Fiducial Detection Research Articles

Vispro improves imaging analysis for Visium spatial transcriptomics.

Spatial transcriptomics (ST) is a cutting-edge technology that enables the comprehensive analysis of gene expression while preserving the spatial context of tissues. Within this framework, histological images serve a crucial role, providing spatially cohesive information that is often challenging to capture through gene expression alone. However, the imaging process in ST data presents inherent challenges, such as variability in image quality, artifacts from fiducial markers, and difficulty in distinguishing tissue regions. These challenges can significantly impair the accuracy and effectiveness of downstream analyses. To address these limitations, we developed Vispro, an end-to-end, fully automated image processing tool tailored for ST data. Vispro integrates four key modules: fiducial marker detection, marker removal and image restoration, tissue region detection, and segmentation of disconnected tissue areas. These modules systematically enhance the quality of ST images, reducing artifacts and improving tissue segmentation accuracy. Furthermore, by improving the integrity of image data, we demonstrate that Vispro facilitates downstream analyses such as cell segmentation and image registration, empowering researchers to extract more detailed and accurate biological insights from complex tissue architectures.

Variation of Seismocardiogram-Derived Cardiac Time Intervals and Heart Rate Variability Metrics Across the Sternum

Abstract Cardiac time intervals (CTIs) are vital indicators of cardiac health and can be noninvasively assessed using a combination of electrocardiography (ECG) and seismocardiography (SCG), a method of capturing cardiac-induced chest vibrations via accelerometers. SCG signals can be measured from different chest locations. However, more investigations are needed to evaluate the impact of sensor placement on SCG-derived cardiac parameters. This study investigates the effect of accelerometer placement along the sternum on SCG-derived CTI estimations and heart rate variability (HRV) parameters. A semi-automated algorithm was developed to detect SCG fiducial points and seven CTIs from thirteen healthy individuals. Comparative analysis with manually selected peaks and gold-standard ECG was conducted to assess fiducial point detection accuracy. Results indicate the highest recall and precision in aortic valve opening (0.84–1.00 and 0.96–1.00, respectively) and mitral valve closure (0.77–1.00 and 0.93–1.00, respectively) detection. Aortic valve closure (0.43–1.00 and 0.61–1.00, respectively) and mitral valve opening (0.64–1.00 and 0.91–1.00, respectively) detection, although slightly less accurate due to signal intensity variations, demonstrated overall effectiveness compared to manually selected peaks. Furthermore, SCG-derived heart rates showed a high correlation coefficient (r > 0.9) with the gold-standard ECG heart rates. Single-factor ANOVA revealed significant differences (p < 0.05) in SCG-derived CTI estimations based on sensor locations on the sternum, highlighting the importance of sensor placement for accurate assessments.

Gender and age classification using ASMNet based facial fiducial detection and Jordan neural network

Gender and age classification using ASMNet based facial fiducial detection and Jordan neural network

A fully automatic fiducial detection and correspondence establishing method for online C-arm calibration.

Online C-arm calibration with a mobile fiducial cage plays an essential role in various image-guided interventions. However, it is challenging to develop a fully automatic approach, which requires not only an accurate detection of fiducial projections but also a robust 2D-3D correspondence establishment. We propose a novel approach for online C-arm calibration with a mobile fiducial cage. Specifically, a novel mobile calibration cage embedded with 16 fiducials is designed, where the fiducials are arranged to form 4 line patterns with different cross-ratios. Then, an auto-context-based detection network (ADNet) is proposed to perform an accurate and robust detection of 2D projections of those fiducials in acquired C-arm images. Subsequently, we present a cross-ratio consistency-based 2D-3D correspondence establishing method to automatically match the detected 2D fiducial projections with those 3D fiducials, allowing for an accurate online C-arm calibration. We designed and conducted comprehensive experiments to evaluate the proposed approach. For automatic detection of 2D fiducial projections, the proposed ADNet achieved a mean point-to-point distance of pixels. Additionally, the proposed C-arm calibration approach achieved a mean re-projection error of pixels and a mean point-to-line distance of mm. When the proposed C-arm calibration approach was applied to downstream tasks involving landmark and surface model reconstruction, sub-millimeter accuracy was achieved. In summary, we developed a novel approach for online C-arm calibration. Both qualitative and quantitative results of comprehensive experiments demonstrated the accuracy and robustness of the proposed approach. Our approach holds potentials for various image-guided interventions.

