Symmetric Surface Research Articles

DCO-Net: Deformable convolution-based O-shape network for fully automated placenta segmentation

The placenta is an important organ that connects the fetus with the mother during pregnancy. Many pregnancies fail because of the placental abnormality. Nowadays, placental segmentation techniques based on magnetic resonance (MR) images are increasingly used to assist in the diagnosis of placental abnormalities. However, manual segmentation of the placenta is unreliable and time-consuming. In comparison to manually segmentation methods, the CNN-based methods perform better. However, due to the limitations of the fixed geometric structures in the traditional convolution, the features of the edge are not completely obtained. That is the challenge in placenta segmentation. By incorporating offsets into the regular grid sampling locations of standard convolution, deformable convolution allows for the utilization of spatial information to its full potential. Therefore, in this paper, we proposed a fully automatic placental segmentation method based on deformable convolution, termed as Deformable Convolution O-shape Network (DCO-Net). Specifically, we adopt the U-shaped architecture and employed a bi-directional O-shaped route by adding backward skip connections. The bi-directional O-shaped route recurrently reused the building blocks without introducing any extra parameters. To evaluate the performance of the proposed DCO-Net, a total of 871 MR images from 20 pregnant women were used for the experiments. The dice and average symmetric surface distance (ASSD) obtained by DCO-Net were 0.8488 and 3.0976, respectively. The experimental results demonstrate that the proposed DCO-Net is more efficient for automatic placental segmentation compared with the other competitive approaches.

Automated Computer-Assisted Medical Decision-Making System Based on Morphological Shape and Skin Thickness Analysis for Asymmetry Detection in Mammographic Images.

Breast cancer is a significant health concern for women, emphasizing the need for early detection. This research focuses on developing a computer system for asymmetry detection in mammographic images, employing two critical approaches: Dynamic Time Warping (DTW) for shape analysis and the Growing Seed Region (GSR) method for breast skin segmentation. The methodology involves processing mammograms in DICOM format. In the morphological study, a centroid-based mask is computed using extracted images from DICOM files. Distances between the centroid and the breast perimeter are then calculated to assess similarity through Dynamic Time Warping analysis. For skin thickness asymmetry identification, a seed is initially set on skin pixels and expanded based on intensity and depth similarities. The DTW analysis achieves an accuracy of 83%, correctly identifying 23 possible asymmetry cases out of 20 ground truth cases. The GRS method is validated using Average Symmetric Surface Distance and Relative Volumetric metrics, yielding similarities of 90.47% and 66.66%, respectively, for asymmetry cases compared to 182 ground truth segmented images, successfully identifying 35 patients with potential skin asymmetry. Additionally, a Graphical User Interface is designed to facilitate the insertion of DICOM files and provide visual representations of asymmetrical findings for validation and accessibility by physicians.

Study of multistep Dense U-Net-based automatic segmentation for head MRI scans.

