Types Of Artifacts Research Articles

Development of an algorithm for detecting slow peak-wave activity in non-convulsive forms of epilepsy

The purpose of this study is to develop a classifier capable of detecting typical absence seizures in real-time using electroencephalogram (EEG) data and a Support Vector Machine (SVM) model. Methods. Sections of the EEG, previously identified by a specialist as containing typical absences, were used to train the SVM model. Key features for classification include the number of zero crossings, cross-correlation between two consecutive windows, spectral power across various frequency bands, and the standard deviation of instantaneous signal power. Results. Training and testing datasets were established, consisting of EEG windows with various types of artifacts. The SVM model was successfully trained and tested, achieving high performance metrics. The developed algorithm can be integrated into a mobile application and used in conjunction with a wearable EEG device with dry electrodes for real-time detection of typical absences. Conclusion. The study results affirm the potential for using machine learning techniques for the automatic detection and logging of epileptic activity. However, additional testing on a larger dataset is needed for more conclusive results, including data acquired through a wireless EEG device using dry electrodes. Future work will involve selecting a suitable EEG device and developing a mobile application for real-time data collection and analysis.

Peculiarities of the dynamics of a viscous liquid with a free boundary under periodic influences

The purpose of this study is to develop a classifier capable of detecting typical absence seizures in real-time using electroencephalogram (EEG) data and a Support Vector Machine (SVM) model. Methods. Sections of the EEG, previously identified by a specialist as containing typical absences, were used to train the SVM model. Key features for classification include the number of zero crossings, cross-correlation between two consecutive windows, spectral power across various frequency bands, and the standard deviation of instantaneous signal power. Results. Training and testing datasets were established, consisting of EEG windows with various types of artifacts. The SVM model was successfully trained and tested, achieving high performance metrics. The developed algorithm can be integrated into a mobile application and used in conjunction with a wearable EEG device with dry electrodes for real-time detection of typical absences. Conclusion. The study results affirm the potential for using machine learning techniques for the automatic detection and logging of epileptic activity. However, additional testing on a larger dataset is needed for more conclusive results, including data acquired through a wireless EEG device using dry electrodes. Future work will involve selecting a suitable EEG device and developing a mobile application for real-time data collection and analysis.

The cellular Potts model on disordered lattices.

The cellular Potts model, also known as the Glazier-Graner-Hogeweg model, is a lattice-based approach by which biological tissues at the level of individual cells can be numerically studied. Traditionally, a square or hexagonal underlying lattice structure is assumed for two-dimensional systems, and this is known to introduce artifacts in the structure and dynamics of the model tissues. That is, on regular lattices, cells can assume shapes that are dictated by the symmetries of the underlying lattice. Here, we developed a variant of this method that can be applied to a broad class of (ir)regular lattices. We show that on an irregular lattice deriving from a fluid-like configuration, two types of artifacts can be removed. We further report on the transition between a fluid-like disordered and a solid-like hexagonally ordered phase present for monodisperse confluent cells as a function of their surface tension. This transition shows the hallmarks of a first-order phase transition and is different from the glass/jamming transitions commonly reported for the vertex and active Voronoi models. We emphasize this by analyzing the distribution of shape parameters found in our state space. Our analysis provides a useful reference for the future study of epithelia using the (ir)regular cellular Potts model.

Utility of spectral CT with orthopedic metal artifact reduction algorithms for 125I seeds implantation in mediastinal and hepatic tumors.