MarkerDetector: A method for robust fiducial marker detection in electron micrographs using wavelet-based template

Fiducial marker detection in electron micrographs becomes an important and challenging task with the development of large-field electron microscopy. The fiducial marker detection plays an important role in several steps during the process of electron micrographs, such as the alignment and parameter calibrations. However, limited by the conditions of low signal-to-noise ratio (SNR) in the electron micrographs, the performance of fiducial marker detection is severely affected. In this work, we propose the MarkerDetector, a novel algorithm for detecting fiducial markers in electron micrographs. The proposed MarkerDetector is built upon the following contributions: Firstly, a wavelet-based template generation algorithm is devised in MarkerDetector. By adopting a shape-based criterion, a high-quality template can be obtained. Secondly, a robust marker determination strategy is devised by utilizing statistic-based filtering, which can guarantee the correctness of the detected fiducial markers. The average running time of our algorithm is 1.67 seconds with promising accuracy, indicating its practical feasibility for applications in electron micrographs.

Frappe: fast fiducial detection on low cost hardware

Square fiducial markers are widely used in robotics to easily obtain pose and other information about the world from camera images. Processing the images to extract the markers is usually performed centrally with standard libraries but the code is typically aimed at PC-level hardware. Platforms with constrained processing power have difficulty handling multiple camera streams at real-time refresh rates. We introduce the Frappe (Fiducial Recognition Accelerated with Parallel Processing Elements) algorithm for detecting and decoding the popular ArUco tags. Designed to be implemented on the low cost hardware of the Raspberry Pi Zero, we show tag detection and decoding on images of 640 × 480 resolution exceeding 60 Hz, five times faster than the standard ArUco library, while maintaining similar detection performance and using much less energy. Using Frappe, we demonstrate improved real-world performance on a visual navigation task with our DOTS robot.

On the use of temporal filtering for mitigating galactic synchrotron calibration bias in 21 cm reionization observations

ABSTRACT Precision antenna calibration is required for mitigating the impact of foreground contamination in 21 cm cosmological radio surveys. One widely studied source of error is the effect of missing point sources in the calibration sky model; however, poorly understood diffuse galactic emission also creates a calibration bias that can complicate the clean separation of foregrounds from the 21 cm signal. In this work, we present a technique for suppressing this bias with temporal filtering of radio interferometric visibilities observed in a drift-scan mode. We demonstrate this technique on mock simulations of the Hydrogen Epoch of Reionization Array (HERA) experiment. Inspecting the recovered calibration solutions, we find that our technique reduces spurious errors by over an order of magnitude. This improved accuracy approaches the required accuracy needed to make a fiducial detection of the 21 cm signal with HERA, but is dependent on a number of external factors that we discuss. We also explore different types of temporal filtering techniques and discuss their relative performance and trade-offs.

A unique cardiac electrocardiographic 3D model. Toward interpretable AI diagnosis.

Mathematical models of cardiac electrical activity are one of the most important tools for elucidating information about heart diagnostics. In this paper, we present an efficient mathematical formulation for this modeling simple enough to be easily parameterized and rich enough to provide realistic signals. It relies on a five dipole representation of the cardiac electric source, each one associated with the well-known waves of the electrocardiogram signal. Beyond the physical basis of the model, the parameters are physiologically interpretable as they characterize the wave shape, similar to what a physician would look for in signals, thus making them very useful in diagnosis. The model accurately reproduces the electrocardiogram signals of any diseased or healthy heart. This new discovery represents a significant advance in electrocardiography research. It is especially useful for diagnosis, patient follow-up or decision-making on new therapies; is also a promising tool for well-performing, transparent and interpretable AI approaches.

Correlation between diastolic seismocardiography variables and echocardiography variables.

