Image Acquisition Research Articles

Individual Cow Recognition Based on Ultra-Wideband and Computer Vision

This study’s primary goal is to use computer vision and ultra-wideband (UWB) localisation techniques to automatically mark numerals in cow photos. In order to accomplish this, we created a UWB-based cow localisation system that involves installing tags on cow heads and placing several base stations throughout the farm. The system can determine the distance between each base station and the cow using wireless communication technology, which allows it to determine the cow’s current location coordinates. The study employed a neural network to train and optimise the ranging data gathered in the 1–20 m range in order to solve the issue of significant ranging errors in conventional UWB positioning systems. The experimental data indicates that the UWB positioning system’s unoptimized range error has an absolute mean of 0.18 m and a standard deviation of 0.047. However, when using a neural network-trained model, the ranging error is much decreased, with an absolute mean of 0.038 m and a standard deviation of 0.0079. The average root mean square error (RMSE) of the positioning coordinates is decreased to 0.043 m following the positioning computation utilising the optimised range data, greatly increasing the positioning accuracy. This study used the conventional camera shooting method for image acquisition. Following image acquisition, the system extracts the cow’s coordinate information from the image using a perspective transformation method. This allows for accurate cow identification and number labelling when compared to the location coordinates. According to the trial findings, this plan, which integrates computer vision and UWB positioning technologies, achieves high-precision cow labelling and placement in the optimised system and greatly raises the degree of automation and precise management in the farming process. This technology has many potential applications, particularly in the administration and surveillance of big dairy farms, and it offers a strong technical basis for precision farming.

2D surface optical reflectance for use in harsh reactive environments.

In recent years, studies of surfaces at more realistic conditions has advanced significantly, leading to an increased understanding of surface dynamics under reaction conditions. The development has mainly been due to the development of new experimental techniques or new experimental approaches. Techniques such as High Pressure Scanning Tunneling/Force Microscopy, Ambient Pressure x-ray Photo emission Spectroscopy, Surface x-ray Diffraction, Polarization-Modulation InfraRed Reflection Absorption Spectroscopy and Planar Laser Induced Fluorescence at semi-realistic conditions has been used to study planar model catalysts or industrial materials under operating conditions. 2D-Surface Optical Reflectance has recently received attention as a useful experimental tool used in gaseous and liquid harsh conditions by providing complementary experimental information on planar model samples as well as being a powerful experimental tool on its own. The simplicity of the approach and the cost of the equipment makes it an attractive alternative and useful tool for surface science studies under reaction conditions. In this topical review, we review some recent studies that have been promoted by the technical development in optical components, image acquisition and computational image analysis.

Autonomous robotic ultrasound scanning system: a key to enhancing image analysis reproducibility and observer consistency in ultrasound imaging

PurposeThis study aims to develop an autonomous robotic ultrasound scanning system (auto-RUSS) pipeline, comparing its reproducibility and observer consistency in image analysis with physicians of varying levels of expertise.Design/methodology/approachAn auto-RUSS was engineered using a 7-degree-of-freedom robotic arm, with real-time regulation based on force control and ultrasound visual servoing. Two phantoms were employed for the human-machine comparative experiment, involving three groups: auto-RUSS, non-expert (4 junior physicians), and expert (4 senior physicians). This setup enabled comprehensive assessment of reproducibility in contact force, image acquisition, image measurement and AI-assisted classification. Radiological feature variability was measured using the coefficient of variation (COV), while performance and reproducibility assessments utilized mean and standard deviation (SD).FindingsThe auto-RUSS had the potential to reduce operator-dependent variability in ultrasound examinations, offering enhanced repeatability and consistency across multiple dimensions including probe contact force, images acquisition, image measurement, and diagnostic model performance.Originality/valueIn this paper, an autonomous robotic ultrasound scanning system (auto-RUSS) pipeline was proposed. Through comprehensive human-machine comparison experiments, the auto-RUSS was shown to effectively improve the reproducibility of ultrasound images and minimize human-induced variability.

