Three-dimensional Image Segmentation Research Articles

Kernel-weighted contribution: a method of visual attribution for 3D deep learning segmentation in medical imaging.

Explaining deep learning model decisions, especially those for medical image segmentation, is a critical step toward the understanding and validation that will enable these powerful tools to see more widespread adoption in healthcare. We introduce kernel-weighted contribution, a visual explanation method for three-dimensional medical image segmentation models that produces accurate and interpretable explanations. Unlike previous attribution methods, kernel-weighted contribution is explicitly designed for medical image segmentation models and assesses feature importance using the relative contribution of each considered activation map to the predicted segmentation. We evaluate our method on a synthetic dataset that provides complete knowledge over input features and a comprehensive explanation quality metric using this ground truth. Our method and three other prevalent attribution methods were applied to five different model layer combinations to explain segmentation predictions for 100 test samples and compared using this metric. Kernel-weighted contribution produced superior explanations of obtained image segmentations when applied to both encoder and decoder sections of a trained model as compared to other layer combinations (). In between-method comparisons, kernel-weighted contribution produced superior explanations compared with other methods using the same model layers in four of five experiments () and showed equivalently superior performance to GradCAM++ when only using non-transpose convolution layers of the model decoder (). The reported method produced explanations of superior quality uniquely suited to fully utilize the specific architectural considerations present in image and especially medical image segmentation models. Both the synthetic dataset and implementation of our method are available to the research community.

SUNet++: A Deep Network with Channel Attention for Small-Scale Object Segmentation on 3D Medical Images

As a deep learning network with an encoder-decoder architecture, UNet and its series of improved versions have been widely used in medical image segmentation with great applications. However, when used to segment targets in 3D medical images such as magnetic resonance imaging (MRI), computed tomography (CT), these models do not model the relevance of images in vertical space, resulting in poor accurate analysis of consecutive slices of the same patient. On the other hand, the large amount of detail lost during the encoding process makes these models incapable of segmenting small-scale tumor targets. Aiming at the scene of small-scale target segmentation in 3D medical images, a fully new neural network model SUNet++ is proposed on the basis of UNet and UNet++. SUNet++ improves the existing models mainly in three aspects: 1) the modeling strategy of slice superposition is used to thoroughly excavate the three dimensional information of the data; 2) by adding an attention mechanism during the decoding process, small scale targets in the picture are retained and amplified; 3) in the up-sampling process, the transposed convolution operation is used to further enhance the effect of the model. In order to verify the effect of the model, we collected and produced a dataset of hyperintensity MRI liver-stage images containing over 400 cases of liver nodules. Experimental results on both public and proprietary datasets demonstrate the superiority of SUNet++ in small-scale target segmentation of three-dimensional medical images.

Segmentation of prostate region in magnetic resonance images based on improved V-Net

Magnetic resonance (MR) imaging is an important tool for prostate cancer diagnosis, and accurate segmentation of MR prostate regions by computer-aided diagnostic techniques is important for the diagnosis of prostate cancer. In this paper, we propose an improved end-to-end three-dimensional image segmentation network using a deep learning approach to the traditional V-Net network (V-Net) network in order to provide more accurate image segmentation results. Firstly, we fused the soft attention mechanism into the traditional V-Net's jump connection, and combined short jump connection and small convolutional kernel to further improve the network segmentation accuracy. Then the prostate region was segmented using the Prostate MR Image Segmentation 2012 (PROMISE 12) challenge dataset, and the model was evaluated using the dice similarity coefficient (DSC) and Hausdorff distance (HD). The DSC and HD values of the segmented model could reach 0.903 and 3.912 mm, respectively. The experimental results show that the algorithm in this paper can provide more accurate three-dimensional segmentation results, which can accurately and efficiently segment prostate MR images and provide a reliable basis for clinical diagnosis and treatment.