Echocardiography is a key diagnostic tool for assessment of myocardial performance and haemodynamics. Seismocardiography (SCG) can potentially provide fast and reliable assessments of key components related to myocardial performance. The aims of this study were to investigate the correlation between SCG and echocardiographic measures, and a decrease in preload by raising the subjects to a 30° head-up tilt position would be detected by both echocardiography and SCG. A total of 45 subjects were included in the study. SCG and electrocardiogram were recorded simultaneously and afterwards echocardiography was recorded. The SCG signals were divided into individual heart beats using a duration-dependent Markov model. Using a fiducial point detection algorithm, the diastolic fiducial points were identified. The amplitudes from the SCG showed a high correlation, especially with the variable e' from the echocardiography. The peak-to-peak amplitude of the diastolic SCG complex and e' had a high correlation of 0.713 (P < 0.001). The second minimum in diastolic occurring after the closing of the aortic valve was the only amplitude showing a high correlation when comparing supine with head-up tilt in the SCG. All the echocardiography variables but E/e' showed a high correlation when comparing supine with head-up tilt. The results found in this study showed a high correlation between the amplitudes from the diastolic SCG and the diastolic variable e' from the echocardiography, thus indicating that the SCG could potentially be utilized to evaluate the diastolic function.

Automatic Fiducial Points Detection for Multi-facial Expressions via Invariant Features and Multi-layer Kernel Sliding Perceptron

Automatic Fiducial Points Detection for Multi-facial Expressions via Invariant Features and Multi-layer Kernel Sliding Perceptron

Computer-Aided Detection of Fiducial Points in Seismocardiography through Dynamic Time Warping.

Accelerometer-based devices have been employed in seismocardiography fiducial point detection with the aid of quasi-synchronous alignment between echocardiography images and seismocardiogram signals. However, signal misalignments have been observed, due to the heartbeat cycle length variation. This paper not only analyzes the misalignments and detection errors but also proposes to mitigate the issues by introducing reference signals and adynamic time warping (DTW) algorithm. Two diagnostic parameters, the ratio of pre-ejection period to left ventricular ejection time (PEP/LVET) and the Tei index, were examined with two statistical verification approaches: (1) the coefficient of determination (R2) of the parameters versus the left ventricular ejection fraction (LVEF) assessments, and (2) the receiver operating characteristic (ROC) classification to distinguish the heart failure patients with reduced ejection fraction (HFrEF). Favorable R2 values were obtained, R2 = 0.768 for PEP/LVET versus LVEF and R2 = 0.86 for Tei index versus LVEF. The areas under the ROC curve indicate the parameters that are good predictors to identify HFrEF patients, with an accuracy of more than 92%. The proof-of-concept experiments exhibited the effectiveness of the DTW-based quasi-synchronous alignment in seismocardiography fiducial point detection. The proposed approach may enable the standardization of the fiducial point detection and the signal template generation. Meanwhile, the program-generated annotation data may serve as the labeled training set for the supervised machine learning.

Alternative design for large scale liquid scintillator detectors

The construction of large-scale liquid scintillator detectors is complicated by the need to separate the scintillation region from photomultiplier tubes (PMTs) due to their intrinsic radioactivity. This is generally done using acrylic or nylon barriers, whose own activity can also lead to substantial reduction of the fiducial detection volume for a number of low energy (MeV) studies, and whose construction becomes increasingly difficult and expensive for larger detector volumes. Here we present an initial simulation study of an alternative geometric construction known as SLIPS (Stratified LIquid Plane Scintillator), which aims to avoid such physical barriers entirely by instead using layers of lipophobic liquids to separate PMTs from the scintillation region.

Fiducial marker recovery and detection from severely truncated data in navigation-assisted spine surgery.

