Accurate Segmentation Research Articles

Dual convolution-transformer UNet (DCT-UNet) for organs at risk and clinical target volume segmentation in MRI for cervical cancer brachytherapy

Objective. MRI is the standard imaging modality for high-dose-rate brachytherapy of cervical cancer. Precise contouring of organs at risk (OARs) and high-risk clinical target volume (HR-CTV) from MRI is a crucial step for radiotherapy planning and treatment. However, conventional manual contouring has limitations in terms of accuracy as well as procedural time. To overcome these, we propose a deep learning approach to automatically segment OARs (bladder, rectum, and sigmoid colon) and HR-CTV from female pelvic MRI.Approach. In the proposed pipeline, a coarse multi-organ segmentation model first segments all structures, from which a region of interest is computed for each structure. Then, each organ is segmented using an organ-specific fine segmentation model separately trained for each organ. To account for variable sizes of HR-CTV, a size-adaptive multi-model approach was employed. For coarse and fine segmentations, we designed a dual convolution-transformer UNet (DCT-UNet) which uses dual-path encoder consisting of convolution and transformer blocks. To evaluate our model, OAR segmentations were compared to the clinical contours drawn by the attending radiation oncologist. For HR-CTV, four sets of contours (clinical + three additional sets) were obtained to produce a consensus ground truth as well as for inter/intra-observer variability analysis.Main results. DCT-UNet achieved dice similarity coefficient (mean ± SD) of 0.932 ± 0.032 (bladder), 0.786 ± 0.090 (rectum), 0.663 ± 0.180 (sigmoid colon), and 0.741 ± 0.076 (HR-CTV), outperforming other state-of-the-art models. Notably, the size-adaptive multi-model significantly improved HR-CTV segmentation compared to a single-model. Furthermore, significant inter/intra-observer variability was observed, and our model showed comparable performance to all observers. Computation time for the entire pipeline per subject was 12.59 ± 0.79 s, which is significantly shorter than the typical manual contouring time of >15 min.Significance. These experimental results demonstrate that our model has great utility in cervical cancer brachytherapy by enabling fast and accurate automatic segmentation, and has potential in improving consistency in contouring. DCT-UNet source code is available athttps://github.com/JHU-MICA/DCT-UNet.

Multi-Source Fusion Enhanced Feature Segmentation in Remote Sensing Imagery

Abstract. With deepening application of deep learning technology in the field of remote sensing, several challenges persist in the segmentation of remote sensing optical images. These challenges include: (1) insufficient availability of deep learning-based remote sensing semantic segmentation datasets; (2) inadequate utilization of multi-source remote sensing data in the field of semantic segmentation; (3) limited sample size for effective model training, as well as the need to enhance both the speed and accuracy of model training. To address these challenges, this study introduces a multi-source remote sensing dataset consisting of 15000 data pairs, each comprising remote sensing multispectral data, synthetic aperture radar(SAR) images, land use and land cover(LULC) data, digital elevation model (DEM) data as well as the analysis data including Slope, Aspect, and Hillshade. Through the application of an end-to-end network based on pix2pix, effective fusion and feature enhancement of multi-source remote sensing data were achieved. The structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and Spectral Angle Mapper(SAM) values reached 0.84, 23.14, and 0.19, respectively, representing significant improvements over the baseline Pix2pix model’s performance metrics of 0.62, 18.84, and 0.23. In downstream semantic segmentation applications, the enhanced dataset was utilized to train semantic segmentation models for remote sensing image analysis. This approach effectively improved the training speed and segmentation accuracy of the models, with the mean intersection over union (mIoU) increasing from 0.467 to 0.481 and accuracy rising from 0.734 to 0.746. Moreover, the visual representation of remote sensing image segmentation demonstrated noticeable enhancements.

Evaluating brain volume segmentation accuracy and reliability of FreeSurfer and Neurophet AQUA at variations in MRI magnetic field strengths.