Virtual monochromatic image (VMI) combined with orthopedic metal artifact reduction algorithms (VMI + O-MAR) can effectively reduce artifacts caused by metal implants of different types. Nevertheless, so far, no study has systematically evaluated the efficacy of VMI + O-MAR in reducing various types of metal artifacts induced by 125I seeds. The aim of this study was to assess the effectiveness of combining spectral computed tomography (CT) images with O-MAR in reducing metal artifacts and improving the image quality affected by artifacts in patients after 125I radioactive seeds implantation (RSI). A total of 45 patients who underwent dual-layer detector spectral CT (DLCT; IQon, Philips Healthcare) scanning of mediastinal and hepatic tumors after 125I RSI were retrospectively included. Spectral data were reconstructed into conventional image (CI), VMI, CI combined with O-MAR (CI + O-MAR), and VMI + O-MAR to evaluate the de-artifact effect and image quality improvement. Objective indicators included signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and artifact index (AI) of lesions affected by artifacts. Subjective indicators included assessment of overcorrected artifacts and new artifacts, different morphology of artifacts, and overall image quality. In artifact-affected lesion areas, SNR and CNR in the CI/VMI + O-MAR groups were better than those in CI groups (all P values <0.05). The AI showed a downward trend as VMI keV increased (all P values <0.001). The AI values of the CI/VMI (50-150 keV) group were all higher than the groups of CI/VMI + O-MAR (50-150 keV) (P<0.001). Overcorrection artifacts and new artifacts were concentrated in the VMI50/70 keV groups. In the evaluation of artifact morphology, as the VMI keV increased, the number of near-field banding artifacts in hyperdense artifacts gradually decreased, whereas the number of minimal or no artifacts increased, and the total number of hyperdense artifacts were decreased. The diagnostic and image quality scores of hyperdense artifacts were higher than those of hypodense artifacts as VMI keV increased. High VMI level combined with O-MAR substantially improve objective and subjective image quality, lesion display ability, and diagnostic confidence of CT follow-up after 125I RSI, especially at the VMI + O-MAR 150 keV level.

Channel-Specific and Spatial Residual Attention Network for Medical Image Denoising.

Medical image quality is crucial for physicians to ensure accurate diagnosis and therapeutic strategies. However, due to the interference of noise, there are often various types of noise and artifacts in medical images. This not only damages the visual clarity of images, but also reduces the accuracy of information extraction. Considering that the edges of medical images are rich in high-frequency information, to enhance the quality of medical images, a dual attention mechanism, the channel-specific and spatial residual attention network (CSRAN) in the U-Net framework is proposed. The CSRAN seamlessly integrates the U-Net architecture with channel-wise and spatial feature attention (CSAR) modules, as well as low-frequency channel attention modules. Combined with the two modules, the ability of medical image processing to extract high-frequency features is improved, thereby significantly improving the edge effects and clarity of reconstructed images. This model can present better performance in capturing high-frequency information and spatial structures in medical image denoising and super-resolution reconstruction tasks. It cannot only enhance the ability to extract high-frequency features and strengthen its nonlinear representation capability, but also endow strong edge detection capabilities of the model. The experimental results further prove the superiority of CSRAN in medical image denoising and super-resolution reconstruction tasks.

Customizable automated cleaning of multichannel sleep EEG in SleepTrip.

While standard polysomnography has revealed the importance of the sleeping brain in health and disease, more specific insight into the relevant brain circuits requires high-density electroencephalography (EEG). However, identifying and handling sleep EEG artifacts becomes increasingly challenging with higher channel counts and/or volume of recordings. Whereas manual cleaning is time-consuming, subjective, and often yields data loss (e.g., complete removal of channels or epochs), automated approaches suitable and practical for overnight sleep EEG remain limited, especially when control over detection and repair behavior is desired. Here, we introduce a flexible approach for automated cleaning of multichannel sleep recordings, as part of the free Matlab-based toolbox SleepTrip. Key functionality includes 1) channel-wise detection of various artifact types encountered in sleep EEG, 2) channel- and time-resolved marking of data segments for repair through interpolation, and 3) visualization options to review and monitor performance. Functionality for Independent Component Analysis is also included. Extensive customization options allow tailoring cleaning behavior to data properties and analysis goals. By enabling computationally efficient and flexible automated data cleaning, this tool helps to facilitate fundamental and clinical sleep EEG research.