Swin Unet3D: a three-dimensional medical image segmentation network combining vision transformer and convolution

BackgroundSemantic segmentation of brain tumors plays a critical role in clinical treatment, especially for three-dimensional (3D) magnetic resonance imaging, which is often used in clinical practice. Automatic segmentation of the 3D structure of brain tumors can quickly help physicians understand the properties of tumors, such as the shape and size, thus improving the efficiency of preoperative planning and the odds of successful surgery. In past decades, 3D convolutional neural networks (CNNs) have dominated automatic segmentation methods for 3D medical images, and these network structures have achieved good results. However, to reduce the number of neural network parameters, practitioners ensure that the size of convolutional kernels in 3D convolutional operations generally does not exceed 7 times 7 times 7, which also leads to CNNs showing limitations in learning long-distance dependent information. Vision Transformer (ViT) is very good at learning long-distance dependent information in images, but it suffers from the problems of many parameters. What’s worse, the ViT cannot learn local dependency information in the previous layers under the condition of insufficient data. However, in the image segmentation task, being able to learn this local dependency information in the previous layers makes a big impact on the performance of the model.MethodsThis paper proposes the Swin Unet3D model, which represents voxel segmentation on medical images as a sequence-to-sequence prediction. The feature extraction sub-module in the model is designed as a parallel structure of Convolution and ViT so that all layers of the model are able to adequately learn both global and local dependency information in the image.ResultsOn the validation dataset of Brats2021, our proposed model achieves dice coefficients of 0.840, 0.874, and 0.911 on the ET channel, TC channel, and WT channel, respectively. On the validation dataset of Brats2018, our model achieves dice coefficients of 0.716, 0.761, and 0.874 on the corresponding channels, respectively.ConclusionWe propose a new segmentation model that combines the advantages of Vision Transformer and Convolution and achieves a better balance between the number of model parameters and segmentation accuracy. The code can be found at https://github.com/1152545264/SwinUnet3D.

3D bi-directional transformer U-Net for medical image segmentation.

As one of the popular deep learning methods, deep convolutional neural networks (DCNNs) have been widely adopted in segmentation tasks and have received positive feedback. However, in segmentation tasks, DCNN-based frameworks are known for their incompetence in dealing with global relations within imaging features. Although several techniques have been proposed to enhance the global reasoning of DCNN, these models are either not able to gain satisfying performances compared with traditional fully-convolutional structures or not capable of utilizing the basic advantages of CNN-based networks (namely the ability of local reasoning). In this study, compared with current attempts to combine FCNs and global reasoning methods, we fully extracted the ability of self-attention by designing a novel attention mechanism for 3D computation and proposed a new segmentation framework (named 3DTU) for three-dimensional medical image segmentation tasks. This new framework processes images in an end-to-end manner and executes 3D computation on both the encoder side (which contains a 3D transformer) and the decoder side (which is based on a 3D DCNN). We tested our framework on two independent datasets that consist of 3D MRI and CT images. Experimental results clearly demonstrate that our method outperforms several state-of-the-art segmentation methods in various metrics.

A novel technique to quantify bone-to-implant contact of zygomatic implants: a radiographic analysis based on three-dimensional image registration and segmentation.

The purpose of this study is to establish a novel, reproducible technique to obtain the BIC area (BICA) between zygomatic implants and zygomatic bone based on post-operative cone-beam computed tomography (CBCT) images. Three-dimensional (3D) image registration and segmentation were used to eliminate the effect of metal-induced artifacts of zygomatic implants. An ex-vivo study was included to verify the feasibility of the new method. Then, the radiographic bone-to-implant contact (rBIC) of 143 implants was measured in a total of 50 patients. To obtain the BICA of zygomatic implants and the zygomatic bone, several steps were necessary, including image preprocessing of CBCT scans, identification of the position of zygomatic implants, registration, and segmentation of pre- and post-operative CBCT images, and 3D reconstruction of models. The conventional two-dimensional (2D) linear rBIC (rBICc) measurement method with post-operative CBCT images was chosen as a comparison. The mean values of rBIC and rBICc were 15.08 ± 5.92 mm and 14.77 ± 5.14 mm, respectively. A statistically significant correlation was observed between rBIC and rBICc values ([Formula: see text]=0.86, p < 0.0001). This study proposed a standardized, repeatable, noninvasive technique to quantify the rBIC of post-operative zygomatic implants in 3D terms. This technique is comparable to conventional 2D linear measurements and seems to be more reliable than these conventional measurements; thus, this method could serve as a valuable tool in the performance of clinical research protocols.