We aimed to compare the accuracy and reliability of two segmentation tools for magnetic resonance (MR) volumetry (FreeSurfer and Neurophet AQUA) at two magnetic field strengths (1.5T and 3T). We included 101 patients for the 1.5T-3T dataset and 112 for the 3T-3T dataset from three hospitals and five open-source datasets. The mean volume difference and average volume difference percentage with the change in magnetic field strength were compared between the methods. The hippocampus volume was larger with FreeSurfer than the Neurophet AQUA. In most brain regions, the Neurophet AQUA yielded a smaller average volume difference percentage (all < 10%) than FreeSurfer (all > 10%). The Neurophet AQUA exhibited more stable connectivity and regularity of the segmented components. Regarding volume, the Neurophet AQUA had effect sizes and ICCs comparable to those of FreeSurfer across the magnetic field strengths. With FreeSurfer, the original volume difference was small, whereas the average volume difference percentage was small with the Neurophet AQUA. Image segmentation took 1h with FreeSurfer and 5min with the Neurophet AQUA. When choosing an automatic segmentation method, the differences in image processing time and volume variability due to changes in the magnetic field strength of these methods should be considered.

Multi-scale dual-channel feature embedding decoder for biomedical image segmentation

Background and Objective:Attaining global context along with local dependencies is of paramount importance for achieving highly accurate segmentation of objects from image frames and is challenging while developing deep learning-based biomedical image segmentation. Several transformer-based models have been proposed to handle this issue in biomedical image segmentation. Despite this, segmentation accuracy remains an ongoing challenge, as these models often fall short of the target range due to their limited capacity to capture critical local and global contexts. However, the quadratic computational complexity is the main limitation of these models. Moreover, a large dataset is required to train those models. Methods:In this paper, we propose a novel multi-scale dual-channel decoder to mitigate this issue. The complete segmentation model uses two parallel encoders and a dual-channel decoder. The encoders are based on convolutional networks, which capture the features of the input images at multiple levels and scales. The decoder comprises a hierarchy of Attention-gated Swin Transformers with a fine-tuning strategy. The hierarchical Attention-gated Swin Transformers implements a multi-scale, multi-level feature embedding strategy that captures short and long-range dependencies and leverages the necessary features without increasing computational load. At the final stage of the decoder, a fine-tuning strategy is implemented that refines the features to keep the rich features and reduce the possibility of over-segmentation. Results:The proposed model is evaluated on publicly available LiTS, 3DIRCADb, and spleen datasets obtained from Medical Segmentation Decathlon. The model is also evaluated on a private dataset from Medical College Kolkata, India. We observe that the proposed model outperforms the state-of-the-art models in liver tumor and spleen segmentation in terms of evaluation metrics at a comparative computational cost. Conclusion:The novel dual-channel decoder embeds multi-scale features and creates a representation of both short and long-range contexts efficiently. It also refines the features at the final stage to select only necessary features. As a result, we achieve better segmentation performance than the state-of-the-art models.

Cleavage-Stage Embryo Segmentation Using SAM-Based Dual Branch Pipeline: Development and Evaluation with the CleavageEmbryo Dataset.

Embryo selection is one of the critical factors in determining the success of pregnancy in in vitro fertilization (IVF) procedures. Using artificial intelligence to aid in embryo selection could effectively address the current time-consuming, expensive, subjectively influenced process of embryo assessment by trained embryologists. However, current deep learning-based methods often focus on blastocyst segmentation, grading, or predicting cell development via time-lapse videos, often overlooking morphokinetic parameters or lacking interpretability. Given the significance of both morphokinetic and morphological evaluation in predicting the implantation potential of cleavage-stage embryos, as emphasized by previous research, there is a necessity for an automated method to segment cleavage-stage embryos to improve this process. In this article, we introduce the SAM-based Dual Branch Segmentation Pipeline for automated segmentation of blastomeres in cleavage-stage embryos. Leveraging the powerful segmentation capability of SAM, the instance branch conducts instance segmentation of blastomeres, while the semantic branch performs semantic segmentation of fragments. Due to the lack of publicly available datasets, we construct the CleavageEmbryo dataset, the first dataset of human cleavage-stage embryos with pixel-level annotations containing fragment information. We train and test a series of state-of-the-art segmentation algorithms on CleavageEmbryo. Our experiments demonstrate that our method outperforms existing algorithms in terms of objective metrics (mAP 0.748 on blastomeres, Dice 0.694 on fragments) and visual quality, enabling more accurate segmentation of cleavage-stage embryos. The code and sample data in this study can be found at: Https://github.com/12austincc/Cleavage-StageEmbryoSegmentation. Supplementary data are available at Bioinformatics online.