Fiducial markers are commonly used in navigation-assisted minimally invasive spine surgery and they help transfer image coordinates into real-world coordinates. In practice, these markers might be located outside the field-of-view (FOV) of C-arm cone-beam computed tomography (CBCT) systems used in intraoperative surgeries, due to the limited detector sizes. As a consequence, reconstructed markers in CBCT volumes suffer from artifacts and have distorted shapes, which sets an obstacle fornavigation. In this work, we propose two fiducial marker detection methods: direct detection from distorted markers (direct method) and detection after marker recovery (recovery method). For direct detection from distorted markers in reconstructed volumes, an efficient automatic marker detection method using two neural networks and a conventional circle detection algorithm is proposed. For marker recovery, a task-specific data preparation strategy is proposed to recover markers from severely truncated data. Afterwards, a conventional marker detection algorithm is applied for position detection. The networks in both methods are trained based on simulated data. For the direct method, 6800 images and 10000 images are generated, respectively, to train the U-Net and ResNet50. For the recovery method, the training set includes 1360 images for FBPConvNet and Pix2pixGAN. The simulated data set with 166 markers and four cadaver cases with real fiducials are used forevaluation. The two methods are evaluated on simulated data and real cadaver data. The direct method achieves 100% detection rates within 1mm detection error on simulated data with normal truncation and simulated data with heavier noise, but only detect 94.6% markers in extremely severe truncation case. The recovery method detects all the markers successfully in three test data sets and around 95% markers are detected within 0.5mm error. For real cadaver data, both methods achieve 100% marker detection rates with mean registration error below 0.2mm. Our experiments demonstrate that the direct method is capable of detecting distorted markers accurately and the recovery method with the task-specific data preparation strategy has high robustness and generalizability on various data sets. The task-specific data preparation is able to reconstruct structures of interest outside the FOV from severely truncated data better than conventional datapreparation.

A feasibility study on the development and use of a deep learning model to automate real-time monitoring of tumor position and assessment of interfraction fiducial marker migration in prostate radiotherapy patients * *Deep Learning Fiducial Marker Localisation.

Purpose. The aim of this study was to assess the feasibility of the development and training of a deep learning object detection model for automating the assessment of fiducial marker migration and tracking of the prostate in radiotherapy patients. Methods and Materials. A fiducial marker detection model was trained on the YOLO v2 detection framework using approximately 20,000 pelvis kV projection images with fiducial markers labelled. The ability of the trained model to detect marker positions was validated by tracking the motion of markers in a respiratory phantom and comparing detection data with the expected displacement from a reference position. Marker migration was then assessed in 14 prostate radiotherapy patients using the detector for comparison with previously conducted studies. This was done by determining variations in intermarker distance between the first and subsequent fractions in each patient. Results. On completion of training, a detection model was developed that operated at a 96% detection efficacy and with a root mean square error of 0.3 pixels. By determining the displacement from a reference position in a respiratory phantom, experimentally and with the detector it was found that the detector was able to compute displacements with a mean accuracy of 97.8% when compared to the actual values. Interfraction marker migration was measured in 14 patients and the average and maximum standard deviation marker migration were found to be and respectively. Conclusion. This study demonstrates the benefits of pairing deep learning object detection, and image-guided radiotherapy and how a workflow to automate the assessment of organ motion and seed migration during prostate radiotherapy can be developed. The high detection efficacy and low error make evident the advantages of using a pre-trained model to automate the assessment of the target volume positional variation and the migration of fiducial markers between fractions.

Fiducial visibility on planar images during motion-synchronized tomotherapy treatments

Objective. Synchrony® is a motion management system on the Radixact® that uses planar kV radiographs to locate the target during treatment. The purpose of this work is to quantify the visibility of fiducials on these radiographs. Approach. A custom acrylic slab was machined to hold 8 gold fiducials of various lengths, diameters, and orientations with respect to the imaging axis. The slab was placed on the couch at the imaging isocenter and planar radiographs were acquired perpendicular to the custom slab with varying thicknesses of acrylic on each side. Fiducial signal to noise ratio (SNR) and detected fiducial position error in millimeters were quantified. Main Results. The minimum output protocol (100 kVp, 0.8 mAs) was sufficient to detect all fiducials on both Radixact configurations when the thickness of the phantom was 20 cm. However, no fiducials for any protocol were detected when the phantom was 50 cm thick. The algorithm accurately detected fiducials on the image when the SNR was larger than 4. The MV beam was observed to cause RFI artifacts on the kV images and to decrease SNR by an average of 10%. Significance. This work provides the first data on fiducial visibility on kV radiographs from Radixact Synchrony treatments. The Synchrony fiducial detection algorithm was determined to be very accurate when sufficient SNR is achieved. However, a higher output protocol may need to be added for use with larger patients. This work provided groundwork for investigating visibility of fiducial-free solid targets in future studies and provided a direct comparison of fiducial visibility on the two Radixact configurations, which will allow for intercomparison of results between configurations.

