Large Shape Variations Research Articles

VerFormer: Vertebrae-Aware Transformer for Automatic Spine Segmentation from CT Images.

The accurate and efficient segmentation of the spine is important in the diagnosis and treatment of spine malfunctions and fractures. However, it is still challenging because of large inter-vertebra variations in shape and cross-image localization of the spine. In previous methods, convolutional neural networks (CNNs) have been widely applied as a vision backbone to tackle this task. However, these methods are challenged in utilizing the global contextual information across the whole image for accurate spine segmentation because of the inherent locality of the convolution operation. Compared with CNNs, the Vision Transformer (ViT) has been proposed as another vision backbone with a high capacity to capture global contextual information. However, when the ViT is employed for spine segmentation, it treats all input tokens equally, including vertebrae-related tokens and non-vertebrae-related tokens. Additionally, it lacks the capability to locate regions of interest, thus lowering the accuracy of spine segmentation. To address this limitation, we propose a novel Vertebrae-aware Vision Transformer (VerFormer) for automatic spine segmentation from CT images. Our VerFormer is designed by incorporating a novel Vertebrae-aware Global (VG) block into the ViT backbone. In the VG block, the vertebrae-related global contextual information is extracted by a Vertebrae-aware Global Query (VGQ) module. Then, this information is incorporated into query tokens to highlight vertebrae-related tokens in the multi-head self-attention module. Thus, this VG block can leverage global contextual information to effectively and efficiently locate spines across the whole input, thus improving the segmentation accuracy of VerFormer. Driven by this design, the VerFormer demonstrates a solid capacity to capture more discriminative dependencies and vertebrae-related context in automatic spine segmentation. The experimental results on two spine CT segmentation tasks demonstrate the effectiveness of our VG block and the superiority of our VerFormer in spine segmentation. Compared with other popular CNN- or ViT-based segmentation models, our VerFormer shows superior segmentation accuracy and generalization.

Neural Novel Actor: Learning a Generalized Animatable Neural Representation for Human Actors.

We propose a new method for learning a generalized animatable neural human representation from a sparse set of multi-view imagery of multiple persons. The learned representation can be used to synthesize novel view images of an arbitrary person and further animate them with the user's pose control. While most existing methods can either generalize to new persons or synthesize animations with user control, none of them can achieve both at the same time. We attribute this accomplishment to the employment of a 3D proxy for a shared multi-person human model, and further the warping of the spaces of different poses to a shared canonical pose space, in which we learn a neural field and predict the person- and pose-dependent deformations, as well as appearance with the features extracted from input images. To cope with the complexity of the large variations in body shapes, poses, and clothing deformations, we design our neural human model with disentangled geometry and appearance. Furthermore, we utilize the image features both at the spatial point and on the surface points of the 3D proxy for predicting person- and pose-dependent properties. Experiments show that our method significantly outperforms the state-of-the-arts on both tasks.

Adaptive radiotherapy for muscle invasive bladder cancer: a retrospective audit of two bladder filling protocols

BackgroundRadical radiotherapy for muscle-invasive bladder cancer (MIBC) is challenging due to large variations in bladder shape, size and volume during treatment, with drinking protocols often employed to mitigate geometric uncertainties. Utilising adaptive radiotherapy together with CBCT imaging to select a treatment plan that best fits the bladder target and reduce normal tissue irradiation is an attractive option to compensate for anatomical changes. The aim of this retrospective study was to compare a bladder empty (BE) protocol to a bladder filling (BF) protocol with regards to variations in target volumes, plan of the day (PoD) selection and plan dosimetry throughout treatment.MethodsForty patients were included in the study; twenty were treated with a BE protocol and twenty with a BF protocol to a total prescribed dose of 55 Gy in 20 fractions. Small, medium and large bladder plans were generated using three different CTV to PTV margins. Bladder (CTV) volumes were delineated on planning CTs and online pre-treatment CBCTs. Differences in CTV volumes throughout treatment, plan selection, PTV volumes and resulting dose metrics were compared for both protocols.ResultsMean bladder volume differed significantly on both the planning CTs and online pre-treatment CBCTs between the protocols (p < 0.05). Significant differences in bladder volumes were observed between the planning CT and pre-treatment CBCTs for BF (p < 0.05) but not for BE (p = 0.11). Both protocols saw a significant decrease in bladder volume between first and final treatment fractions (p < 0.05). Medium plans were preferentially selected for BE whilst when using the BF protocol the small plan was chosen most frequently. With no significant change to PTV coverage between the protocols, the volume of body receiving 25.0–45.8 Gy was found to be significantly smaller for BE patients (p < 0.05).ConclusionsThis work provides evidence in favour of a BE protocol compared to a BF protocol for radical radiotherapy for MIBC. The smaller treatment volumes observed in the BE protocol led to reduced OAR and total body doses and were also observed to be more consistent throughout the treatment course. These results highlight improvements in dosimetry for patients who undergo a BE protocol for MIBC.