Contour-constrained branch U-Net for accurate left ventricular segmentation in echocardiography.

Using echocardiography to assess the left ventricular function is one of the most crucial cardiac examinations in clinical diagnosis, and LV segmentation plays a particularly vital role in medical image processing as many important clinical diagnostic parameters are derived from the segmentation results, such as ejection function. However, echocardiography typically has a lower resolution and contains a significant amount of noise and motion artifacts, making it a challenge to accurate segmentation, especially in the region of the cardiac chamber boundary, which significantly restricts the accurate calculation of subsequent clinical parameters. In this paper, our goal is to achieve accurate LV segmentation through a simplified approach by introducing a branch sub-network into the decoder of the traditional U-Net. Specifically, we employed the LV contour features to supervise the branch decoding process and used a cross attention module to facilitate the interaction relationship between the branch and the original decoding process, thereby improving the segmentation performance in the region LV boundaries. In the experiments, the proposed branch U-Net (BU-Net) demonstrated superior performance on CAMUS and EchoNet-dynamic public echocardiography segmentation datasets in comparison to state-of-the-art segmentation models, without the need for complex residual connections or transformer-based architectures. Our codes are publicly available at Anonymous Github https://anonymous.4open.science/r/Anoymous_two-BFF2/ .

Enhanced multi-layer Ramanujan decomposition: an effective rolling bearing condition monitoring method

ABSTRACT Due to the influence of strong noise, it is difficult to extract and identify the fault features of rolling bearing when it is slightly damaged, which greatly increases the risk of fault miss detection. In this paper, the enhanced multi-layer Ramanujan decomposition (EMRD) method is proposed. On the one hand, EMRD method adopts accurate spectrum segmentation technology (ASST) to realise adaptive frequency band division. ASST firstly minimises the frequency erosion caused by noise through prior-unknown blind deconvolution method based on SOSO (SOSO-PUBD), then obtains relatively pure frequency distribution through power spectral density function, and finally realises frequency band segmentation by minimum segmentation method. On the other hand, EMRD method obtains the optimal number of decomposition modes in the form of multi-layer decomposition, and selects the optimal mode by Ramanujan spectrum signal-to-noise ratio index. The comparative experimental results show that EMRD method has excellent accuracy of frequency band segmentation and meets the requirements of rolling bearing fault diagnosis.

Automatic semantic segmentation of breast cancer in DCE-MRI using DeepLabV3+ with modified ResNet50

Research on breast cancer segmentation is essential due to its high prevalence as the most common cancer in women and its occurrence in men as well. Breast cancer involves abnormal cell growth in the breast, highlighting the importance of advanced imaging. Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) is an effective technique for this purpose. Deep learning has significantly influenced medical imaging in recent years, especially in accurately segmenting tumors from MRI images. Two techniques have been proposed for breast tumor segmentation: Dilated ResNet50 (RN50D) and Parallel Layers Added ResNet50 (PLA-RN50). RN50D involves altering the dilation factor of the convolution layer within the residual block of ResNet50. PLA-RN50 entails the integration of parallel layers following the final residual block of the ResNet50 architecture. The modified architectures serve as the backbone for the DeepLabV3+ network. The DeepLabV3+ with RN50D or PLA-RN50D is a powerful and effective architecture that integrates deep feature extraction, multiscale spatial information, and precise segmentation to achieve high accuracy in lesion segmentation for breast DCE-MRI images. The proposed technique is tested on a QIN Breast DCE-MRI dataset comprising 233 images sourced from The Cancer Image Archive. The proposed method achieves a dice score of 0.92. The superior segmentation performance of DeepLabV3+ with PLA-RN50, as compared to its counterparts using ResNet18 and ResNet50, highlights the impactful modifications incorporated in PLA-RN50 for optimizing breast tumor segmentation.