Enigmatic Perforated Stone Disk and Grooved Stones from Three Caves Sites in Sumatra

Ground stone tools, especially perforated stone disks and grooved stones, are rarely discussed in Indonesian prehistory. In terms of its quantity and workmanship, these types of artifacts are fairly unique. They are often limited in quantity, which makes it difficult to compare with the other references. Moreover, their technological aspect still needs to be widely understood by academics. For the first time in Indonesia, this article will discuss perforated stone disks and grooved stones in the context of prehistoric cave sites in Sumatra. Through formal analysis of its attributes at medium to high magnification, we provide a reference for their technological aspects as well as the context of the artifact. Our study reveals that both types of artifacts appear to be closely related to the aquatic culture. The practical function of these artifacts is associated with a fishery tradition on the rivers inland. Our argument is corroborated by the remnants of aquatic fauna associated with these two types of artifacts. Apart from that, the perforated stone disks that were previously reported were found in the mainland of Southeast Asia and, in fact, also found in Sumatra. It further emphasizes the connection between the cultural entities of prehistoric populations that inhabited insular and mainland Southeast Asia.

Drilling Tools in Prehistoric China from the Perspective of Experimental Archaeology

As a type of artifact commonly found in Chinese prehistoric sites, perforated shellfish materials can be classified into freshwater mussel materials and perforated seashell materials, and drilling tools can be classified into single and composite drilling tools. The experimental reconstruction of the two types of drilling tools and drilling methods explored the influence of the composition of drilling tools, drilling methods, and other factors on the drilling process. Meanwhile, the experiments were recorded and analyzed using a stereo microscope. The experimental results show that the composite drilling tools provide better drilling results and more dynamics than stone drill bits. The perforated weight stone stabilized the center of gravity and safeguarded the dynamic performance. The type of drilling tool, drilling method, and characteristics such as smoothness and hardness of perforated shellfish materials significantly impact the drilling effect.

Recent Study of Artifact Elimination in EEG Signals

The progress of electroencephalography (EEG) has promoted this technology to be widely used in various fields such as computer science, medical engineering, and signal processing because of its non-invasiveness and low cost. However, the quality of EEG recordings may be degraded due to the introduction of artifacts, which has a non-negligible negative impact on subsequent operations. Artifacts are unwanted signals that vary in their source, such as muscular, ocular, and cardiac ones. Many studies have proposed methods to remove artifacts from EEG signals, from the classic ones including filtering, empirical mode decomposition, and blind source separation to the recent frequent introduction of neural networks. This work reviews the highly influential publications in the past 5 years to update the latest headways. Based on the survey of essays and papers, we notice hybrid methods with the greatest proportion. Besides, not only do we discover the development and advancement of the typical methods of signal processing mentioned, but we also find that machine learning techniques are becoming more prevailing and effective for artifact removal. Moreover, we observe that recent works have proposed integrated architectures and blockchain solutions to address the problem globally, regardless of the type of artifact.

Identifying accurate artefact morphological ranges using optimal linear estimation: Method validation, case studies, and code

A fundamental goal of archaeologists is to infer the behaviour of past humans from the attributes of the artefacts they left behind. The archaeological record is, however, fragmented and often provides a partial record of the total artefacts produced by a given population. In turn, there is potential for population-level morphometric data, and therefore behavioural inferences, to be biased relative to the trends realised in the past. This includes morphological range data which are important for identifying similarities and differences between artefact groups, and for contextualising artefacts relative to external variables such as human anatomy, ecology, climate and chronology. Here, we investigate whether optimal linear estimation (OLE) modelling can be used to accurately identify the upper and lower limits of artefact morphological ranges (including those represented by sparse datasets). First, we test whether OLE reliably identifies morphological ranges using randomly sampled subsets of ‘known and complete’ replica artefact assemblages. Using morphometric data from lithic, ceramic, and metal archaeological case studies, we then identify how much further the upper and lower form limits of these artefact types would have been in the past, relative to the ranges evidenced by excavated (i.e., known partial) records. Validation tests demonstrate OLE to be capable of providing broadly accurate estimates for the true morphological range of artefact assemblages. Estimate accuracy increases relative to the percentage of the total assemblage used and the method is shown to function well using as few as five records (k) from an assemblage. The case studies reveal how OLE can overhaul or reinforce our understanding of artefact morphological ranges. In some instances, it is clear that the archaeological record provides a highly accurate representation of artefact morphological ranges and the overlap between artefact groups. For others, it is demonstrated that our understanding of the extreme artefact forms produced by past people is likely inaccurate