Label-aware Attention Network with Multi-scale Boosting for Medical Image Segmentation

Deep medical image segmentation calls for features with strong discrimination and rich scales due to ambiguous background distraction and large variations in object sizes and shapes. In this paper, we propose two modules to obtain these features. First, existing encoders tend to extract similar foreground/background features at blurry boundaries due to mixed-label feature aggregation. To enhance the discrimination of these features, a Label-Aware Attention (LAA) module is presented to reconstruct them by fusing same-label local features. The fusion is guided by local attention maps based on label-aware affinity learning. Second, instead of relying on a single encoder for scale context mining, we propose a Multi-scale Feature Boosting (MFB) module that applies parallel convolution with different receptive fields for scale embedding and integrates an additional backbone for cross-encoder scale reference. Combining LAA and MFB, a new encoder–decoder based framework is presented, where MFBs act as encoder blocks to recursively extract features with rich scale context, while LAA operates in the decoder layer to enhance the label-aware discriminativeness of features. Extensive experiments on three standard medical segmentation datasets demonstrate the effectiveness of the proposed framework.

Point based weakly semi-supervised biomarker detection with cross-scale and label assignment in retinal OCT images

Background and ObjectiveOptical coherence tomography (OCT) is currently one of the most advanced retinal imaging methods. Retinal biomarkers in OCT images are of clinical significance and can assist ophthalmologists in diagnosing lesions. Compared with fundus images, OCT can provide higher resolution segmentation. However, image annotation at the bounding box level needs to be performed by ophthalmologists carefully and is difficult to obtain. In addition, the large variation in shape of different retinal markers and the inconspicuous appearance of biomarkers make it difficult for existing deep learning-based methods to effectively detect them. To overcome the above challenges, we propose a novel network for the detection of retinal biomarkers in OCT images. MethodsWe first address the issue of labeling cost using a novel weakly semi-supervised object detection method with point annotations which can reduce bounding box-level annotation efforts. To extend the method to the detection of biomarkers in OCT images, we propose multiple consistent regularizations for point-to-box regression network to deal with the shortage of supervision, which aims to learn more accurate regression mappings. Furthermore, in the subsequent fully supervised detection, we propose a cross-scale feature enhancement module to alleviate the detection problems caused by the large-scale variation of biomarkers. We also propose a dynamic label assignment strategy to distinguish samples of different importance more flexibly, thereby reducing detection errors due to the indistinguishable appearance of the biomarkers. ResultsWhen using our detection network, our regressor also achieves an AP value of 20.83 s when utilizing a 5 % fully labeled dataset partition, surpassing the performance of other comparative methods at 5 % and 10 %. Even coming close to the 20.87 % result achieved by Point DETR under 20 % full labeling conditions. When using Group R-CNN as the point-to-box regressor, our detector achieves 27.21 % AP in the 50 % fully labeled dataset experiment. 7.42 % AP improvement is achieved compared to our detection network baseline Faster R-CNN. ConclusionsThe experimental findings not only demonstrate the effectiveness of our approach with minimal bounding box annotations but also highlight the enhanced biomarker detection performance of the proposed module. We have included a detailed algorithmic flow in the supplementary material.

Accurate object localization facilitates automatic esophagus segmentation in deep learning

BackgroundCurrently, automatic esophagus segmentation remains a challenging task due to its small size, low contrast, and large shape variation. We aimed to improve the performance of esophagus segmentation in deep learning by applying a strategy that involves locating the object first and then performing the segmentation task.MethodsA total of 100 cases with thoracic computed tomography scans from two publicly available datasets were used in this study. A modified CenterNet, an object location network, was employed to locate the center of the esophagus for each slice. Subsequently, the 3D U-net and 2D U-net_coarse models were trained to segment the esophagus based on the predicted object center. A 2D U-net_fine model was trained based on the updated object center according to the 3D U-net model. The dice similarity coefficient and the 95% Hausdorff distance were used as quantitative evaluation indexes for the delineation performance. The characteristics of the automatically delineated esophageal contours by the 2D U-net and 3D U-net models were summarized. Additionally, the impact of the accuracy of object localization on the delineation performance was analyzed. Finally, the delineation performance in different segments of the esophagus was also summarized.ResultsThe mean dice coefficient of the 3D U-net, 2D U-net_coarse, and 2D U-net_fine models were 0.77, 0.81, and 0.82, respectively. The 95% Hausdorff distance for the above models was 6.55, 3.57, and 3.76, respectively. Compared with the 2D U-net, the 3D U-net has a lower incidence of delineating wrong objects and a higher incidence of missing objects. After using the fine object center, the average dice coefficient was improved by 5.5% in the cases with a dice coefficient less than 0.75, while that value was only 0.3% in the cases with a dice coefficient greater than 0.75. The dice coefficients were lower for the esophagus between the orifice of the inferior and the pulmonary bifurcation compared with the other regions.ConclusionThe 3D U-net model tended to delineate fewer incorrect objects but also miss more objects. Two-stage strategy with accurate object location could enhance the robustness of the segmentation model and significantly improve the esophageal delineation performance, especially for cases with poor delineation results.

Age Readings and Assessment in Coastal Batoid Elasmobranchs from Small-Scale Size-Selective Fishery: The Importance of Data Comparability in Multi-Specific Assemblages

The large variation in vertebral shape and calcification observed among elasmobranch species prevents using a single method for enhancing growth bands and reading age. Further, estimating age and growth parameters can be difficult or impractical when samples are incomplete due to the bycatch of a size-selective fishery. Using a single and rapid method, age readings were obtained on the vertebrae of four batoid species, namely 53 individuals of Dasyatis pastinaca, 51 of Raja asterias, 15 of Torpedo marmorata, and 55 specimens of Torpedo torpedo, from the local small-scale trammel net fishery in the coastal waters (5–20 m depth) of the Central Tyrrhenian Sea during 2019–2021. Based on these data, a statistical routine was developed to obtain multiple estimates of age and growth parameters for incomplete samples due to size-selective fishing. The acceptable agreement between and within readers (intra and inter-reader disagreement &lt; 5%) and the rate of increase in vertebral size with body size (differently ranked across species) demonstrated the consistency of the enhancing method. The parameters estimated by the Von Bertalanffy and Gompertz growth models matched the data available in the Mediterranean Sea for the species studied, with D. pastinaca, T. torpedo, and R. asterias showing the lowest (k = 0.05–0.12), intermediate (k = 0.112–0.19), and highest (k = 0.18–0.23) growth rates, respectively, in line with the life history traits of these species. Overall, the method proved effective both in delineating band pairs in vertebrae of different species and in reliably estimating the age and growth parameters of problematic samples due to size-selective fishing. The proposed method supports the collection of comparable demographic data from other areas where similar multi-specific assemblages are bycatch of size-selective fisheries impacting potential nursery areas and other essential habitats for elasmobranchs.

Automatic segmentation of tumour and organs at risk in 3D MRI for cervical cancer radiation therapy with anatomical variations.

Cervical cancer is a common cancer in women globally, with treatment usually involving radiation therapy (RT). Accurate segmentation for the tumour site and organ-at-risks (OARs) could assist in the reduction of treatment side effects and improve treatment planning efficiency. Cervical cancer Magnetic Resonance Imaging (MRI) segmentation is challenging due to a limited amount of training data available and large inter- and intra- patient shape variation for OARs. The proposed Masked-Net consists of a masked encoder within the 3D U-Net to account for the large shape variation within the dataset, with additional dilated layers added to improve segmentation performance. A new loss function was introduced to consider the bounding box loss during training with the proposed Masked-Net. Transfer learning from a male pelvis MRI data with a similar field of view was included. The approaches were compared to the 3D U-Net which was widely used in MRI image segmentation. The data used consisted of 52 volumes obtained from 23 patients with stage IB to IVB cervical cancer across a maximum of 7 weeks of RT with manually contoured labels including the bladder, cervix, gross tumour volume, uterus and rectum. The model was trained and tested with a 5-fold cross validation. Outcomes were evaluated based on the Dice Similarity Coefficients (DSC), the Hausdorff Distance (HD) and the Mean Surface Distance (MSD). The proposed method accounted for the small dataset, large variations in OAR shape and tumour sizes with an average DSC, HD and MSD for all anatomical structures of 0.790, 30.19mm and 3.15mm respectively.

Gradient‐based eigenvalue optimization for electromagnetic cavities with built‐in mode matching

AbstractShape optimization with respect to eigenvalues of a cavity plays an important role in the design of new resonators or in the optimization of existing ones. This paper proposes a gradient‐based optimization scheme, which is enhanced with closed‐form shape derivatives of the system matrices. Based on these, accurate derivatives of eigenvalues, eigenmodes, and the cost function can be computed with respect to the geometry, which significantly reduces the computational effort of the optimizer. The work is demonstrated by applying it to the 9‐cell TESLA cavity, for which the design parameters of the computational model are considered as optimization variables to match the design criteria for devices in realistic use cases. Since eigenvalues may cross during the shape optimization of a cavity, a new algorithm based on an eigenvalue matching procedure is proposed, to ensure the optimization of the desired mode in order to also enable successful matching along large shape variations.

CRSANet: Class Representations Self-Attention network for the segmentation of thyroid nodules

Segmenting accurate thyroid nodules in medical ultrasound images is always non-trivial due to the large variation in size, shape and texture of nodules, and also the presence of surrounding tissues and organs with similar intensity values. Existing algorithms solve this by designing multi-scale spatial context modeling modules and introducing the emerging self-attention based paradigm, while failing to learn the class-wise relational context around nodules. To this end, we propose a novel coarse-to-fine framework, namely, CRSANet, which leverages both class-wise relational context and spatial multi-scale context for comprehensive context perception. Specifically, we first introduce the concept of Class Representations, which describe the overall representation of thyroid nodules in ultrasound images from a categorical perspective. Secondly, we propose a backbone-free dual-branch Class Representation Self-Attention Module (CRSA) to refine the coarse segmentation results from segmentation backbones. Concretely, Fine Class Region (FCR) branch is proposed to impose consistency between pixels and their corresponding Class Representations to correct the wrongly classified pixels. Meanwhile, Class Contextual Representation (CCR) branch is introduced to enhance the ability of pixel representations with the corresponding Class Representations. We evaluate the effectiveness and generality of CRSANet using three types of backbone networks. In the experiments, our proposed U-CRSANet achieves a mIoU of 78.86% and 81.35%, an accuracy of 97.11% and 98.63%, a Dice value of 74.26% and 87.57%, respectively, which outperforms most state-of-the-art methods on the public DDTI and TN-SCUI datasets.

Multi-scale local-global transformer with contrastive learning for biomarkers segmentation in retinal OCT images

Quantitative analysis of biomarkers in Optical Coherence Tomography (OCT) images plays an import role in the diagnosis and treatment of retinal diseases. However, biomarker segmentation in retinal OCT images is very hard due to the large variations in size and shape of retinal biomarkers, blurred boundaries, low contrast, and speckle interference. We proposed a novel Multi-scale Local-Global Transformer network (MsLGT-Net) for biomarker segmentation in retinal OCT images. The network combines the proposed Multi-scale Fusion Attention (MFA) module, Local-Global Transformer (LGT) module, and Contrastive Learning Enhancement (CLE) module to tackle the challenges of biomarker segmentation. Specifically, the proposed MFA module aims to enhance the network’s ability to learn multi-scale features of retinal biomarkers by effectively combining the local detail information and contextual semantic information of biomarkers at different scales, and improve the representation ability for different classes of biomarkers. The LGT module is designed to learn local and global information adaptively from multi-scale fused features to address the challenge of small biomarker segmentation. In addition, to distinguish features between different types of retinal biomarkers, we propose the CLE module to enhance the feature representation of different biomarkers. Our proposed method is validated on one public dataset and one local dataset. The experimental results show that the proposed method is more effective than other state-of-the-art methods.

Automated Quantification of HER2 Amplification Levels Using Deep Learning.

HER2 assessment is necessary for patient selection in anti-HER2 targeted treatment. However, manual assessment of HER2 amplification is time-costly, labor-intensive, highly subjective and error-prone. Challenges in HER2 analysis in fluorescence in situ hybridization (FISH) and dual in situ hybridization (DISH) images include unclear and blurry cell boundaries, large variations in cell shapes and signals, overlapping and clustered cells and sparse label issues with manual annotations only on cells with high confidences, producing subjective assessment scores according to the individual choices on cell selection. To address the above-mentioned issues, we have developed a soft-sampling cascade deep learning model and a signal detection model in quantifying CEN17 and HER2 of cells to assist assessment of HER2 amplification status for patient selection of HER2 targeting therapy to breast cancer. In evaluation with two different kinds of clinical datasets, including a FISH data set and a DISH data set, the proposed method achieves high accuracy, recall and F1-score for both datasets in instance segmentation of HER2 related cells that must contain both CEN17 and HER2 signals. Moreover, the proposed method is demonstrated to significantly outperform seven state of the art recently published deep learning methods, including contour proposal network (CPN), soft label-based FCN (SL-FCN), modified fully convolutional network (M-FCN), bilayer convolutional network (BCNet), SOLOv2, Cascade R-CNN and DeepLabv3+ with three different backbones (p ≤ 0.01). Clinically, anti-HER2 therapy can also be applied to gastric cancer patients. We applied the developed model to assist in HER2 DISH amplification assessment for gastric cancer patients, and it also showed promising predictive results (accuracy 97.67 ±1.46%, precision 96.15 ±5.82%, respectively).

A reduced order model for geometrically parameterized two-scale simulations of elasto-plastic microstructures under large deformations

In recent years, there has been a growing interest in understanding complex microstructures and their effect on macroscopic properties. In general, it is difficult to derive an effective constitutive law for such microstructures with reasonable accuracy and meaningful parameters. One numerical approach to bridge the scales is computational homogenization, in which a microscopic problem is solved at every macroscopic point, essentially replacing the effective constitutive model. Such approaches are, however, computationally expensive and typically infeasible in multi-query contexts such as optimization and material design. To render these analyses tractable, surrogate models that can accurately approximate and accelerate the microscopic problem over a large design space of shapes, material and loading parameters are required. In this work, we develop a reduced order model based on Proper Orthogonal Decomposition (POD), Empirical Cubature Method (ECM) and a geometrical transformation method with the following key features: (i) large shape variations of the microstructure are captured, (ii) only relatively small amounts of training data are necessary, and (iii) highly non-linear history-dependent behaviors are treated. The proposed framework is tested and examined in two numerical examples, involving two scales and large geometrical variations. In both cases, high speed-ups and accuracies are achieved while observing good extrapolation behavior.

MSSPA-GC: Multi-Scale Shape Prior Adaptation with 3D Graph Convolutions for Category-Level Object Pose Estimation

Category-level object pose estimation aims to predict the 6D object pose and size of arbitrary objects from known categories. It remains a challenge due to the large intra-class shape variation. Recently, the introduction of the shape prior adaptation mechanism into the normalized canonical coordinates (i.e., NOCS) reconstruction process has been shown to be effective in mitigating the intra-class shape variation. However, existing shape prior adaptation methods simply map the observed point cloud to the normalized object space, and the extracted object descriptors are not sufficient for the perception of the object pose. As a result, they fail to predict the pose of objects with complex geometric structures (e.g., cameras). To this end, this paper proposes a novel shape prior adaption method named MSSPA-GC for category-level object pose estimation. Specifically, our main network takes the observed instance point cloud converted from the RGB-D image and the prior shape point cloud pre-trained on the object CAD models as inputs. Then, a novel 3D graph convolution network and a PointNet-like MLP network are designed to extract pose-aware object features and shape-aware object features from these two inputs, respectively. After that, the two-stream object features are aggregated through a multi-scale feature propagation mechanism to generate comprehensive 3D object descriptors that maintain both pose-sensitive geometric stability and intra-class shape consistency. Finally, by leveraging object descriptors aware of both object pose and shape when reconstructing the NOCS coordinates, our approach elegantly achieves state-of-the-art performance on the widely used REAL275 and CAMERA25 datasets using only 25% of the parameters compared with existing shape prior adaptation models. Moreover, our method also exhibits decent generalization ability on the unconstrained REDWOOD75 dataset.

Residual Deformable Split Channel and Spatial U-Net for Automated Liver and Liver Tumour Segmentation.

Accurate segmentation of the liver and liver tumour (LT) is challenging due to its hazy boundaries and large shape variability. Although using U-Net for liver and LT segmentation achieves better results than manual segmentation, it loses spatial and channel features during segmentation, leading to inaccurate liver and LT segmentation. A residual deformable split depth-wise separable U-Net (RDSDSU-Net) is proposed to increase the accuracy of liver and LT segmentation. The residual deformable convolution layer (DCL) with deformable pooling (DP) is used in the encoder as an attention mechanism to adaptively extract liver and LT shape and position characteristics. Afterward, a convolutional spatial and channel features split graph network (CSCFSG-Net) is introduced in the middle processing layer to improve the expression capability of the liver and LT features by capturing spatial and channel features separately and to extract global contextual liver and LT information from spatial and channel features. Sub-pixel convolutions (SPC) are used in the decoder section to prevent the segmentation results from having a chequerboard artefact effect. Also, the residual deformable encoder features are combined with the decoder through summation to avoid increasing the number of feature maps (FM). Finally, the efficiency of the RDSDSU-Net is evaluated on the 3DIRCADb and LiTS datasets. The DICE score of the proposed RDSDSU-Net achieved 98.21% for liver segmentation and 93.25% for LT segmentation on 3DIRCADb. The experimental outcomes illustrate that the proposed RDSDSU-Net model achieved better segmentation results than the existing techniques.

Changes in plasticity of the pelvic girdle from infancy to late adulthood in Homo sapiens

Previous research on the effects of body mass on the pelvic girdle focused mostly on adult females and males. Because the ontogenetic plasticity level in the pelvis remains largely unknown, this study investigated how the association between body mass index (BMI) and pelvic shape changes during development. It also assessed how the large variation in pelvic shape could be explained by the number of live births in females. Data included CT scans of 308 humans from infancy to late adulthood with known age, sex, body mass, body stature, and the number of live births (for adult females). 3D reconstruction and geometric morphometrics was used to analyze pelvic shape. Multivariate regression showed a significant association between BMI and pelvic shape in young females and old males. The association between the number of live births and pelvic shape in females was not significant. Less plasticity in pelvic shape in adult females than during puberty, perhaps reflects adaptation to support the abdominopelvic organs and the fetus during pregnancy. Non-significant susceptibility to BMI in young males may reflect bone maturation accelerated by excessive body mass. Hormonal secretion and biomechanical loading associated with pregnancy may not have a long-term effect on the pelvic morphology of females.

Detection of ERBB2 and CEN17 signals in fluorescent in situ hybridization and dual in situ hybridization for guiding breast cancer HER2 target therapy

The overexpression of the human epidermal growth factor receptor 2 (HER2) is a predictive biomarker in therapeutic effects for metastatic breast cancer. Accurate HER2 testing is critical for determining the most suitable treatment for patients. Fluorescent in situ hybridization (FISH) and dual in situ hybridization (DISH) have been recognized as FDA-approved methods to determine HER2 overexpression. However, analysis of HER2 overexpression is challenging. Firstly, the boundaries of cells are often unclear and blurry, with large variations in cell shapes and signals, making it challenging to identify the precise areas of HER2-related cells. Secondly, the use of sparsely labeled data, where some unlabeled HER2-related cells are classified as background, can significantly confuse fully supervised AI learning and result in unsatisfactory model outcomes. In this study, we present a weakly supervised Cascade R-CNN (W-CRCNN) model to automatically detect HER2 overexpression in HER2 DISH and FISH images acquired from clinical breast cancer samples. The experimental results demonstrate that the proposed W-CRCNN achieves excellent results in identification of HER2 amplification in three datasets, including two DISH datasets and a FISH dataset. For the FISH dataset, the proposed W-CRCNN achieves an accuracy of 0.970±0.022, precision of 0.974±0.028, recall of 0.917±0.065, F1-score of 0.943±0.042 and Jaccard Index of 0.899±0.073. For DISH datasets, the proposed W-CRCNN achieves an accuracy of 0.971±0.024, precision of 0.969±0.015, recall of 0.925±0.020, F1-score of 0.947±0.036 and Jaccard Index of 0.884±0.103 for dataset 1, and an accuracy of 0.978±0.011, precision of 0.975±0.011, recall of 0.918±0.038, F1-score of 0.946±0.030 and Jaccard Index of 0.884±0.052 for dataset 2, respectively. In comparison with the benchmark methods, the proposed W-CRCNN significantly outperforms all the benchmark approaches in identification of HER2 overexpression in FISH and DISH datasets (p<0.05). With the high degree of accuracy, precision and recall , the results show that the proposed method in DISH analysis for assessment of HER2 overexpression in breast cancer patients has significant potential to assist precision medicine.

Crosshole Ground‐Penetrating Radar in Clay‐Rich Quaternary Deposits: Toward Characterization of High‐Loss Media

AbstractKnowing the centimeter‐to meter‐scale distribution of sand in clayey deposits is important for determining the dominating water flow pathways. Borehole information has a high vertical resolution, on the millimeter‐ to centimeter‐scale, but provides poor lateral coverage. For highly heterogeneous deposits, such as glacial diamicts, this detailed borehole information may not be sufficient for creating reliable geological models. Crosshole ground‐penetrating radar (GPR) can provide information on the decimeter‐ to meter‐scale variation between boreholes, but has mainly been used in sandy environments. In this study, we investigate whether crosshole GPR can provide information on the structural relationship of diamicts and their material properties, such as water content, bulk density, and clay content. To achieve ground truth, we compare the crosshole GPR data with geological information from boreholes and an excavation at the field site. The GPR data was analyzed comprehensively using several radar wave attributes in both time‐ and frequency domain, describing the signal velocity, strength, and shape. We found small variations in signal velocity (between 0.06 and 0.07 m/ns) but large variations in both amplitude and shape (either order of magnitude variation or doubling/tripling of attribute values). We see that the GPR response from wetter and more clayey diamicts have both lower amplitudes and lower centroid frequencies than the response from their drier and sandier counterparts. Lastly, we find that the variation in amplitude and shape attributes is better correlated to the diamicts' material properties than the signal velocity.

Hybrid neural-like P systems with evolutionary channels for multiple brain metastases segmentation

Neural-like P systems are membrane computing models inspired by natural computing. Spiking neurral (SN) P systems, a kind of neural-like P systems, are viewed as third-generation neural network models. Although real neurons have complex structures, classical SN P systems simplify the structures and corresponding mechanisms to stationary two-dimensional graphs and lack related evolution mechanisms on spikes and channels, which limits the real applications of these models. In this paper, we propose a new hybrid SN P system with evolutionary channels (HN P systems), including three new types of rules for dynamically generating or removing one-one and one-many/many-one channels with related evolutions of spikes on the hybrid neuron structures. Two dynamic regulatory factors are also presented on rules to help guide the optimization of the HN P systems automatically. Based on the new P system, a multiple brain metastases (BMs) segmentation model is developed. The experimental results indicate that the proposed models outperform the state-of-the-art methods on the BMs, which have large variations in sizes, positions and shapes, and low contrast with their surroundings. Performances on the head and neck segmentation dataset also verifies the effectiveness of the HN P system.

Uncertainty-weighted and relation-driven consistency training for semi-supervised head-and-neck tumor segmentation

Accurately segmenting nasopharyngeal carcinoma (NPC), a type of head-and-neck tumor, in computed tomography (CT) images is essential for clinical treatment. However, due to the limited number of annotated samples, large shape variations, and boundary ambiguity, NPC segmentation remains a challenging task. Semi-supervised learning (SSL), which makes use of unlabeled data, has become a popular solution for NPC segmentation. In this paper, we propose a consistency training framework for semi-supervised NPC segmentation, which includes an uncertainty-weighted prediction consistency training (UPCT) strategy and a relation-driven consistency training (RCT) strategy. Our model is an encoder–decoder architecture consisting of a shared encoder, a main decoder, and several auxiliary decoders. We apply various perturbations to the shared encoder’s output to leverage the unlabeled data and enforce consistency between the predictions of the main and auxiliary decoders. To avoid being misled by unreliable outputs during training due to annotation scarcity, we introduce uncertainty estimation into our model. The UPCT strategy emphasizes predictions with high reliability and weakens unreliable predictions. Additionally, we propose the RCT strategy to explore intrinsic relations among samples in each mini-batch, encouraging the model to extract richer semantic information. Our extensive experiments on the in-house NPC dataset and the public 2017 ACDC dataset not only demonstrate the effectiveness of the UPCT and RCT strategies but also show the superiority and generalization of our method compared to the state-of-the-art methods.