Research on Improved Image Segmentation Algorithm Based on GrabCut

The classic interactive image segmentation algorithm GrabCut achieves segmentation through iterative optimization. However, GrabCut requires multiple iterations, resulting in slower performance. Moreover, relying solely on a rectangular bounding box can sometimes lead to inaccuracies, especially when dealing with complex shapes or intricate object boundaries. To address these issues in GrabCut, an improvement is introduced by incorporating appearance overlap terms to optimize segmentation energy function, thereby achieving optimal segmentation results in a single iteration. This enhancement significantly reduces computational costs while improving the overall segmentation speed without compromising accuracy. Additionally, users can directly provide seed points on the image to more accurately indicate foreground and background regions, rather than relying solely on a bounding box. This interactive approach not only enhances the algorithm’s ability to accurately segment complex objects but also simplifies the user experience. We evaluate the experimental results through qualitative and quantitative analysis. In qualitative analysis, improvements in segmentation accuracy are visibly demonstrated through segmented images and residual segmentation results. In quantitative analysis, the improved algorithm outperforms GrabCut and min_cut algorithms in processing speed. When dealing with scenes where complex objects or foreground objects are very similar to the background, the improved algorithm will display more stable segmentation results.

A point cloud segmentation algorithm based on multi-feature training and weighted random forest

Abstract Point cloud segmentation is the process of dividing point cloud data into a series of coherent subsets according to its attributes. It has been widely used in target recognition, digital protection of cultural relics, medical research and other fields. To improve the classification accuracy of point cloud and achieve accurate segmentation of objects or scenes, a point cloud segmentation algorithm based on multi–features training and weighted random forest (RF) is proposed. Firstly, the feature vector composed of 3D coordinate value, RGB value, echo intensity, point cloud density, normal direction and average curvature is used to train the SVM classifier, and the ‘one–to–one’ strategy is adopted to achieve the initial multivariate rough segmentation of point cloud. Then, the maximum information coefficient and sample correlation coefficient (SCC) are used to evaluate the correlation of the decision tree, and the decision tree is weighted accordingly to build a weak correlation weighted RF, so as to achieve further accurate segmentation of the point cloud. The experiment verifies the effectiveness of the proposed algorithm by segmenting the outdoor scene point cloud data model. The results show that the segmentation algorithm based on multi–features training and weighted RF can achieve accurate point cloud segmentation, and is an effective point cloud segmentation method.

Image segmentation of tunnel water leakage defects in complex environments using an improved Unet model

Computer vision technology provides an intelligent means for detecting tunnel water leakage areas. However, the accuracy of defect feature extraction and segmentation is limited by factors such as insufficient lighting and environmental interference inside tunnels. To address the problem, this paper proposes a tunnel water leakage area segmentation network model called Customized Side Guided-Unet (CSG-Unet), using Unet as the baseline model. The main contributions are: (1) To improve the accuracy of water leakage area extraction, a customized side guided term is introduced to direct the net’s attention to the changes in light and shade within the image. A parallel attention network module is designed to extract internal information from the guided term. Subsequently, a strengthened channel attention module aggregates the guided term and the original information to achieve accurate segmentation of water leakage areas; (2) To address the scarcity of tunnel water leakage area datasets, a basic dataset is constructed by collecting data from open-source datasets and manually gathered data in tunnels. On this basis, perspective transformation is used to change the camera viewpoint, gaussian noise is randomly added to the images in the dataset to simulate images taken in dimly lit scenes, thereby expanding the dataset and enhancing the network’s generalization. The CSG-Unet network was trained using the constructed training set, achieving a mean Intersection over Union (mi IoU) of 85.54%, a mean Dice coefficient (mi Dice) of 85.26%, and a mean Pixel Accuracy (mi PA) of 90.85%. Compared to its baseline network, U-Net (tiny), these metrics show an improvement of over 3.2% in each indicator. Finally, a visual comparison between the improved network and the baseline network further confirms that the proposed model can effectively adapt to the segmentation of water leakage areas in complex environments.