Technology from hunting blinds: A study of lithic assemblages in Late Holocene stone structures from Patagonia (Argentina)

Stone structures called parapetos are a highlighted feature of the archaeological record in central-western Santa Cruz, Patagonia. More than 500 have been registered so far with varied sizes and shapes and clustered in different amounts. They are located in basaltic plateaus, over 700 masl. These hunter-gatherer structures are believed to be mainly related to hunting activities though some sites have revealed a more general function. Radiocarbon dates reveal that they are a typical Late Holocene technology.&#x0D; This work discusses the characteristics of lithic materials recovered in these particular sites with a specific spatial and temporal distribution in Patagonia. We evaluate the existence of variability in lithic artefacts related to stone structures recovered in different areas: Pampa del Asador (Cerro Pampa), Guitarra Plateau, Asador Plateau and the Strobel Plateau. Diverse types of hunting blind sites have been included: isolated structures and grouped ones located in different topographic and ecological contexts. Materials were recovered from inner and outer surfaces of structures as well as from stratigraphic contexts. The sample includes tools, debitage and cores; more than 10000 lithic artefacts are included.&#x0D; Results show that there are similarities in the characteristics of artefacts discarded in parapetos from different areas. However, variability in the type and characteristics of artefacts in each assemblage could account for a wider array of activities being carried out in parapetos from the northern sector of the region under study. Thus, strategies for occupying highlands during the Late Holocene differed.&#x0D; The evidence discussed summarises more than 20 years of on-going investigations in the region. It provides exceptional information based on systematic analysis of a significant sample of lithic artefacts in hunting blinds.

Are you sure it’s an artifact? Artifact detection and uncertainty quantification in histological images

Modern cancer diagnostics involves extracting tissue specimens from suspicious areas and conducting histotechnical procedures to prepare a digitized glass slide, called Whole Slide Image (WSI), for further examination. These procedures frequently introduce different types of artifacts in the obtained WSI, and histological artifacts might influence Computational Pathology (CPATH) systems further down to a diagnostic pipeline if not excluded or handled. Deep Convolutional Neural Networks (DCNNs) have achieved promising results for the detection of some WSI artifacts, however, they do not incorporate uncertainty in their predictions. This paper proposes an uncertainty-aware Deep Kernel Learning (DKL) model to detect blurry areas and folded tissues, two types of artifacts that can appear in WSIs. The proposed probabilistic model combines a CNN feature extractor and a sparse Gaussian Processes (GPs) classifier, which improves the performance of current state-of-the-art artifact detection DCNNs and provides uncertainty estimates. We achieved 0.996 and 0.938 F1 scores for blur and folded tissue detection on unseen data, respectively. In extensive experiments, we validated the DKL model on unseen data from external independent cohorts with different staining and tissue types, where it outperformed DCNNs. Interestingly, the DKL model is more confident in the correct predictions and less in the wrong ones. The proposed DKL model can be integrated into the preprocessing pipeline of CPATH systems to provide reliable predictions and possibly serve as a quality control tool.