Surface defect detection on industrial drum rollers: Using enhanced YOLOv8n and structured light for accurate inspection.

Drum roller surface defect detection is of great research significance for control production quality. Aiming at solving the problems that the traditional light source visual imaging system, which does not clearly reflect defect features, the defect detection efficiency is low, and the accuracy is not enough, this paper designs an image acquisition system based on line fringe structured light and proposes an improved deep learning network model based on YOLOv8n to achieve efficient detection of defects on the rolling surface of a drum roller. In the aspect of image acquisition, this paper selected the line fringe structured light as the system light source, which made up for the problem that the traditional light source does not reflect the defect characteristics. In terms of algorithms, firstly, using deformable convolution instead of standard convolution to enhance the feature extraction ability of the backbone network. Then, a new feature fusion module was proposed to enable the fusion network to learn additional original information. Finally, Wise-IoU was applied to replace CIoU in the loss function, so that the network pays more attention to the high-quality samples. The experimental results show that the improved YOLOv8n algorithm has a certain improvement in detection accuracy. The main average accuracy (mAP) is 97.2%, and the detection time is 4.3ms. The system and algorithm designed in this paper can better ensure the production quality of drum rollers. While effective, the model's standard rectangular bounding boxes may limit precision for elongated defects. Future work could explore rotated bounding boxes and broader dataset diversity to enhance generalization in real-world applications.

Barriers and Facilitators to the Preadoption of a Computer-Aided Diagnosis Tool for Cervical Cancer: Qualitative Study on Health Care Providers' Perspectives in Western Cameroon.

Computer-aided detection and diagnosis (CAD) systems can enhance the objectivity of visual inspection with acetic acid (VIA), which is widely used in low- and middle-income countries (LMICs) for cervical cancer detection. VIA's reliance on subjective health care provider (HCP) interpretation introduces variability in diagnostic accuracy. CAD tools can address some limitations; nonetheless, understanding the contextual factors affecting CAD integration is essential for effective adoption and sustained use, particularly in resource-constrained settings. This study investigated the barriers and facilitators perceived by HCPs in Western Cameroon regarding sustained CAD tool use for cervical cancer detection using VIA. The aim was to guide smooth technology adoption in similar settings by identifying specific barriers and facilitators and optimizing CAD's potential benefits while minimizing obstacles. The perspectives of HCPs on adopting CAD for VIA were explored using a qualitative methodology. The study participants included 8 HCPs (6 midwives and 2 gynecologists) working in the Dschang district, Cameroon. Focus group discussions were conducted with midwives, while individual interviews were conducted with gynecologists to comprehend unique perspectives. Each interview was audio-recorded, transcribed, and independently coded by 2 researchers using the ATLAS.ti (Lumivero, LLC) software. The technology acceptance lifecycle framework guided the content analysis, focusing on the preadoption phases to examine the perceived acceptability and initial acceptance of the CAD tool in clinical workflows. The study findings were reported adhering to the COREQ (Consolidated Criteria for Reporting Qualitative Research) and SRQR (Standards for Reporting Qualitative Research) checklists. Key elements influencing the sustained use of CAD tools for VIA by HCPs were identified, primarily within the technology acceptance lifecycle's preadoption framework. Barriers included the system's ease of use, particularly challenges associated with image acquisition, concerns over confidentiality and data security, limited infrastructure and resources such as the internet and device quality, and potential workflow changes. Facilitators encompassed the perceived improved patient care, the potential for enhanced diagnostic accuracy, and the integration of CAD tools into routine clinical practices, provided that infrastructure and training were adequate. The HCPs emphasized the importance of clinical validation, usability testing, and iterative feedback mechanisms to build trust in the CAD tool's accuracy and utility. This study provides practical insights from HCPs in Western Cameroon regarding the adoption of CAD tools for VIA in clinical settings. CAD technology can aid diagnostic objectivity; however, data management, workflow adaptation, and infrastructure limitations must be addressed to avoid "pilotitis"-the failure of digital health tools to progress beyond the pilot phase. Effective implementation requires comprehensive technology management, including regulatory compliance, infrastructure support, and user-focused training. Involving end users can ensure that CAD tools are fully integrated and embraced in LMICs to aid cervical cancer screening.