Hybrid 3D T1-weighted gradient-echo sequence for fiducial marker detection and tumor delineation via magnetic resonance imaging in liver stereotactic body radiation therapy

PurposeGold fiducial markers are used to guide liver stereotactic body radiation therapy (SBRT) and are hard to detect by magnetic resonance imaging (MRI). In this study, the parameters of the three-dimensional T1-weighted turbo gradient-echo (3D T1W-GRE) sequence were optimized for gold marker detection without degrading tumor delineation. MethodsCustom-made phantoms mimicking tumor and normal liver parenchyma were prepared and embedded with a gold marker. The 3D T1W-GRE was scanned by varying echo time (TE), bandwidth (BW), flip angle (FA), and base matrix size. The signal-to-noise ratio (SNR), contrast ratio (CR), and relative standard deviation (RSD) of the signal intensity in the area including the gold marker were evaluated, and the parameters were optimized accordingly. The modified 3D T1W-GRE (called HYBRID) was compared with the conventional T1W-GRE- and T2*-sequences in both phantom and clinical studies. In the clinical study of six patients with primary liver tumors, two observers visually assessed marker detection, tumor delineation, and overall image quality on a four-point scale. ResultsIn the phantom study, HYBRID showed significantly higher SNR and RSD than those of conventional T1W-GRE (P < 0.001). In the clinical study, HYBRID yielded significantly higher scores than conventional T1W-GRE did in terms of marker detection (P < 0.001). The scores of both sequences were not statistically different in terms of tumor delineation and overall image quality (P = 0.56 and P = 0.32). ConclusionsThe proposed HYBRID sequence improved gold fiducial marker detection without degrading tumor delineation in MRI for SBRT of primary liver tumor.

Commissioning and routine quality assurance of the Radixact Synchrony system

The Radixact Synchrony system allows for target motion correction when tracking either fiducials in/around the target or a dense lesion in the lung. As such evaluation testing and quality assurance (QA) tests are required. To allow for QA procedures to be performed with a range of available phantoms evaluation of the dosimetric delivery accuracy was performed for a range of motions, phantoms, and motion platforms. A Computerized Imaging Reference Systems, Incorporated (CIRS) 1D motion platform and Accuray Tomotherapy "Cheese" phantom was utilized to perform absolute dose and GafChromic EBT3 film measurements. A HexaMotion platform and Delta4 phantom were utilized to quantify the effects of 1D and 3D motions. Inter-device comparison was performed with the ArcCHECK and Delta4 phantoms and GafChromic EBT3 film, five patient plans were delivered to each phantom when static and with two different motion types both with and without Synchrony motion correction. A range of QA tests are described. A phantom was designed to allow for daily verification of system functionality. This test allows for the detection of either fiducials or a dense silicone target with a stationary phantom. Monthly testing procedures are described that allow the user to verify the dosimetric improvement when utilizing synchrony delivery motion compensation versus uncorrected motions. These can be performed utilizing a 1D motion stage with an ion-chamber and GafChromic EBT3 film to allow for a 2D dosimetric validation. Alternatively, a 3D motion platform can be utilized where available. Monthly and annual imaging tests are described. Finally, annual test procedures designed to verify the coincidence of the imaging system and treatment isocenter are described. The evaluation of the Synchrony system using a range of QA devices shows consistently high dosimetric accuracy with similar trends in passing criteria found with GafChromic EBT3 film, ArcCHECK, and Delta4 phantoms for density-based respiratory model compensation. These results highlight the large improvements in the dose distribution when motion is accounted for with the Synchrony system as measured with a range of phantoms and motion platforms that the majority of users will have available. The testing methods and QA procedures described provide guidance for new users of the Radixact Synchrony system as they implement their own QA programs for this system, until such time as an AAPM task group report is made available. QA procedures including Kilovolts (kV) imaging quality metrics and imaging dose parameters, dose deposition accuracy, target detection coincidence, and target position detection accuracy are described.