Imaging Artifacts and Quality Evaluation with Ultrawide-Field Swept-Source OCTA in Diabetic Retinopathy

Purpose To evaluate the prevalence and types of artifacts in ultrawide-field swept-source optical coherence tomography angiography (SS-OCTA) scans of diabetic retinopathy (DR) patients. Methods This study was a prospective, observational study conducted from May 2022 to October 2022. Participants comprised individuals with proliferative diabetic retinopathy (PDR), nonproliferative diabetic retinopathy (NPDR), no diabetic retinopathy, and healthy controls. SS-OCTA imaging was performed, and a 5-scan composite with a larger field of view (23.5 mm × 17.5 mm) was captured using built-in software. Two experienced ophthalmologists analyzed the images independently, and the image quality and artifact prevalence were recorded and analyzed. Results The study included 70 eyes (16 with PDR, 24 with NPDR, 12 eyes of diabetic patients without DR, and 18 healthy eyes) in 70 subjects. Imaging artifacts were observed in a high percentage of eyes, with 98.57% of eyes presenting at least one type of artifact. A significant proportion of eyes (58.57%) exhibited a severe degree of artifacts. The most prevalent artifacts were loss of signal in 63 eyes (90%) and displacement artifact and masking artifact in 43 eyes (61.4%). Patients with more severe stages of DR had higher artifact scores (p < 0.05). Multivariate regression analysis indicated that DR severity was the most important factor influencing artifact scores (p < 0.05). Conclusions In OCTA photos, various artifacts arise at different frequencies. It is crucial to qualitatively evaluate the images to ensure their quality. The results demonstrate that DR severity has a significant correlation with artifact scores.

On crescent-shaped objects of the early Bronze Age in southern Siberia and the surrounding areas

Abstract Crescent-shaped objects are a special type of artifact commonly found among early Bronze Age archaeological cultures in southern Siberia and the surrounding areas. Based on typology, burial context, and accompanying assemblages, in addition to related iconographic evidence, this study shows that these crescent-shaped objects, in the form of single or double birds, were religious representations once suspended from sacred attire. Crescent-shaped objects in southern Siberia and the surrounding areas might have originated in the Baikal region during the first half of the third millennium BCE, and likely began to influence regions of northern China toward the end of the third or the beginning of the second millennium BCE.

Efficiency of Portable X-ray Fluorescence for Distinguishing Lead Slag.

In the Minier Valley (France, Aveyron), an archaeological site excavation has led to the discovery of 25 kg of lead (Pb) slag, found in a secondary position. On a macroscopic level, these slags can be classified into two distinct categories, vitreous black and porous gray. This clear visual differentiation has been previously documented at other Pb-silver (Ag) metallurgy sites dating from the 12th century to the 14th century in Europe, and more particularly in the workshops of Mont Lozère, located approximately 80 km away from Le Minier. However, to date, scant evidence exists regarding the specific metallurgical process that produced this slag. This study presents the results of the analysis of 218 Pb slags (Supplemental Material) using portable X-ray fluorescence (pXRF). This method does not appear to be ideally suited for investigating this type of artifact, as it does not provide quantification of oxygen content. Nevertheless, it can be used to quickly identify trends and determine whether the two identified facies exhibit dissimilar chemical compositions. The results indicate that, based on the elemental compositions of the slags, no discernible distinction can be made to categorize them into two separate groups, both in bivariate and multivariate analyses. Consequently, it can be inferred that the gray and black slags originate from a single process executed within the same type of furnace. Scanning electron microscopy energy dispersive spectrometry analyses on selected samples demonstrate that variations in metal content are considerably exaggerated by pXRF, but that the trends observed are confirmed. The use of pXRF is therefore a relevant tool for determining whether or not Pb-Ag slag is the result of the same process. However, this initial approach needs to be refined by other analyses to confirm whether the smelted ores are of local origin and to be able to better define the details of the metallurgical process used.