A Skeleton-Based Method of Root System 3D Reconstruction and Phenotypic Parameter Measurement from Multi-View Image Sequence

The phenotypic parameters of root systems are vital in reflecting the influence of genes and the environment on plants, and three-dimensional (3D) reconstruction is an important method for obtaining phenotypic parameters. Based on the characteristics of root systems, being featureless, thin structures, this study proposed a skeleton-based 3D reconstruction and phenotypic parameter measurement method for root systems using multi-view images. An image acquisition system was designed to collect multi-view images for root system. The input images were binarized by the proposed OTSU-based adaptive threshold segmentation method. Vid2Curve was adopted to realize the 3D reconstruction of root systems and calibration objects, which was divided into four steps: skeleton curve extraction, initialization, skeleton curve estimation, and surface reconstruction. Then, to extract phenotypic parameters, a scale alignment method based on the skeleton was realized using DBSCAN and RANSAC. Furthermore, a small-sized root system point completion algorithm was proposed to achieve more complete root system 3D models. Based on the above-mentioned methods, a total of 30 root samples of three species were tested. The results showed that the proposed method achieved a skeleton projection error of 0.570 pixels and a surface projection error of 0.468 pixels. Root number measurement achieved a precision of 0.97 and a recall of 0.96, and root length measurement achieved an MAE of 1.06 cm, an MAPE of 2.37%, an RMSE of 1.35 cm, and an R2 of 0.99. The whole process of reconstruction in the experiment was very fast, taking a maximum of 4.07 min. With high accuracy and high speed, the proposed methods make it possible to obtain the root phenotypic parameters quickly and accurately and promote the study of root phenotyping.

Robot-Based Procedure for 3D Reconstruction of Abdominal Organs Using the Iterative Closest Point and Pose Graph Algorithms

Image-based 3D reconstruction enables robot-assisted interventions and image-guided navigation, which are emerging technologies in laparoscopy. When a robotic arm guides a laparoscope for image acquisition, hand–eye calibration is required to know the transformation between the camera and the robot flange. The calibration procedure is complex and must be conducted after each intervention (when the laparoscope is dismounted for cleaning). In the field, the surgeons and their assistants cannot be expected to do so. Thus, our approach is a procedure for a robot-based multi-view 3D reconstruction without hand–eye calibration, but with pose optimization algorithms instead. In this work, a robotic arm and a stereo laparoscope build the experimental setup. The procedure includes the stereo matching algorithm Semi Global Matching from OpenCV for depth measurement and the multiscale color iterative closest point algorithm from Open3D (v0.19), along with the multiway registration algorithm using a pose graph from Open3D (v0.19) for pose optimization. The procedure is evaluated quantitatively and qualitatively on ex vivo organs. The results are a low root mean squared error (1.1–3.37 mm) and dense point clouds. The proposed procedure leads to a plausible 3D model, and there is no need for complex hand–eye calibration, as this step can be compensated for by pose optimization algorithms.

Development and assessment of a prototype of an interproximal image receptor-holding device for use in pediatric dentistry.