A prospective randomized study on the efficacy of real-time dynamic navigation in deep horizontal mandibular third molar extractions

PurposeThis study aimed to evaluate the clinical efficacy of applying real-time dynamic navigation (RDN) in the extraction of deep horizontal mandibular impacted third molars, hypothesizing that RDN reduces surgical time and minimizes the risk of injury to adjacent anatomical structures.MethodsA prospective study was conducted on 160 patients aged between 18 and 37 years with deep horizontal impaction of the mandibular third molar. The participants were randomly assigned to either the experimental group (receiving RDN-assisted extractions) or the control group (undergoing traditional extraction methods). Preoperative planning utilized cone beam computed tomography (CBCT) and Mimics software for the accurate localization and segmentation of impacted teeth. Parametric data were analysed via an independent t test for intergroup comparisons, and significance was set to p < 0.05.ResultsIn the experimental group, an average of 11 ± 1 min was required for preoperative planning via RDN, which was not required in the control group. The setup of the navigation system took an average of 4 ± 1 min in the experimental group and 0 min in the control group. The experimental group demonstrated a significantly shorter average surgical time (22 ± 3 min) than did the control group (36 ± 3 min). The differences in the preoperative design time, surgical time, and complication rates between the two groups were statistically significant (p = 0.005). Additionally, the RDN group reported no complications related to adjacent tooth damage or nerve injury.ConclusionThe precision, safety, real-time guidance of RDN supports its use in complicated dental extractions, which would introduce a new era of oral and maxillofacial surgery.Graphical

U-Net Semantic Segmentation-Based Calorific Value Estimation of Straw Multifuels for Combined Heat and Power Generation Processes

This paper proposes a system for real-time estimation of the calorific value of mixed straw fuels based on an improved U-Net semantic segmentation model. This system aims to address the uncertainty in heat and power generation per unit time in combined heat and power generation (CHPG) systems caused by fluctuations in the calorific value of straw fuels. The system integrates an industrial camera, moisture detector, and quality sensors to capture images of the multi-fuel straw. It applies the improved U-Net segmentation network for semantic segmentation of the images, accurately calculating the proportion of each type of straw. The improved U-Net network introduces a self-attention mechanism in the skip connections of the final layer of the encoder, replacing traditional convolutions by depthwise separable convolutions, as well as replacing the traditional convolutional bottleneck layers with Transformer encoder. These changes ensure that the model achieves high segmentation accuracy and strong generalization capability while maintaining good real-time performance. The semantic segmentation results of the straw images are used to calculate the proportions of different types of straw and, combined with moisture content and quality data, the calorific value of the mixed fuel is estimated in real time based on the elemental composition of each straw type. Validation using images captured from an actual thermal power plant shows that, under the same conditions, the proposed model has only a 0.2% decrease in accuracy compared to the traditional U-Net segmentation network, while the number of parameters is significantly reduced by 74%, and inference speed is improved 23%.

Advances in Medical Image Segmentation: A Comprehensive Review of Traditional, Deep Learning and Hybrid Approaches.