Semi-supervised dual-task cross-consistency constraint network for 3D cardiac MRI segmentation

目的 针对人体组织器官及病灶区域的3维图像分割是计算机辅助医疗诊断的重要前提，是医学影像3维可视化的重要技术基础。深度学习方法在医学图像分割任务中的成功通常取决于大量有标注数据。半监督学习利用未标注数据容易获取的优点，在模型训练过程中使用少量标注数据和大量未标注数据进行学习，缓解了数据标注昂贵耗时的问题，在医学图像分割中受到了广泛关注。为更好地利用无标注数据，提升医学图像分割效果，提出一种新的一致性正则方法用于半监督3维医学图像分割。方法 模型以V-Net为基础架构，通过扩展网络结构，在均带有分割任务及回归任务属性的双任务主副解码器之间添加了用于正则化约束的交叉损失，构建了具有形状感知的基于双任务的交叉一致性正则网络SACC-Net （shape-aware cross-consistency regular network based on dual tasks），实现将数据层面和模型层面的扰动融合进多任务机制的一致性正则方法，使模型能够更好地利用未标注数据的有效先验信息，并且具有更好的泛化性能。结果 在MICCAI 2018（Medical Image Computing and Computer Assisted Intervention Society）心房分割挑战赛公布的数据集中的3维左心房核磁共振成像上验证本文算法，在仅使用训练集中10%的有标注数据的实验组中，Dice系数、Jaccard指数、HD（Hausdorff distance）距离和平均对称表面距离分别为88.01%、78.89%、8.19和2.09。在另一组仅使用20%的有标注数据的实验中Dice系数、Jaccard指数、HD距离和平均对称表面距离分别达到90.11%、82.11%、6.57和1.78。结论 本文提出的半监督分割模型性能显著，综合了数据、模型和任务层面的一致性正则约束，与其他半监督方法相比分割效果更好且具有更佳的泛化性能。;Objective Three-dimensional（3D）image segmentation of human tissues，organs and lesion areas is projected for computer-aided diagnosis and medical images-related 3D visualization. Thanks to the emerging deep learning technique，fully-supervised network models have been developing intensively in relevant to medical image segmentation tasks. However，it is challenged for a large amount of annotated data and 3D image segmentation data-labeled is costly and inefficient. Semi-supervised learning is focused on a small size of data-labeled and sufficient data-unlabeled in terms of easy acquisition of unlabeled data，which can alleviate the cost and time-consuming problem of data labeling. Our research is focused on new consistency regular method for semi-supervised 3D medical image segmentation model. To improve the medical image segmentation effect，our model can use unlabeled data through the fusion of different consistency methods. Method The network model is demonstrated on the V-Net，which can remove the residual structure of encoding and decoding. To get efficient features of unlabeled data，the proposed shape-aware cross-consistency regular network is introduced via the V-Net network structure extension on the basis of an encoder and two independent decoders-involved dual tasks （shape-aware cross-consistency regular network based on dual tasks（SACC-Net）），which are divided into a main decoder a and an auxiliary decoder b. The output of encoder-shared is transmitted to the two decoders after noise disturbance. At the same time，the two decoders can output the prediction results after each iteration. To increase the generalization and anti-noise ability of the model，it can minimize the difference between the two parts of the results during the training process. Additionally，the proportion of labeled samples in the training samples is extremely small because the feature distributions between the pre-processed medical image samples are relatively similar. To improve the learning ability of the model to segmented samples further，geometric prior information constraints are melted into the segmentation target. A shape-aware regression layer is added at the end of each decoder as well. During the training phase，each decoder can output two parts of the prediction results at the same time. That is，the total output of each iteration-after network consists of four parts. It can be used to decode the segmentation map SA and the signed distance map output by the decoder A，and the segmentation map SB and the signed distance map output by the decoder B through the dual-task consistency of each decoding part. To enhance the model’s ability and learn the effective features of segmentation targets to a greater extent， constraints and cross constraints can be used to realize a consistent regular method that combines data-level and modellevel disturbances with multi-task mechanisms，and make better use of unlabeled data. Result Our algorithm is validated on the MRI data set published in Atrium Segmentation Challenge held by MICCAI（Medical Image Computing and Computer Assisted Intervention Society）in 2018. The experiment is divided into two test groups based on the amounts proportion of labeled data. In the training set，10% annotated data is used only in the experimental group，the Dice coefficient， Jaccard index，HD （Hausdorff distance） distance，and average symmetric surface distance is reached to 88. 01%， 78. 89%，8. 19，and 2. 09 of each. In the other group，20% annotated data of the experiments are used only. The median Dice coefficient，Jaccard index，HD distance and average symmetric surface distance can be reached to 90. 14%， 82. 11%，6. 57，and 1. 78 each as well. Furthermore，In respect of the shape perception method using the level set function for regression tasks，the Dice evaluation index can be improved by 0. 69 and 0. 60 in comparison with shape-aware semi-supervised net（SASS Net）in 10% and 20% of the marked training results. Each improvement is reached to 1. 44% and 0. 72% in terms of comparative results of dual-task consistency（DTC）trained with 10% and 20% labeled data. Conclusion The semi-supervised segmentation model（SACC-Net）is illustrated for the criteria optimization for both of the region and boundary-based segmentation，which can incorporate the level-consistency of its data，model and task. The constrained method has its potential segmentation effect and generalization performance for semi-supervised methods.