To develop and test a prototype for interproximal radiography positioning intended for pediatric dentistry and compare the technical quality of image receptor-holding devices (IRHD) commonly used in clinical practice. Six prototypes, with three wedge dimensions (0.5 mm, 1 mm, and 2 mm) in upper and lower positions on the bitewing surface were compared regarding their capacity to acquire interproximal radiographs without overlapping surfaces with other IRHDs: RinnXCPTM, Hawe KerrTM, and Cone Indicator. Fifteen graduate students obtained images of deciduous molars in a child's skull and mandible. The chi-square and Fisher's exact tests were employed to analyze significant differences in the number of repetitions and failures in obtaining correct images. A one-way ANOVA assessed the difference between the mean times required for image acquisition according to each IRHD, adopting a significance level of 5%. The effectiveness of the tested devices, evaluated by the number of correct and incorrect acquisitions using Fisher's exact test, revealed a p-value of 0.057. The ANOVA demonstrated statistically significant differences in the mean acquisition times (p < 0.0000). The upper and lower wedge prototypes 2 mm performed better in acquiring only one radiograph (76.7%) and took less time to execute the technique (1.17 min and 1.12 min, respectively). The prototypes showed comparable performance to alternative IRHDs but provided advantages in time efficiency and radiation exposure.

4D Dynamic contrast-enhanced breast CT: Phantom-based reconstruction parameter optimization for iodine quantification.

Four-dimensional dynamic contrast-enhanced breast CT (4D DCE-bCT) offers promising high-resolution spatial and temporal imaging capabilities for the characterization and monitoring of breast tumors. However, the optimal combination of parameters for iodine quantification in image space remains to bedetermined. This study aims to optimize a dedicated bCT system to perform long dynamic contrast-enhanced scans with high spatio-temporal resolution while maintaining a reasonable radiation dose. Our protocol includes the acquisition of a high-quality prior image that is reconstructed with a polychromatic iterative algorithm (IMPACT). The acquisition of the post-contrast sequence is continuous but sparse and these images are reconstructed using prior image constrained compressed sensing (PICCS). A four-step optimization process is performed using images of a physical phantom. First, the optimal tube current is selected by taking the noise level into account. Second, the optimal number of angles is selected based on the absence of streak artifacts. Third, the number of iterations in IMPACT is specified at the lowest value that achieves the highest spatial resolution. Finally, the number of iterations in PICCS is determined based on the quantitative accuracy of a range of iodine concentrations. When a high-quality prior image is available, the imaging of post-contrast images can be performed using just 40 projection angles with a tube current of 32 mA. The noise level in the post-contrast images is inherited from the prior image and no streak artifacts are visible. Mean difference between the linear attenuation coefficients of samples containing iodine reconstructed with IMPACT using all 360 projections and PICCS using 40 projections is 0.0004 at most. The spatial resolution of images reconstructed with PICCS is lower than the one of IMPACT images and is concentration dependent. The cut-off frequency at 10% modulation transfer function drops from 1.55 in the prior image to 0.9 when the target with the largest concentration is evaluated. The total mean glandular dose of the protocol does not exceed 22.5mGy. This study found the optimal acquisition and reconstruction parameters for a low-dose dynamic contrast-enhanced bCT protocol. The numerical accuracy of the proposed protocol was ensured by performing a physical phantomstudy.

Detection of opening motion characteristics in DC circuit breakers based on machine vision.

A circuit breaker is a crucial component in power systems, and its operation is essential for evaluating its interruption performance. However, electromagnetic interference often affects sensor accuracy. To address this issue, this paper investigates a non-contact measurement technique for assessing the motion characteristics of circuit breakers. A motion detection method based on Franklin moments is proposed. A synchronous image acquisition platform was established using high-speed cameras to capture the motion of 252kV circuit breakers. The captured images are preprocessed, with coarse edges extracted using the Laplacian algorithm. Franklin moment convolution calculations are then applied to determine sub-pixel coordinates of the image edges based on these coarse edges. By analyzing the frame-to-frame variations of these sub-pixel coordinates, the opening motion characteristics of the circuit breaker are extracted. This method can detect the vibration parameters and bouncing phenomenon of circuit breaker motion machine in millisecond level, and the accuracy is 0.01 mm. These findings offer valuable insights for future research on circuit breaker performance.