Medical image segmentation plays a critical role in accurate diagnosis and treatment planning, enabling precise analysis across a wide range of clinical tasks. This review begins by offering a comprehensive overview of traditional segmentation techniques, including thresholding, edge-based methods, region-based approaches, clustering, and graph-based segmentation. While these methods are computationally efficient and interpretable, they often face significant challenges when applied to complex, noisy, or variable medical images. The central focus of this review is the transformative impact of deep learning on medical image segmentation. We delve into prominent deep learning architectures such as Convolutional Neural Networks (CNNs), Fully Convolutional Networks (FCNs), U-Net, Recurrent Neural Networks (RNNs), Adversarial Networks (GANs), and Autoencoders (AEs). Each architecture is analyzed in terms of its structural foundation and specific application to medical image segmentation, illustrating how these models have enhanced segmentation accuracy across various clinical contexts. Finally, the review examines the integration of deep learning with traditional segmentation methods, addressing the limitations of both approaches. These hybrid strategies offer improved segmentation performance, particularly in challenging scenarios involving weak edges, noise, or inconsistent intensities. By synthesizing recent advancements, this review provides a detailed resource for researchers and practitioners, offering valuable insights into the current landscape and future directions of medical image segmentation.

Precision Segmentation of Subretinal Fluids in OCT Using Multiscale Attention-Based U-Net Architecture.

This paper presents a deep-learning architecture for segmenting retinal fluids in patients with Diabetic Macular Oedema (DME) and Age-related Macular Degeneration (AMD). Accurate segmentation of multiple fluid types is critical for diagnosis and treatment planning, but existing techniques often struggle with precision. We propose an encoder-decoder network inspired by U-Net, processing enhanced OCT images and their edge maps. The encoder incorporates Residual and Inception modules with an autoencoder-based multiscale attention mechanism to extract detailed features. Our method shows superior performance across several datasets. On the RETOUCH dataset, the network achieved F1 Scores of 0.82 for intraretinal fluid (IRF), 0.93 for subretinal fluid (SRF), and 0.94 for pigment epithelial detachment (PED). The model also performed well on the OPTIMA and DUKE datasets, demonstrating high precision, recall, and F1 Scores. This architecture significantly enhances segmentation accuracy and edge precision, offering a valuable tool for diagnosing and managing retinal diseases. Its integration of dual-input processing, multiscale attention, and advanced encoder modules highlights its potential to improve clinical outcomes and advance retinal disease treatment.

Ensemble percepts of colored targets among distractors are influenced by hue similarity, not categorical identity.

Color can be used to group similar elements, and ensemble percepts of color can be formed for such groups. In real-life settings, however, elements of similar color are often spatially interspersed among other elements and seen against a background. Forming an ensemble percept of these elements would require the segmentation of the correct color signals for integration. Can the human visual system do this? We examined whether observers can extract the ensemble mean hue from a target hue distribution among distractors and whether a color category boundary between target and distractor hues facilitates ensemble hue formation. Observers were able to selectively judge the target ensemble mean hue, but the presence of distractor hues added noise to the ensemble estimates and caused perceptual biases. The more similar the distractor hues were to the target hues, the noisier the estimates became, possibly reflecting incomplete or inaccurate segmentation of the two hue ensembles. Asymmetries between nominally equidistant distractors and substantial individual variability, however, point to additional factors beyond simple mixing of target and distractor distributions. Finally, we found no evidence for categorical facilitation in selective ensemble hue formation.

Gut Analysis Toolbox - automating quantitative analysis of enteric neurons.

The enteric nervous system (ENS) consists of an extensive network of neurons and glial cells embedded within the wall of the gastrointestinal (GI) tract. Alterations in neuronal distribution and function are strongly associated with GI dysfunction. Current methods for assessing neuronal distribution suffer from undersampling, partly due to challenges associated with imaging and analyzing large tissue areas, and operator bias due to manual analysis. We present the Gut Analysis Toolbox (GAT), an image analysis tool designed for characterization of enteric neurons and their neurochemical coding using two-dimensional images of GI wholemount preparations. GAT is developed in Fiji, has a user-friendly interface, and offers rapid and accurate segmentation via custom deep learning (DL)-based cell segmentation models developed using StarDist, as well as a ganglia segmentation model in deepImageJ. We apply proximal neighbor-based spatial analysis to reveal differences in cellular distribution across gut regions using a public dataset. In summary, GAT provides an easy-to-use toolbox to streamline routine image analysis tasks in ENS research. GAT enhances throughput, allowing rapid unbiased analysis of larger tissue areas, multiple neuronal markers and numerous samples.

Accuracy of imaging software usable in clinical settings for 3D rendering of tooth structures.

The aim of the present study was to evaluate the segmentation accuracy of the dentition by testing four open-source semi-automatic software programs. Twenty CBCT scans were selected to perform semi-automatic segmentation of the maxillary and mandibular dentition. The software programs tested were InVesalius, ITK-SNAP, 3D Slicer, and Seg3D. In addition, each tooth model was manually segmented using Mimics software; this was set as the gold standard (GS) reference of the investigation. A specific 3D imaging technology was used to perform the superimposition between the tooth models obtained with the semi-automatic software and the GS model as well as to perform the surface-to-surface matching analysis. The accuracy of semi-automatic segmentation was evaluated, calculating the volumetric mean differences (mean bias and limits of agreement) and the percentage of matching of the tooth models compared with the manual segmentation (GS). Qualitative assessments were performed using color-coded maps. All data were statistically analyzed to perform comparisons between the investigated software programs. Statistically significant differences were found in the volumetric and matching percentage data (P 0.05). InVesalius was the most accurate software program for 3D rendering of the dentition, with a volumetric bias (Mimics software) ranging from 4.59 to 85.79 mm3, while ITK-SNAP showed the highest volumetric bias, ranging from 30.22 to 319.83 mm3. The mismatched area was mainly located at the radicular tooth region. The volumetric data showed excellent inter-software reliability, with coefficient values ranging from 0.951 to 0.997. Different semi-automatic software algorithms could generate different patterns of inaccuracy error in the segmentation of teeth.

Case Report: Case report: Aslanger’s sign in electrocardiogram.

Electrocardiograms (ECGs) can be affected by various factors and technical problems. It is rare for an artefact to be the cause of ST-segment elevation, especially in asymptomatic patients. An important distinction between true ST segment elevation caused by myocardial infarction and an artefact is that the baseline elevation in an artefact may begin before or after the appearance of the QRS complex. When confronted with an abnormal ECG with suspicious waveform contours and possibly only one completely normal limb lead, the diagnosis of arterial pulse artefact should be considered. It is important to exclude subjective assessments unless they are clearly labelled as such. When encountering an abnormal ECG with suspicious waveform contours and possibly only one completely normal limb lead, the diagnosis of arterial pulse artefact should be considered.

A (sub)field guide to quality control in hippocampal subfield segmentation on high-resolution T2-weighted MRI.

Inquiries into properties of brain structure and function have progressed due to developments in magnetic resonance imaging (MRI). To sustain progress in investigating and quantifying neuroanatomical details in vivo, the reliability and validity of brain measurements are paramount. Quality control (QC) is a set of procedures for mitigating errors and ensuring the validity and reliability of brain measurements. Despite its importance, there is little guidance on best QC practices and reporting procedures. The study of hippocampal subfields in vivo is a critical case for QC because of their small size, inter-dependent boundary definitions, and common artifacts in the MRI data used for subfield measurements. We addressed this gap by surveying the broader scientific community studying hippocampal subfields on their views and approaches to QC. We received responses from 37 investigators spanning 10 countries, covering different career stages, and studying both healthy and pathological development and aging. In this sample, 81% of researchers considered QC to be very important or important, and 19% viewed it as fairly important. Despite this, only 46% of researchers reported on their QC processes in prior publications. In many instances, lack of reporting appeared due to ambiguous guidance on relevant details and guidance for reporting, rather than absence of QC. Here, we provide recommendations for correcting errors to maximize reliability and minimize bias. We also summarize threats to segmentation accuracy, review common QC methods, and make recommendations for best practices and reporting in publications. Implementing the recommended QC practices will collectively improve inferences to the larger population, as well as have implications for clinical practice and public health.