Visual Navigation System for Autonomous Drone using Fiducial Marker Detection

Drones have been quickly developing for civilian applications in recent years. Because of the nonlinearity of the mathematical drone model, and the importance of precise navigation to avoid possible dangers, it is necessary to establish an algorithm to localize the drone simultaneously and maneuver it to the desired destination. This paper presents a visual-based multi-stage error tolerance navigation algorithm of an autonomous drone by a tag-based fiducial marker detection in finding its target. Dynamic and kinematic models of the drone were developed by Newton-Euler. The position and orientation of the drone, related to the tag, are determined by AprilTag, which is used as feedback in a closed-loop control system with an Adjustable Proportional-Integral-Derivative (APID) controller. Parameters of the controller are tuned based on steady-State error, which is defined as the distance of the drone from the desired point. The sequence of path trajectory, that drone follows to reach the desired point, is defined as a navigation algorithm. A model of the drone was simulated in a virtual outdoor to mimic hovering in complex obstacles environment. The results present satisfactory performance of the navigation system programmed by the APID controller in comparison with the conventional Proportional-Integral-Derivative (PID) controller. It can be ascertained that the proposed navigation system based on a tag marker in the closed-loop control system is applicable to maneuvering the drone autonomously and useful for various industrial tasks in indoor/outdoor environments.

Reducing the Impact of External Vibrations on Fiducial Point Detection in Seismocardiogram Signals.

Wearable systems that enable continuous non-invasive monitoring of hemodynamic parameters can aid in cardiac health evaluation in non-hospital settings. The seismocardiogram (SCG) is a non-invasively acquired cardiovascular biosignal for which timings of fiducial points, like aortic valve opening (AO) and aortic valve closing (AC), can enable estimation of key hemodynamic parameters. However, SCG is susceptible to motion artifacts, making accurate estimation of these points difficult when corrupted by high-g or in-band vibration artifacts. In this paper, a novel denoising pipeline is proposed that removes vehicle-vibration artifacts from corrupted SCG beats for accurate fiducial point detection. The noisy SCG signal is decomposed with ensemble empirical mode decomposition (EEMD). Corrupted segments of the decomposed signal are then identified and removed using the quasi-periodicity of the SCG. Signal quality assessment of the reconstructed SCG beats then removes unreliable beats before feature extraction. The overall approach is validated on simulated vehicle-corrupted SCG generated by adding real subway collected vibration signals onto clean SCG. SNR increased by 8.1dB in the AO complex and 11.5dB in the AC complex of the SCG signal. Hemodynamic timing estimation errors reduced by 16.5% for pre-ejection period (PEP), 67.2% for left ventricular ejection time (LVET), and 57.7% for PEP/LVET-a feature previously determined in prior work to be of great importance for assessing blood volume status during hemorrhage. These findings suggest that usable SCG signals can be recovered from vehicle-corrupted SCG signals using the presented denoising framework, allowing for accurate hemodynamic timing estimation. Reliable hemodynamic estimates from vehicle-corrupted SCG signals will enable the adoption of the SCG in outside-of-hospital settings.

DeepTag: A General Framework for Fiducial Marker Design and Detection.

A fiducial marker system usually consists of markers, a detection algorithm, and a coding system. The appearance of markers and the detection robustness are generally limited by the existing detection algorithms, which are hand-crafted with traditional low-level image processing techniques. Furthermore, a sophisticatedly designed coding system is required to overcome the shortcomings of both markers and detection algorithms. To improve the flexibility and robustness in various applications, we propose a general deep learning based framework, DeepTag, for fiducial marker design and detection. DeepTag not only supports detection of a wide variety of existing marker families, but also makes it possible to design new marker families with customized local patterns. Moreover, we propose an effective procedure to synthesize training data on the fly without manual annotations. Thus, DeepTag can easily adapt to existing and newly-designed marker families. To validate DeepTag and existing methods, beside existing datasets, we further collect a new large and challenging dataset where markers are placed in different view distances and angles. Experiments show that DeepTag well supports different marker families and greatly outperforms the existing methods in terms of both detection robustness and pose accuracy. Both code and dataset are available at https://herohuyongtao.github.io/research/publications/deep-tag/.