Despite extensive efforts to obtain accurate segmentation of magnetic resonance imaging (MRI) scans of a head, it remains challenging primarily due to variations in intensity distribution, which depend on the equipment and parameters used. The goal of this study is to evaluate the effectiveness of an automatic segmentation method for head MRI scans using a multistep Dense U-Net (MDU-Net) architecture. The MDU-Net-based method comprises two steps. The first step is to segment the scalp, skull, and whole brain from head MRI scans using a convolutional neural network (CNN). In the first step, a hybrid network is used to combine 2.5D Dense U-Net and 3D Dense U-Net structure. This hybrid network acquires logits in three orthogonal planes (axial, coronal, and sagittal) using 2.5D Dense U-Nets and fuses them by averaging. The resultant fused probability map with head MRI scans then serves as the input to a 3D Dense U-Net. In this process, different ratios of active contour loss and focal loss are applied. The second step is to segment the cerebrospinal fluid (CSF), white matter, and gray matter from extracted brain MRI scans using CNNs. In the second step, the histogram of the extracted brain MRI scans is standardized and then a 2.5D Dense U-Net is used to further segment the brain's specific tissues using the focal loss. A dataset of 100 head MRI scans from an OASIS-3 dataset was used for training, internal validation, and testing, with ratios of 80%, 10%, and 10%, respectively. Using the proposed approach, we segmented the head MRI scans into five areas (scalp, skull, CSF, white matter, and gray matter) and evaluated the segmentation results using the Dice similarity coefficient (DSC) score, Hausdorff distance (HD), and the average symmetric surface distance (ASSD) as evaluation metrics. We compared these results with those obtained using the Res-U-Net, Dense U-Net, U-Net++, Swin-Unet, and H-Dense U-Net models. The MDU-Net model showed DSC values of 0.933, 0.830, 0.833, 0.953, and 0.917 in the scalp, skull, CSF, white matter, and gray matter, respectively. The corresponding HD values were 2.37, 2.89, 2.13, 1.52, and 1.53mm, respectively. The ASSD values were 0.50, 1.63, 1.28, 0.26, and 0.27mm, respectively. Comparing these results with other models revealed that the MDU-Net model demonstrated the best performance in terms of the DSC values for the scalp, CSF, white matter, and gray matter. When compared with the H-Dense U-Net model, which showed the highest performance among the other models, the MDU-Net model showed substantial improvements in the HD view, particularly in the gray matter region, with a difference of approximately 9%. In addition, in terms of the ASSD, the MDU-Net model outperformed the H-Dense U-Net model, showing an approximately 7% improvements in the white matter and approximately 9% improvements in the gray matter. Compared with existing models in terms of DSC, HD, and ASSD, the proposed MDU-Net model demonstrated the best performance on average and showed its potential to enhance the accuracy of automatic segmentation for head MRI scans.

CotepRes-Net: An efficient U-Net based deep learning method of liver segmentation from Computed Tomography images

Automatic liver segmentation from CT images is challenging due to the indistinct boundaries between the liver and surrounding organs in the abdominal cavity CT. To address these limitations, we propose a deep learning model called CotepRes-Net, which is based on the U-Net architecture but with modified skip connection using a convolutional block attention module (CBAM). We introduce a modified inception block and a contextual transformer block (CoT) at the bottleneck to capture richer feature representations and leverage global contextual information to constrain the liver shape at the unclear boundary. To accelerate the network convergence in the training stage, we utilize an Efficient channel attention module (ECA) in the decoder layers and replace the convolution operation in each convolution layer with a residual structure. We evaluate the performance of the proposed model using the publicly available dataset LiTS (MICCAI 2017 Liver Tumor Segmentation Challenge) and show the segmentation results on the dataset Sliver07 (Segmentation of the Liver Competition 2007). The results show that our model achieves a dice similarity coefficient (DSC) of 0.9688, Jaccard index of 0.9422, volumetric overlap error (VOE) of 0.0578, relative absolute volume difference (RAVD) of 0.0039, average symmetric surface distance (ASSD) of 1.094 mm and maximum symmetric surface distance (MSSD) of 16.079 mm for liver segmentation. The experimental results show that the proposed method is comparable to or even outperform the state-of-the-art methods in terms of accuracy and computation cost, which can serve as a valuable tool for image-based liver analysis.

Can Deep Adult Lung Segmentation Models Generalize to the Pediatric Population?

Lung segmentation in chest X-rays (CXRs) is an important prerequisite for improving the specificity of diagnoses of cardiopulmonary diseases in a clinical decision support system. Current deep learning models for lung segmentation are trained and evaluated on CXR datasets in which the radiographic projections are captured predominantly from the adult population. However, the shape of the lungs is reported to be significantly different across the developmental stages from infancy to adulthood. This might result in age-related data domain shifts that would adversely impact lung segmentation performance when the models trained on the adult population are deployed for pediatric lung segmentation. In this work, our goal is to (i) analyze the generalizability of deep adult lung segmentation models to the pediatric population and (ii) improve performance through a stage-wise, systematic approach consisting of CXR modality-specific weight initializations, stacked ensembles, and an ensemble of stacked ensembles. To evaluate segmentation performance and generalizability, novel evaluation metrics consisting of mean lung contour distance (MLCD) and average hash score (AHS) are proposed in addition to the multi-scale structural similarity index measure (MS-SSIM), the intersection of union (IoU), Dice score, 95% Hausdorff distance (HD95), and average symmetric surface distance (ASSD). Our results showed a significant improvement (p < 0.05) in cross-domain generalization through our approach. This study could serve as a paradigm to analyze the cross-domain generalizability of deep segmentation models for other medical imaging modalities and applications.

Observation of momentum-dependent charge density wave gap in a layered antiferromagnet {textrm{Gd}}{textrm{Te}}_{3}

Charge density wave (CDW) ordering has been an important topic of study for a long time owing to its connection with other exotic phases such as superconductivity and magnetism. The R{textrm{Te}}_{3} (R = rare-earth elements) family of materials provides a fertile ground to study the dynamics of CDW in van der Waals layered materials, and the presence of magnetism in these materials allows to explore the interplay among CDW and long range magnetic ordering. Here, we have carried out a high-resolution angle-resolved photoemission spectroscopy (ARPES) study of a CDW material {textrm{Gd}}{textrm{Te}}_{3}, which is antiferromagnetic below sim mathrm {12~K}, along with thermodynamic, electrical transport, magnetic, and Raman measurements. Our ARPES data show a two-fold symmetric Fermi surface with both gapped and ungapped regions indicative of the partial nesting. The gap is momentum dependent, maximum along {overline{Gamma }}-mathrm{overline{Z}} and gradually decreases going towards {overline{Gamma }}-mathrm{overline{X}}. Our study provides a platform to study the dynamics of CDW and its interaction with other physical orders in two- and three-dimensions.

Multi-modal medical Transformers: A meta-analysis for medical image segmentation in oncology

Multi-modal medical image segmentation is a crucial task in oncology that enables the precise localization and quantification of tumors. The aim of this work is to present a meta-analysis of the use of multi-modal medical Transformers for medical image segmentation in oncology, specifically focusing on multi-parametric MR brain tumor segmentation (BraTS2021), and head and neck tumor segmentation using PET-CT images (HECKTOR2021). The multi-modal medical Transformer architectures presented in this work exploit the idea of modality interaction schemes based on visio-linguistic representations: (i) single-stream, where modalities are jointly processed by one Transformer encoder, and (ii) multiple-stream, where the inputs are encoded separately before being jointly modeled. A total of fourteen multi-modal architectures are evaluated using different ranking strategies based on dice similarity coefficient (DSC) and average symmetric surface distance (ASSD) metrics. In addition, cost indicators such as the number of trainable parameters and the number of multiply-accumulate operations (MACs) are reported. The results demonstrate that multi-path hybrid CNN-Transformer-based models improve segmentation accuracy when compared to traditional methods, but come at the cost of increased computation time and potentially larger model size.

Ultrasound image segmentation based on Transformer and U-Net with joint loss

Background Ultrasound image segmentation is challenging due to the low signal-to-noise ratio and poor quality of ultrasound images. With deep learning advancements, convolutional neural networks (CNNs) have been widely used for ultrasound image segmentation. However, due to the intrinsic locality of convolutional operations and the varying shapes of segmentation objects, segmentation methods based on CNNs still face challenges with accuracy and generalization. In addition, Transformer is a network architecture with self-attention mechanisms that performs well in the field of computer vision. Based on the characteristics of Transformer and CNNs, we propose a hybrid architecture based on Transformer and U-Net with joint loss for ultrasound image segmentation, referred to as TU-Net. Methods TU-Net is based on the encoder-decoder architecture and includes encoder, parallel attention mechanism and decoder modules. The encoder module is responsible for reducing dimensions and capturing different levels of feature information from ultrasound images; the parallel attention mechanism is responsible for capturing global and multiscale local feature information; and the decoder module is responsible for gradually recovering dimensions and delineating the boundaries of the segmentation target. Additionally, we adopt joint loss to optimize learning and improve segmentation accuracy. We use experiments on datasets of two types of ultrasound images to verify the proposed architecture. We use the Dice scores, precision, recall, Hausdorff distance (HD) and average symmetric surface distance (ASD) as evaluation metrics for segmentation performance. Results For the brachia plexus and fetal head ultrasound image datasets, TU-Net achieves mean Dice scores of 79.59% and 97.94%; precisions of 81.25% and 98.18%; recalls of 80.19% and 97.72%; HDs (mm) of 12.44 and 6.93; and ASDs (mm) of 4.29 and 2.97, respectively. Compared with those of the other six segmentation algorithms, the mean values of TU-Net increased by approximately 3.41%, 2.62%, 3.74%, 36.40% and 31.96% for the Dice score, precision, recall, HD and ASD, respectively.

Uncertainty-aware refinement framework for ovarian tumor segmentation in CECT volume.

Ovarian cancer is a highly lethal gynecological disease. Accurate and automated segmentation of ovarian tumors in contrast-enhanced computed tomography (CECT) images is crucial in the radiotherapy treatment of ovarian cancer, enabling radiologists to evaluate cancer progression and develop timely therapeutic plans. However, automatic ovarian tumor segmentation is challenging due to factors such as inhomogeneous background, ambiguous tumor boundaries, and imbalanced foreground-background, all of which contribute to high predictive uncertainty for a segmentationmodel. To tackle these challenges, we propose an uncertainty-aware refinement framework that aims to estimate and refine regions with high predictive uncertainty for accurate ovarian tumor segmentation in CECTimages. To this end, we first employ an approximate Bayesian network to detect coarse regions of interest (ROIs) of both ovarian tumors and uncertain regions. These ROIs allow a subsequent segmentation network to narrow down the search area for tumors and prioritize uncertain regions, resulting in precise segmentation of ovarian tumors. Meanwhile, the framework integrates two guidance modules that learn two implicit functions capable of mapping query features sampled according to their uncertainty to organ or boundary manifolds, guiding the segmentation network to facilitate information encoding of uncertainregions. Firstly, 367 CECT images are collected from the same hospital for experiments. Dice score, Jaccard, Recall, Positive predictive value (PPV), 95% Hausdorff distance (HD95) and Average symmetric surface distance (ASSD) for the testing group of 77 cases are 86.31%, 73.93%, 83.95%, 86.03%, 15.17 mm and 2.57 mm, all of which are significantly better than that of the other state-of-the-art models. And results of visual comparison shows that the compared methods have more mis-segmentation than our method. Furthermore, our method achieves a Dice score that is at least 20% higher than the Dice scores of other compared methods when tumor volumes are less than 20 cm , indicating better recognition ability to small regions by our method. And then, 38 CECT images are collected from another hospital to form an external testing group. Our approach consistently outperform the compared methods significantly, with the external testing group exhibiting substantial improvements across key evaluation metrics: Dice score (83.74%), Jaccard (69.55%), Recall (82.12%), PPV (81.61%), HD95 (12.31mm), and ASSD (2.32mm), robustly establishing its superiorperformance. Experimental results demonstrate that the framework significantly outperforms the compared state-of-the-art methods, with decreased under- or over-segmentation and better small tumor identification. It has the potential for clinicalapplication.

Femurs segmentation by machine learning from CT scans combined with autonomous finite elements in orthopedic and endocrinology applications

Efficient, accurate and reliable segmentation of femurs from CT-scans is of major importance for patient-specific autonomous finite element analysis (AFE) to determine bone's stiffness and strength. We present a fully automated segmentation algorithm for whole and partial femurs with or without tumors, and clinical applications of the AFE [1] in clinical practice.The segmentation is based on an U-Net convolutional neural network, resulting a 3D mask representing the desired femur in a CT scan. It is robust, independent of the scanning parameters such as slice spacing, pixel size, scanner manufacturer or the femoral length available in the scan. The U-Net was trained on 178 manually segmented femurs (23,721 images) and tested on 43. The performance evaluation resulted in a Dice similarity score (DSC) of 0.9924, intersection over union (IoU) of 0.9849, Hausdorff distance of 4.3315 mm and symmetric average surface distance (ASD) of 0.1326 mm. The algorithm is competitive with the best state-of-the-art femoral segmentation methodologies available.Based on the segmentation an automatic p-FE mesh is generated and physiological boundary conditions representing sidewise falls or stance are being applied automatically to improve the performance of the AFE described in [1]. New examples of the usage of the AFE in endocrinology and orthopedic oncology demonstrate this disruptive technology in actual clinical practice. We present the use of AFE for predicting hip fracture risk in the elderly population due to a sidewise fall and the identification of patients who require a prophylactic surgery due to metastatic tumors in their femurs.

Left atrium 4D-flow segmentation with high-resolution contrast-enhanced magnetic resonance angiography.

Atrial fibrillation (AF) leads to intracardiac thrombus and an associated risk of stroke. Phase-contrast cardiovascular magnetic resonance (CMR) with flow-encoding in all three spatial directions (4D-flow) provides a time-resolved 3D volume image with 3D blood velocity, which brings individual hemodynamic information affecting thrombus formation. As the resolution and contrast of 4D-flow are limited, we proposed a semi-automated 4D-flow segmentation method for the left atrium (LA) using a standard-of-care contrast-enhanced magnetic resonance angiography (CE-MRA) and registration technique. LA of 54 patients with AF were segmented from 4D-flow taken in sinus rhythm using two segmentation methods. (1) Phase-contrast magnetic resonance angiography (PC-MRA) was calculated from 4D-flow, and LA was segmented slice-by-slice manually. (2) LA and other structures were segmented from CE-MRA and transformed into 4D-flow coordinates by registration with the mutual information method. Overlap of volume was tested by the Dice similarity coefficient (DSC) and the average symmetric surface distance (ASSD). Mean velocity and stasis were calculated to compare the functional property of LA from two segmentation methods. LA volumes from segmentation on CE-MRA were strongly correlated with PC-MRA volume, although mean CE-MRA volumes were about 10% larger. The proposed registration scheme resulted in visually successful registration in 76% of cases after two rounds of registration. The mean of DSC of the registered cases was 0.770 ± 0.045, and the mean of ASSD was 2.704 mm ± 0.668 mm. Mean velocity had no significant difference between the two segmentation methods, and mean stasis had a 3.3% difference. The proposed CE-MRA segmentation and registration method can generate segmentation for 4D-flow images. This method will facilitate 4D-flow analysis for AF patients by making segmentation easier and overcoming the limit of resolution.

Three-dimensional measurement of laser-induced thermal mirror dynamics using photothermal vortex interferometer with azimuthal phase spectra analysis

Measurement of the thermal mirror caused by laser heating provides direct access to the optical, thermophysical, and mechanical properties of solid materials. However, the nanoscale time-dependent three-dimensional (3D) thermal mirror hinders the existing photothermal techniques to obtain the thermal mirror dynamics without sacrificing the spatial profile of the thermal mirror. To fill the gap of 3D measurement of laser-induced thermal mirror dynamics, a photothermal vortex interferometer (PTVI) is proposed. The PTVI produces a multi-ring petal-like interferogram by the coaxial coherent superposition of the high-order conjugated Laguerre-Gaussian beams. The nanoscale 3D thermal mirror with a rotationally symmetric surface profile can be encoded into different azimuthal phase shifts at the different radii of the interferogram. An azimuthal phase spectra analysis is proposed to extract the azimuthal phase shifts from the continuously captured interferograms, enabling reconstruction of the 3D thermal mirror dynamics. The experimental results verified that the PTVI can measure the surface deformation due to the thermal mirror at an accuracy of picometer scale, and the optical and thermophysical properties of an optical glass sample can be determined with the assistance of the thermophysical model of the 3D thermal mirror dynamics. The approach can be an effective tool for characterization of solid materials.

A Cascaded Deep Learning-Based Cardiac Substructures Segmentation Frame and on Non-Gated Non-Enhanced Planning CT Scans in Breast Cancer Patients

To develop a deep learning-based segmentation frame for cardiac substructures especially coronary arteries (CAs) on non-gated non-enhanced planning computed tomography (CT) scans in breast cancer (BC) patients. Non-gated non-enhanced CT scans of 39 BC patients receiving adjuvant radiotherapy (RT) were collected. Cardiac substructures were manually labelled, including four chambers, left main (LM), left anterior descending (LAD), left circumflex (LCX) and right coronary artery (RCA). The training, validation, and test sample is 28, 7 and 4, respectively. A cascaded network, using nnUNet as the backbone, is proposed to use chambers as prior information to constrain the segmentation of CAs. The mean Dice similarity coefficient (DSC), 95th percentile Hausdorff distance (HD95) and average symmetrical surface distance (ASSD) were used as geometric metrics. Dosimetric parameters of cardiac substructures was calculated based on the segmentation frame and manually labeled contouring, respectively. The data of cardiac examination including ultrasonography, electrocardiogram before and during the follow-up after RT were retrospectively collected. The cardiac event was any symptomatic heart disease or new-onset abnormality in the cardiac examination after RT. The mean DSC of heart, atriums and ventricles of the proposed frame was 0.93, 0.90, and 0.93, respectively. As shown in Table 1, compared with direct segmentation (as baseline), the proposed frame had a better performance in terms of HD95, ASSD, and the mean dose (Dmean) absolute error for all CAs. Compared to the dosimetric parameters of the heart collected based on the manual labelled contours, the relative errors of D5, D95, and V15Gy for LAD was 4.3±7.8%, 11.7±5.9%, and 14.6±13.0% collected based on the direct segmentation contours and 2.4±4.4%, 3.9±3.1%, 8.5±6.9% collected based on the auto-segmented contours, respectively. Multivariate analysis showed that increased V15Gy of LAD was an independent cardiac toxicity risk factor ([HR] = 1.07, 95% CI 1-1.15, p = 0.0387). We developed a cascaded network for cardiac substructures segmentation with dosimetric validation on non-enhanced CT scans in breast cancer radiotherapy. This is the first attempt to use chambers as prior information for CAs' segmentation and had a superior stable performance. Accurate segmentation will help radiation oncologists to better evaluate DVHs based on substructures and thus to estimate cardiovascular risk. An optimized cardiac substructure-based dosimetric constrain may be proposed accordingly.

A Comprehensive Deep Learning Framework for Automatic Target Volumes Segmentation in Post-Operative Stereotactic Partial Breast Irradiation (S-PBI)

In S-PBI, accurate delineation of post-surgical tumor bed volume (TBV) and clinical target volume (CTV) are crucial tasks to achieve effective radiotherapy outcomes. However, manual contouring is labor intensive, time consuming, and largely relies on the experience of clinicians. We aimed to propose a deep learning (DL) approach which mimics physicians' contouring practice to accurately segment target volumes in post-operative breast CT images. Our approach incorporated domain knowledge into a 3D U-Net based DL model for breast target volumes (TBV and CTV) delineation. Our TBV segmentation approach was inspired by the marker-guidance procedure in manual delineation, where the visual clues provided by the markers assist physicians in defining TBV. For this purpose, a distance-transformation coupled with a Gaussian filter was adopted to convert markers' locations on the CT images to saliency maps. Subsequently, the CT images and the corresponding saliency maps formed a two-channel input for the segmentation model. For CTV segmentation, TBV was incorporated as an input in addition to the CT images, guiding the model to encode the location-related image features. The architecture allowed the network to emulate the oncologist's manual delineation where CTV is derived from TBV via a margin expansion, followed by correcting the extensions for anatomical barriers of tumor invasion (e.g., skin, chest wall). We retrospectively collected 175 prone CT images from 35 post-operative breast cancer patients who received 5-fraction partial breast irradiation (PBI) regimen on a Co-60 prone based S-PBI unit. The 35 patients were randomly split into 25, 5, and 5 for model training, validation, and testing respectively. We evaluated the performance of the developed DL model on the testing dataset by comparing the predicted volumes with the manually delineated contours (ground truth) using Dice similarity coefficient (DSC), 95th percentile Hausdorff distance (HD95), and average symmetric surface distance (ASD). For TBV segmentation, our model achieved mean (standard deviation) of 0.76 (±2.7), 6.76 (±1.83) mm, and 1.9 (±0.66) mm for DSC, HD95, and ASD respectively. For CTV segmentation, our model achieved 0.94 (±0.02), 2.46 (±0.5) mm, and 0.53 (±0.14) mm for DSC, HD95, and ASD respectively. The proposed auto-segmentation approach generated TBV and CTV masks in ∼11 seconds per CT volume, implying significantly improved efficiency compared to manual contouring. We developed a comprehensive DL framework mimicking clinical contouring practice for auto-segmentation of target volumes in S-PBI. The results demonstrated high levels of agreement between the predicted contours and physicians' manual contours. The approach is promising for improving the efficiency and accuracy of the on-line treatment planning workflow, such as adaptive based S-PBI.

The Influence of Alumina Airborne-Particle Abrasion on the Properties of Zirconia-Based Dental Ceramics (3Y-TZP)

The objective of this study was to assess the effect of airborne-particle abrasion with alumina particles of various sizes (50 μm, 110 μm, 250 μm) on the surface properties of 3Y-TZP (3 mol% yttria-stabilized tetragonal zirconia polycrystals). The analysis comprised the Vickers hardness test (HV1kp), a surface roughness evaluation (Ra, Rq, Rz, Rsk, Rsm, Rt, Vo, profile displays), and SEM–EDS microanalysis (scanning electron microscopy–energy-dispersive X-ray spectroscopy). Any statistical relationships were assessed using the Kruskal–Wallis one-way analysis-of-variance-by-rank test (p = 0.05). While airborne-particle abrasion with 50 µm alumina significantly increases the Vickers hardness of 3Y-TZP (1678.8 HV1kp), 110 μm and 250 μm alumina particles do not appear to have any such effect. The surface roughness of 3Y-TZP increases with the size of the alumina particles (Ra, Rq, Rz, Rt, Vo: vertical plane and retention volume assessment); a higher Rsm (horizontal plane assessment) was noted for specimens air-abraded with 250 µm alumina particles; air abrasion with 50 μm alumina particles yielded a symmetrical surface roughness profile (Rsk). The major topographic pattern of the surface of 3Y-TZP is altered by alumina airborne-particle abrasion, with larger alumina particles causing more severe changes. Aluminum traces on the 3Y-TZP surface are observed. The shape of the alumina particles is irregular, with rough edges; the size of the alumina particles plays a significant role in the air abrasion of zirconia-based dental ceramics, affecting their morphology and properties.

Impact behaviour of tunnel lining assembled from non-planar interlocking steel fibre reinforced concrete bricks

In this study, a new design of tunnel lining assembled from non-planar interlocking segments was developed and tested. The new tunnel segment is made of steel fibre reinforced concrete (SFRC), and it has six symmetrical side surfaces with a concave-convex topology, which can prevent the movement of each element in the interlocked tunnel. A series of lab-scale drop weight tests were conducted to investigate the performance of the interlocking SFRC tunnel specimen under impact. A special aluminium mould was designed to cast the developed tunnel segments, and a novel testing frame was developed to allow the drop hammer to hit on the inner surface of the tunnel specimen. The peak impact force, the displacement of the drop hammer and the failure pattern of the interlocking SFRC brick were measured and analysed, considering the influences of hammer dropping height and confining load on the dynamic behaviour of the interlocking SFRC tunnel specimen.

3D PET/CT tumor segmentation based on nnU-Net with GCN refinement

Objective. Whole-body positron emission tomography/computed tomography (PET/CT) scans are an important tool for diagnosing various malignancies (e.g. malignant melanoma, lymphoma, or lung cancer), and accurate segmentation of tumors is a key part of subsequent treatment. In recent years, convolutional neural network based segmentation methods have been extensively investigated. However, these methods often give inaccurate segmentation results, such as oversegmentation and undersegmentation. To address these issues, we propose a postprocessing method based on a graph convolutional network (GCN) to refine inaccurate segmentation results and improve the overall segmentation accuracy. Approach. First, nnU-Net is used as an initial segmentation framework, and the uncertainty in the segmentation results is analyzed. Certain and uncertain pixels are used to establish the nodes of a graph. Each node and its 6 neighbors form an edge, and 32 nodes are randomly selected as uncertain nodes to form edges. The highly uncertain nodes are used as the subsequent refinement targets. Second, the nnU-Net results of the certain nodes are used as labels to form a semisupervised graph network problem, and the uncertain part is optimized by training the GCN to improve the segmentation performance. This describes our proposed nnU-Net + GCN segmentation framework. Main results. We perform tumor segmentation experiments with the PET/CT dataset from the MICCIA2022 autoPET challenge. Among these data, 30 cases are randomly selected for testing, and the experimental results show that the false-positive rate is effectively reduced with nnU-Net + GCN refinement. In quantitative analysis, there is an improvement of 2.1% for the average Dice score, 6.4 for the 95% Hausdorff distance (HD95), and 1.7 for the average symmetric surface distance. Significance. The quantitative and qualitative evaluation results show that GCN postprocessing methods can effectively improve the tumor segmentation performance.