Advanced 3D Visualization and 3D Printing in Radiology.

Since the discovery of X-rays in 1895, medical imaging systems have played a crucial role in medicine by permitting the visualization of internal structures and understanding the function of organ systems. Traditional imaging modalities including Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and Ultrasound (US) present fixed two-dimensional (2D) images which are difficult to conceptualize complex anatomy. Advanced volumetric medical imaging allows for three-dimensional (3D) image post-processing and image segmentation to be performed, enabling the creation of 3D volume renderings and enhanced visualization of pertinent anatomic structures in 3D. Furthermore, 3D imaging is used to generate 3D printed models and extended reality (augmented reality and virtual reality) models. A 3D image translates medical imaging information into a visual story rendering complex data and abstract ideas into an easily understood and tangible concept. Clinicians use 3D models to comprehend complex anatomical structures and to plan and guide surgical interventions more precisely. This chapter will review the volumetric radiological techniques that are commonly utilized for advanced 3D visualization. It will also provide examples of 3D printing and extended reality technology applications in radiology and describe the positive impact of advanced radiological image visualization on patient care.

Brain Tumor Image Segmentation Based on Grouped Convolution

The automatic segmentation of MRI multi-modal images of brain tumors is one of the important research contents of disease detection and analysis. Due to the heterogeneity of tumors, it is difficult to achieve efficient and accurate automatic segmentation of brain tumors. Traditional segmentation methods based on machine learning cannot handle complex scenes such as complex edges and overlapping categories. In clinical assisted diagnosis, it is of great significance to apply deep learning to two-dimensional natural image segmentation and three-dimensional medical image segmentation. In this paper, we propose a three-dimensional network model to achieve precise segmentation of brain tumors. The model adopts an encoder-decoder structure and replaces ordinary convolution with grouped convolution to reduce network parameters and improve network performance. The model improves the problem of information exchange between different groups through channel mixing. Experiments conducted on the BraTS (Brain Tumor Segmentation) 2018 challenge dataset prove that our network greatly reduces the computational cost while ensuring segmentation accuracy.