Single-pulse electrical stimulation artifact removal using the novel matching pursuit-based artifact reconstruction and removal method (MPARRM)

Objective. Single-pulse electrical stimulation (SPES) has been widely used to probe effective connectivity. However, analysis of the neural response is often confounded by stimulation artifacts. We developed a novel matching pursuit-based artifact reconstruction and removal method (MPARRM) capable of removing artifacts from stimulation-artifact-affected electrophysiological signals. Approach. To validate MPARRM across a wide range of potential stimulation artifact types, we performed a bench-top experiment in which we suspended electrodes in a saline solution to generate 110 types of real-world stimulation artifacts. We then added the generated stimulation artifacts to ground truth signals (stereoelectroencephalography signals from nine human subjects recorded during a receptive speech task), applied MPARRM to the combined signal, and compared the resultant denoised signal with the ground truth signal. We further applied MPARRM to artifact-affected neural signals recorded from the hippocampus while performing SPES on the ipsilateral basolateral amygdala in nine human subjects. Main results. MPARRM could remove stimulation artifacts without introducing spectral leakage or temporal spread. It accommodated variable stimulation parameters and recovered the early response to SPES within a wide range of frequency bands. Specifically, in the early response period (5–10 ms following stimulation onset), we found that the broadband gamma power (70–170 Hz) of the denoised signal was highly correlated with the ground truth signal ( R=0.98±0.02 , Pearson), and the broadband gamma activity of the denoised signal faithfully revealed the responses to the auditory stimuli within the ground truth signal with 94%±1.47% sensitivity and 99%±1.01% specificity. We further found that MPARRM could reveal the expected temporal progression of broadband gamma activity along the anterior-posterior axis of the hippocampus in response to the ipsilateral amygdala stimulation. Significance. MPARRM could faithfully remove SPES artifacts without confounding the electrophysiological signal components, especially during the early-response period. This method can facilitate the understanding of the neural response mechanisms of SPES.

Evaluating Sonographers' Awareness of Artifacts in Renal Ultrasound Scans.

A routine ultrasound scan is used to assess a variety of renal pathological cases. Sonographers face a variety of challenges that may affect their interpretation. Understanding normal organ shapes, human anatomy, physical concepts, and artifacts is required for accurate diagnosis. Sonographers must understand how artifacts appear in ultrasound images in order to reduce errors and improve diagnosis. The purpose of this study is to assess sonographers' awareness and knowledge of artifacts in renal ultrasound scans. Participants in this cross-sectional study were asked to complete a survey containing different types of common artifacts in renal system ultrasound scans. An online questionnaire survey was used to collect the data. The questionnaire targeted radiologists, radiologic technologists, and intern students in Madinah hospitals in the ultrasound department. The total number of participants was 99, with 9.1% being radiologists, 31.3% being radiology technologists, 6.1% being senior specialists, and 53.5% being intern students. There was a significant difference in the participants' knowledge of ultrasound artifacts in the renal system with the total selection of the right artifact in senior specialists at 73% and intern students 45%. There was a direct relationship between age and years of experience in distinguishing artifacts in the renal system scan. A category of participants with the highest age and experience got 92% of the correct selection of artifacts. The study concluded that intern students and radiology technologists have limited knowledge of ultrasound scan artifacts, whereas senior specialists and radiologists have a high level of awareness of the artifacts.

Image Quality Enhancement via Machine Learning: A Unified Approach to Super-Resolution, Denoising, and Low-Density Enhancement

Problem: The escalating demand for high-quality images across various applications has underscored the necessity for advanced image enhancement techniques. Traditionally, denoising, super-resolution, and low-density enhancement, the three key image enhancement techniques, have been approached independently, resulting in separate developments for each. Unfortunately, a unified framework that seamlessly combines all three techniques and surpasses individual method performance has been lacking. Proposed Idea: The objective of this research is to develop a unified image enhancement framework that not only unites these techniques but also substantiates its superiority over existing individual methods through an extensive series of experiments. The proposed method utilizes cascade autoencoder architectures to generate high-quality enhanced images. In addition, an auxiliary artifact type prediction module has been introduced to enhance the noise-awareness, resulting in improved accuracy. Result: The proposed method demonstrates superior performance in achieving state-of-the-art accuracy when evaluated against three image quality metrics on various public datasets. