- and feature-based segmentation methods fail to separate subdurals from brain and CSF as subdural geometry varies greatly across different patients and their intensity varies with time. We combat this problem by a learning approach that treats segmentation as supervised classification at the pixel level, i.e., a training set of CT scans with labeled pixel identities is employed. Methods: Our contributions include: 1) a dictionary learning framework that learns class (segment) specific dictionaries that can efficiently represent test samples from the same class while poorly represent corresponding samples from other classes; 2) quantification of associated computation and memory footprint; and 3) a customized training and test procedure for segmenting post-op hydrocephalic CT images. Results: Experiments performed on infant CT brain images acquired from the CURE Children's Hospital of Uganda reveal the success of our method against the state-of-the-art alternatives. We also demonstrate that the proposed algorithm is computationally less burdensome and exhibits a graceful degradation against a number of training samples, enhancing its deployment potential. [ABSTRACT FROM AUTHOR]

Using digital material twins reveals the fabric architectures of woven composites and details the morphological features of fiber tows and defects, which makes a unique approach to accessing the mechanical performance of woven composites, especially the damage and failure behaviors. However, the current major challenges are accurately and efficiently segmenting low-contrast digital images, such as μCT images or MRI images, and reconstructing 3D braided structures. In this paper, a ResL-U-Net convolutional neural network is proposed, in which we use the leaky-ReLU activation function to improve efficiency. We use the residual structure to prevent network degradation and obtain excellent robustness and accuracy. The initial manufacturing defects are implanted into the finite element model by three-dimensional spatial mapping, which ensures the authenticity of defects on the one hand and high-quality mesh on the other. The damage evolution and fracture features of the GFRP material are investigated based on digital material twins; simultaneously, an in-situ CT tensile experiment is tailored to verify the simulations. It is shown that the simulations by the material twins demonstrated the mechanical performances of the GFRP very well, especially on the damage locations and material failure patterns. [ABSTRACT FROM AUTHOR]

The lungs of patients with COVID-19 exhibit distinctive lesion features in chest CT images. Fast and accurate segmentation of lesion sites from CT images of patients' lungs is significant for the diagnosis and monitoring of COVID-19 patients. To this end, we propose a progressive dense residual fusion network named PDRF-Net for COVID-19 lung CT segmentation. Dense skip connections are introduced to capture multi-level contextual information and compensate for the feature loss problem in network delivery. The efficient aggregated residual module is designed for the encoding-decoding structure, which combines a visual transformer and the residual block to enable the network to extract richer and minute-detail features from CT images. Furthermore, we introduce a bilateral channel pixel weighted module to progressively fuse the feature maps obtained from multiple branches. The proposed PDRF-Net obtains good segmentation results on two COVID-19 datasets. Its segmentation performance is superior to baseline by 11.6% and 11.1%, and outperforming other comparative mainstream methods. Thus, PDRF-Net serves as an easy-to-train, high-performance deep learning model that can realize effective segmentation of the COVID-19 lung CT images., Competing Interests: Conflict of interestThe authors declare no conflict of interest., (© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Due to its precision, computed tomography (CT) is now generally used to image the liver and diagnose its diseases. Computer-assisted methods aimed at facilitating the extraction of organ shapes from medical images and helping to diagnose disease entities are rapidly developing. This study presents a new method of automatically segmenting the shape of the liver, both for cases free of lesions and those showing certain disease units, i.e. focused lesions like hemangiomas and hepatomas. For the 1,330 2D CT images of the abdominal cavity and liver analysed, the Dices similarity coefficient amounted to 81.3 %. The method proposed is to be used in an IT system supporting liver diagnostics. [ABSTRACT FROM AUTHOR]

• A novel segmentation method is proposed for analyzing the CT images of asphalt mixture. • Mineral aggregates, bituminous mortar, and air voids in asphalt mixture can be separately recognized. • 3D mesoscopic structure of asphalt-mixture components can be interpreted. • The p proposed method has been verified by a benchmark test and a case study. The three-dimensional reconstruction of asphalt mixture using X-ray computed tomography (CT) images is beneficial to establish the correlation between its mesoscopic structure and its macroscopic properties. Segmentation of CT images that distinguish the mineral aggregates and air voids in asphalt mixture is an essential step for this purpose, wherein up-to-date methods are often limited due to complex image quality and failed to identify the bituminous mortar with gray levels ranging between void and aggregate. In this paper, we propose a local threshold based and Monte-Carlo incorporated segmentation method to address the above problem effectively. The probability of each pixel identified as aggregate could be calculated in a CT image. Notably, the proposed method is able to identify the multiphase components of asphalt mixture and build the three-dimensional mesoscopic structure model to investigate the spatial variation of different components. The proposed method has been verified in a case study, wherein 400 sequenced CT images of an asphalt mixture sample are segmented to interpret its mesoscopic structure. Results show that the proposed method performs well to distinguish the multi-phase components of the asphalt mixture, particularly the bituminous mortar that is usually miscategorized as void or aggregate by previous binarization methods. [ABSTRACT FROM AUTHOR]