A Deep Learning Framework for Segmenting Brain Tumors Using MRI and Synthetically Generated CT Images.

Multi-modal three-dimensional (3-D) image segmentation is used in many medical applications, such as disease diagnosis, treatment planning, and image-guided surgery. Although multi-modal images provide information that no single image modality alone can provide, integrating such information to be used in segmentation is a challenging task. Numerous methods have been introduced to solve the problem of multi-modal medical image segmentation in recent years. In this paper, we propose a solution for the task of brain tumor segmentation. To this end, we first introduce a method of enhancing an existing magnetic resonance imaging (MRI) dataset by generating synthetic computed tomography (CT) images. Then, we discuss a process of systematic optimization of a convolutional neural network (CNN) architecture that uses this enhanced dataset, in order to customize it for our task. Using publicly available datasets, we show that the proposed method outperforms similar existing methods.

Three-Dimensional Liver Image Segmentation Using Generative Adversarial Networks Based on Feature Restoration.

Medical imaging provides a powerful tool for medical diagnosis. In the process of computer-aided diagnosis and treatment of liver cancer based on medical imaging, accurate segmentation of liver region from abdominal CT images is an important step. However, due to defects of liver tissue and limitations of CT imaging procession, the gray level of liver region in CT image is heterogeneous, and the boundary between the liver and those of adjacent tissues and organs is blurred, which makes the liver segmentation an extremely difficult task. In this study, aiming at solving the problem of low segmentation accuracy of the original 3D U-Net network, an improved network based on the three-dimensional (3D) U-Net, is proposed. Moreover, in order to solve the problem of insufficient training data caused by the difficulty of acquiring labeled 3D data, an improved 3D U-Net network is embedded into the framework of generative adversarial networks (GAN), which establishes a semi-supervised 3D liver segmentation optimization algorithm. Finally, considering the problem of poor quality of 3D abdominal fake images generated by utilizing random noise as input, deep convolutional neural networks (DCNN) based on feature restoration method is designed to generate more realistic fake images. By testing the proposed algorithm on the LiTS-2017 and KiTS19 dataset, experimental results show that the proposed semi-supervised 3D liver segmentation method can greatly improve the segmentation performance of liver, with a Dice score of 0.9424 outperforming other methods.

Technical note: The effect of image annotation with minimal manual interaction for semiautomatic prostate segmentation in CT images using fully convolutional neural networks.

The goal is to study the performance improvement of a deep learning algorithm in three-dimensional (3D) image segmentation through incorporating minimal user interaction into a fully convolutional neural network (CNN). A U-Net CNN was trained and tested for 3D prostate segmentation in computed tomography (CT) images. To improve the segmentation accuracy, the CNN's input images were annotated with a set of border landmarks to supervise the network for segmenting the prostate. The network was trained and tested again with annotated images after 5, 10, 15, 20, or 30 landmark points were used. Compared to fully automatic segmentation, the Dice similarity coefficient increased up to 9% when 5-30 sparse landmark points were involved, with the segmentation accuracy improving as more border landmarks were used. When a limited number of sparse border landmarks are used on the input image, the CNN performance approaches the interexpert observer difference observed in manual segmentation.

Fusing 2D and 3D convolutional neural networks for the segmentation of aorta and coronary arteries from CT images

Automated segmentation of three-dimensional medical images is of great importance for the detection and quantification of certain diseases such as stenosis in the coronary arteries. Many 2D and 3D deep learning models, especially deep convolutional neural networks (CNNs), have achieved state-of-the-art segmentation performance on 3D medical images. Yet, there is a trade-off between the field of view and the utilization of inter-slice information when using pure 2D or 3D CNNs for 3D segmentation, which compromises the segmentation accuracy. In this paper, we propose a two-stage strategy that retains the advantages of both 2D and 3D CNNs and apply the method for the segmentation of the human aorta and coronary arteries, with stenosis, from computed tomography (CT) images. In the first stage, a 2D CNN, which can extract large-field-of-view information, is used to segment the aorta and coronary arteries simultaneously in a slice-by-slice fashion. Then, in the second stage, a 3D CNN is applied to extract the inter-slice information to refine the segmentation of the coronary arteries in certain subregions not resolved well in the first stage. We show that the 3D network of the second stage can improve the continuity between slices and reduce the missed detection rate of the 2D CNN. Compared with directly using a 3D CNN, the two-stage approach can alleviate the class imbalance problem caused by the large non-coronary artery (aorta and background) and the small coronary artery and reduce the training time because the vast majority of negative voxels are excluded in the first stage. To validate the efficacy of our method, extensive experiments are carried out to compare with other approaches based on pure 2D or 3D CNNs and those based on hybrid 2D-3D CNNs.

A novel weighted spatial T‐spherical fuzzy C‐means algorithms with bias correction for image segmentation

Fuzzy c-means (FCM) is a time-honored method for its simplicity of calculation and ease of understanding. However, the previous image segmentation work exploiting FCM could not achieve the ideal effect in image edge recognition of the fuzzy information or antinoise of the pixel. This paper exploits the spatial T-spherical fuzzy c-means model with bias correction ( E M- s T S F C M p q ) to improves the effect of antinoise and image edge recognition. First, the image is transformed into a T-spherical fuzzy set by a novel T-spherical fuzzification technology with the processing of the bias fields. Second, the membership degree of the image is updated through the proposed T-spherical FCM algorithm. Third, a novel calculation method of weighted combination is exploited to integrate spatial information to overcome the noise. The proposed method is applied to three-dimensional MR image segmentation and the Berkeley segmentation datasets to illustrate the accuracy and efficiency.

Accelerating 3D Medical Image Segmentation by Adaptive Small-Scale Target Localization.

The prevailing approach for three-dimensional (3D) medical image segmentation is to use convolutional networks. Recently, deep learning methods have achieved human-level performance in several important applied problems, such as volumetry for lung-cancer diagnosis or delineation for radiation therapy planning. However, state-of-the-art architectures, such as U-Net and DeepMedic, are computationally heavy and require workstations accelerated with graphics processing units for fast inference. However, scarce research has been conducted concerning enabling fast central processing unit computations for such networks. Our paper fills this gap. We propose a new segmentation method with a human-like technique to segment a 3D study. First, we analyze the image at a small scale to identify areas of interest and then process only relevant feature-map patches. Our method not only reduces the inference time from 10 min to 15 s but also preserves state-of-the-art segmentation quality, as we illustrate in the set of experiments with two large datasets.

A Review of Publicly Available Automatic Brain Segmentation Methodologies, Machine Learning Models, Recent Advancements, and Their Comparison.

Background:The noninvasive study of the structure and functions of the brain using neuroimaging techniques is increasingly being used for its clinical and research perspective. The morphological and volumetric changes in several regions and structures of brains are associated with the prognosis of neurological disorders such as Alzheimer’s disease, epilepsy, schizophrenia, etc. and the early identification of such changes can have huge clinical significance. The accurate segmentation of three-dimensional brain magnetic resonance images into tissue types (i.e., grey matter, white matter, cerebrospinal fluid) and brain structures, thus, has huge importance as they can act as early biomarkers. The manual segmentation though considered the “gold standard” is time-consuming, subjective, and not suitable for bigger neuroimaging studies. Several automatic segmentation tools and algorithms have been developed over the years; the machine learning models particularly those using deep convolutional neural network (CNN) architecture are increasingly being applied to improve the accuracy of automatic methods.Purpose:The purpose of the study is to understand the current and emerging state of automatic segmentation tools, their comparison, machine learning models, their reliability, and shortcomings with an intent to focus on the development of improved methods and algorithms.Methods:The study focuses on the review of publicly available neuroimaging tools, their comparison, and emerging machine learning models particularly those based on CNN architecture developed and published during the last five years.Conclusion:Several software tools developed by various research groups and made publicly available for automatic segmentation of the brain show variability in their results in several comparison studies and have not attained the level of reliability required for clinical studies. The machine learning models particularly three dimensional fully convolutional network models can provide a robust and efficient alternative with relation to publicly available tools but perform poorly on unseen datasets. The challenges related to training, computation cost, reproducibility, and validation across distinct scanning modalities for machine learning models need to be addressed.