Machine vision-based detection of key traits in shiitake mushroom caps

This study puts forward a machine vision-based prediction method to solve the problem regarding the measurement of traits in shiitake mushroom caps during the shiitake mushroom breeding process. It enables precise phenotyping through accurate image acquisition and analysis. In practical applications, this method improves the breeding process by rapidly and non-invasively assessing key traits such as the size and color of shiitake mushroom caps, which helps in efficiently screening strains and reducing human errors. Firstly, an edge detection model was established. This model is called KL-Dexined. It achieved an per-image best threshold (OIS) rate of 93.5%. Also, it reached an Optimal Dynamic Stabilization (ODS) rate of 96.3%. Moreover, its Average Precision (AP) was 97.1%. Secondly, the edge information detected by KL-Dexined was mapped onto the original image of shiitake mushroom caps, and using the OpenCV model,11 phenotypic key features including shiitake mushroom caps area, perimeter, and external rectangular length were obtained. Experimental results demonstrated that the R² between predicted values and true values was 0.97 with an RMSE as low as 0.049. After conducting correlation analysis between phenotypic features and shiitake mushroom caps weight, four most correlated phenotypic features were identified: Area, Perimeter, External rectangular width, and Long axis; they were divided into four groups based on their correlation rankings. Finally,M3 group using GWO_SVM algorithm achieved optimal performance among six mainstream machine learning models tested with an R²value of 0.97 and RMSE only at 0.038 when comparing predicted values with true values. Hence, this study provided guidance for predicting key traits in shiitake mushroom caps.

Automation of Ultrasonographic Optic Nerve Sheath Diameter Measurement: A Scoping Review.

Intracranial pressure (ICP) monitoring is a cornerstone of neurocritical care in managing severe brain injury. However, current invasive ICP monitoring methods carry significant risks, including infection and intracranial hemorrhage, and are contraindicated in certain clinical situations. Additionally, these methods are not universally available. Optic nerve sheath diameter (ONSD) measurement presents a promising noninvasive alternative for ICP monitoring, though its clinical adoption has been limited due to its operator dependence and inconsistencies in imaging acquisition and measurement techniques. Automating both ONSD image acquisition and measurement could enhance accuracy and reliability, thereby improving its utility as a noninvasive ICP estimation tool. A range of image analysis and machine learning (ML) techniques have been applied to address these challenges. In this paper, we provide a narrative review of the current literature on ONSD automation, examining the strengths and limitations of classical image analysis and ML models in improving ONSD-based ICP assessment.

Intra-individual radiomic analysis of pericoronary adipose tissue: Photon-counting detector vs energy-integrating detector CT angiography.

The impact of novel photon-counting detector (PCD)-CT technology on in-vivo radiomics is not fully understood. This study aimed to compare the intra-individual stability and reproducibility of pericoronary adipose tissue (PCAT) radiomic features between PCD-CT and energy-integrating detector (EID)-CT in patients undergoing coronary CT angiography (CCTA) on both systems. Patients undergoing clinically indicated CCTA on an EID-CT were prospectively enrolled for research PCD-CCTA within 30days. Image acquisition parameters were standardized; PCD-CT datasets were reconstructed both down-sampled to 0.6mm to match the clinical scan (PCD-CTDS) and at 0.2mm ultrahigh-resolution mode (PCD-CTUHR). Automatic PCAT segmentation was performed; a total of 110 radiomic feature classes were extracted and compared across the three datasets (EID-CT, PCD-CTDS, and PCD-CDUHR). Feature stability was assessed using paired t-test filtered for false discoveries using Benjamini-Hochberg method, and reproducibility using intraclass correlation coefficient (ICC). A total of 42 patients (34 male [81.0%]; 67.9±7.6years) were included. Feature stability was 91% for EID-CT vs. PCD-CTDS, but decreased for UHR datasets (EID-CT vs. PCD-CTUHR: 55%; PCD-CTDS vs. PCD-CTUHR: 51%). However, inter-scanner reproducibility was poor in both comparisons (EID-CT vs. PCD-CTDS median ICC: 0.43 [0.03-0.69]; EID-CT vs. PCD-CTUHR: 0.29 [0.01-0.51]). Nevertheless, reproducibility improved within PCD-CT datasets (PCD-CTDS vs. PCD-CTUHR: 0.72 [0.48-0.83]), regardless of the difference in slice thickness. Most PCAT radiomic features remained stable between EID-CT and PCD-CTDS, although inter-scanner reproducibility was poor, emphasizing the significant impact of detector technology. Conversely, reproducibility of features within PCD-CT datasets showed more consistent results, even when comparing standard to UHR.

Model for predicting milled floor surface topography by importing actual bottom tool-edge profile details via online image acquisition

Model for predicting milled floor surface topography by importing actual bottom tool-edge profile details via online image acquisition

Abstract 29: Non-gated Dual-source Photon-counting CT Angiography Can Detect Cardioembolic Sources Of Acute Ischemic Stroke At The Primary Investigation: A Retrospective Consecutive Cohort Study

Introduction: Early identification of the etiology of ischemic stroke is crucial for secondary prevention. Recent publications on dedicated ECG-gated cardiac CT showcase the potential to detect sources of cardiac embolism. Photon-counting CT (PCCT) uses semiconductors to convert X-ray photons into electrical signals directly. This allows for improved image quality and lower radiation dose compared to conventional CT. Exploiting PCCT technology with a high-pitch dual-source image acquisition may render an exceptionally high temporal resolution. We hypothesized that non-ECG-gated high-pitch dual-source PCCT angiography from the diaphragm to the brain might provide added clinical value in detecting cardiac stroke sources during initial stroke imaging while maintaining optimal brain and neck image quality. Methods: Consecutive patients with a clinical suspicion of acute stroke imaged with a PCCT system between October 4 th , 2023 and April 13 th , 2024 at a Swedish comprehensive stroke center were included. Diaphragm to brain coverage was obtained in an acquisition time of 1.3 seconds; on average, yielding a dose-length product of 360 mGy*cm for the study participants (2.34 mSv). Image quality was graded using a 4-point Likert scale. Images were assessed for cardiac stroke sources. Where available, reference standard echocardiography results were collected. Results: Of 249 consecutive stroke investigations, 193 included cardiothoracic imaging, which constituted the study population. 126 scans (65.3%) were cases with imaging confirmed ischemic stroke. The median age was 74 (IQR, 61-81) years, 99 were male (51.3%). In total, 39.4% underwent follow-up echocardiography. Image quality of the heart was excellent in 19 scans (9.8%) good in 97 (50.3%), moderate in 72 (37.3%), and poor in 5 scans (2.6%). Fourteen (7.3%) certain or probable interatrial septal defects were found in the study population. In the imaging-confirmed ischemic stroke subgroup, six certain or probable cardiac thrombi were found (4.8%). Additionally, a possible cardiac thrombus was found in 31 instances (24.6%). Three aortic valve vegetations were found (2.4%), all confirmed by echocardiography. Conclusions: Non-ECG-gated high-pitch dual-source PCCT angiography typically provides images of high quality, at virtually no time expense while maintaining a reasonably low radiation exposure. Non-gated PCCT angiography is a promising technique to be used as a primary screening method for cardioembolic stroke.

Identification and information acquisition of high-value construction solid waste combined millimeter-wave radar and convolutional neural networks.

The accumulation of construction solid waste (CSW) leads to the waste of land resources and environmental pollution, becoming a significant social problem. Identifying the amount of high-value CSW is essential for assessing the value of accumulated CSW and formulating appropriate recycling strategies. With the development of machine learning technology, CSW recognition techniques combining image acquisition devices and convolutional neural networks have been widely applied. However, most technologies are based on 2D images, making it difficult to recognize high-value CSW in accumulated CSW. This study proposes a new method to identify high-value CSW using millimeter wave radar based on penetration properties of electromagnetic waves. First, efficient imaging of CSW was achieved by optimizing the imaging algorithm. Then, CSW were classified in combination with the selected convolutional neural network (CNN) method based on the dataset constructed in the lab. At last, high-value CSW was screened by normalizing the imaging algorithm. The findings indicate that the balance between imaging effect and efficiency is achieved when the step speed and height are 200mm/s and 4mm. The optimized imaging approach effectively captures images of CSW. Compared with SegNet and PSPNet, DeepLabv3+ can identify complete bricks and reinforcing bars precisely. The accuracy can reach 85.18%. Moreover, the millimeter-wave radar can determine the location and size of waste and can potentially acquire three-dimensional and buried information about waste.

CT-based migration analysis of a tibial component compared to radiostereometric analysis : one-year results of a prospective single-group implant safety study.

Radiostereometric analysis (RSA) is considered the gold standard for in vivo migration analysis, but CT-based alternatives show comparable results in the shoulder and hip. We have previously validated a CT-based migration analysis method (CTMA) in a knee phantom compared to RSA. In this study, we validated the method in patients undergoing total knee arthroplasty (TKA). Our primary outcome measure was the difference in maximum total point motion (MTPM) between the differing methods. A total of 31 patients were prospectively studied having undergone an uncemented medial pivot knee TKA. Migrations were measured up to 12 months with marker-based and model-based RSA, and CT-RSA. Mean precision data for MTPM were 0.27 mm (SD 0.09) for marker-based RSA, 0.37 mm (SD 0.26) for model-based RSA, and 0.25 mm (SD 0.11) for CTMA. CTMA was as precise as both RSA methods (p = 0.845 and p = 0.156). At three months, MTPM showed a mean of 0.66 mm (95% CI 0.52 to 0.81) for marker-based RSA, 0.79 (95% CI 0.64 to 0.94) for model-based RSA, and 0.59 (95% CI 0.47 to 0.72) for CTMA. There was no difference between CTMA and marker-based RSA (p = 0.400), but CTMA showed lower migration than model-based RSA (p = 0.019). At 12 months, MTPM was 1.03 (95% CI 0.79 to 1.26) for marker-based RSA, 1.02 (95% CI 0.79 to 1.25) for model-based RSA, and 0.71 (95% CI 0.48 to 0.94) for CTMA. MTPM for CTMA was lower than both RSA methods (p < 0.001). Differences between migration increased between the methods from three to 12 months. Mean effective radiation doses per examination were 0.016 mSv (RSA) and 0.069 mSv (CT). Imaging time for performing RSA radiographs was 17 minutes 26 seconds (SD 7 mins 9 sec) and 4 minutes 24 seconds (SD 2 mins 3 sec) for CT. No difference in precision was found between CTMA and marker- or model-based RSA, but CTMA shows lower migration values of the tibial component at 12 months. CTMA can be used with low effective radiation doses, and CT image acquisition is faster to perform than RSA methods and may be suitable for use in ordinary clinical settings.

Abstract WP177: Impact of Imaging Acquisition Protocol on Automated ASPECTS Performance

Abstract WP177: Impact of Imaging Acquisition Protocol on Automated ASPECTS Performance

Characterizing the pore structure of a porous building material: Impacts of image acquisition, image denoising and image segmentation choices

Characterizing the pore structure of a porous building material: Impacts of image acquisition, image denoising and image segmentation choices

Protocol for functional screening of CFTR-targeted genetic therapies in patient-derived organoids using DETECTOR deep-learning-based analysis.

Here, we present a protocol for the rapid functional screening of gene editing and addition strategies in patient-derived organoids using the deep-learning-based tool DETECTOR (detection of targeted editing of cystic fibrosis transmembrane conductance regulator [CFTR] in organoids). We describe steps for wet-lab experiments, image acquisition, and CFTR function analysis by DETECTOR. We also detail procedures for applying pre-trained models and training custom models on new customized datasets. For complete details on the use and execution of this protocol, refer to Bulcaen etal.1.