Adsorption of Random Copolymer on a Chemically Heterogeneous Periodic Stripe-Patterned Surface

The adsorption of a single random copolymer chain containing correlations in a sequence on the chemically heterogeneous periodic surface with the alternating striped texture is studied theoretically. The problem is solved within the framework of a partially directed walk polymer model in three dimensions using the generating functions approach and the annealed disorder approximation for averaging over the ensemble of random sequences of units in the copolymer. Dependences of the adsorption transition point on the composition of the random copolymer and the degree of correlation in the random sequence of units for various periodic surfaces are presented. It is shown that for compositionally symmetric and weakly symmetric surfaces there is the optimal composition of the random copolymer and the degree of correlation in the sequence of units, at which the inverse temperature corresponding to the adsorption transition point has a local minimum. In the case of the compositionally symmetric surface, the “optimal” random copolymer is also symmetric in composition. For surfaces with a pronounced composition asymmetry the best adsorbent is a homopolymer complementary to sites that prevail on the surface. The degree of asymmetry range, in which the dependence of the inverse transition temperature on the copolymer composition and the correlation parameter exhibits the local minimum, is fairly narrow.

Asymmetric secondary flows above geometrically symmetric surface roughness

The mean flow behaviour of a turbulent boundary layer over rough walls is expected to exhibit symmetries that govern the flow dynamics. In particular, when roughness elements are arranged in a spanwise symmetric manner, the mean flow above them should also exhibit spanwise symmetry. This symmetrical consideration has garnered substantial empirical support. We conduct direct numerical simulations (DNS) of flow over aligned cube arrays to test such symmetry considerations further. We vary the surface coverage density from 0.25 % to 6.25 %, and employ an averaging time of about 100 large-eddy turnover times, which is longer than the typical averaging time in prior DNS studies of rough-wall boundary layers. The results suggest the presence of spanwise asymmetry in the mean flow. Specifically, we observe the development of a prominent secondary vortex on one side of the cubical roughness, accompanied by a relatively smaller secondary vortex on the other side. This asymmetry becomes most pronounced when the surface coverage density is approximately 0.59 %, and diminishes as the coverage density approaches either a low or a high value. We also establish that this mean flow asymmetry is robust across variations in the domain size, the initial condition, and the placement of the cubes in the spanwise direction.

Fibroglandular tissue segmentation in breast MRI using vision transformers: a multi-institutional evaluation

Accurate and automatic segmentation of fibroglandular tissue in breast MRI screening is essential for the quantification of breast density and background parenchymal enhancement. In this retrospective study, we developed and evaluated a transformer-based neural network for breast segmentation (TraBS) in multi-institutional MRI data, and compared its performance to the well established convolutional neural network nnUNet. TraBS and nnUNet were trained and tested on 200 internal and 40 external breast MRI examinations using manual segmentations generated by experienced human readers. Segmentation performance was assessed in terms of the Dice score and the average symmetric surface distance. The Dice score for nnUNet was lower than for TraBS on the internal testset (0.909 ± 0.069 versus 0.916 ± 0.067, P < 0.001) and on the external testset (0.824 ± 0.144 versus 0.864 ± 0.081, P = 0.004). Moreover, the average symmetric surface distance was higher (= worse) for nnUNet than for TraBS on the internal (0.657 ± 2.856 versus 0.548 ± 2.195, P = 0.001) and on the external testset (0.727 ± 0.620 versus 0.584 ± 0.413, P = 0.03). Our study demonstrates that transformer-based networks improve the quality of fibroglandular tissue segmentation in breast MRI compared to convolutional-based models like nnUNet. These findings might help to enhance the accuracy of breast density and parenchymal enhancement quantification in breast MRI screening.