Application of CT Image Based on Three-Dimensional Image Segmentation Algorithm in Diagnosis of Osteoarthritis

Objective: To investigate the application of multi-slice spiral CT in degenerative changes of lumbar facet joints using the LOG algorithm. Methods: The CT findings of 100 cases of degenerative vertebral facet joint disease were reviewed and analyzed in this paper. Results: The main CT manifestations of facet disease are osteophyte formation, articular hyperplasia and hypertrophy, osteosclerosis, narrowing of joint space, articular surface destruction, joint capsule calcification, joint gas accumulation, joint subluxation, and lateral recesses and vertebrae. Signs such as narrow mesopores. Conclusion: The multi-slice spiral CT (MSCT) and multi-planar reconstruction (MPR) techniques are analyzed by the LOG operator algorithm. It is found that the two techniques can fully display the anatomical structure and pathological changes of the vertebral facet joints, and are useful for the diagnosis of facet joint disease. Provide enough imaging evidence.

Macular hole morphology and measurement using an automated three-dimensional image segmentation algorithm

ObjectiveFull-thickness macular holes (MH) are classified principally by size, which is one of the strongest predictors of anatomical and visual success. Using a three-dimensional (3D) automated image processing algorithm, we...

Self-healing in fiber-reinforced alkali-activated slag composites incorporating different additives

Despite that considerable research has explored self-healing of portland cement-based materials, potential self-healing in alkali-activated binders remains largely unexplored. Therefore, this paper deploys a wide array of experimental techniques to investigate the self-healing properties of PVA fibre-reinforced alkali-activated slag-based composites incorporating crystalline additives (CA) and bentonite minerals (BN). X-ray microcomputed tomography (μCT) with three-dimensional image segmentation and analysis, scanning electron microscopy with energy dispersive X-ray (SEM-EDS) analysis, inductively coupled plasma optical emission spectroscopy (ICP-OES), optical microscopy, and water sorptivity measurements were used to visualize, quantify, and identify the nature of healing compounds. While specimens incorporating CA exhibited significant self-healing, less self-healing was observed in specimens incorporating bentonite. X-ray μCT results indicated that the healing mechanism mainly occurred at surface cracks and calcium carbonate was the prevalent self-healing product formed. It was found that rising the pH and concentration of calcium ions of the curing medium and using polyvinyl alcohol (PVA) fibers in the mixture acted synergistically to accelerate crack self-healing in alkali-activated slag composites.

Three-Dimensional Image Segmentation of Upper Airway by Cone Beam CT: A Review of Literature

The aim of this paper is to review the different approaches of three-dimensional image segmentation using cone-beam computed tomography (CBCT), with a focus on the human upper airway. Literature reviews in the dental field have been included, relating to the use of CBCT to assess the upper airway using image segmentation. Obstruction of the upper airway often modifies normal breathing, which can have a noticeable impact on the typical development of craniofacial structures. CBCT is a modality that allows for the improved understanding of airway anatomy, pathology, and upper airway analysis. It is more accurate, efficient and has a relatively less radiation dose compared to multi-detector CT. Three-dimensional (3D) models of the upper airway that have been segmented and designed from CBCT images can be used to visualize and analyze treatment efficiency in subjects with breathing or obstruction disorders. The accuracy of these 3D morpho-functional analytical models is essential for improving diagnosis, treatment planning, and assessing treatment outcomes of the upper airway. Therefore, the purpose of the present review is to discuss the different methods for upper airway segmentation using CBCT to achieve accurate modeling.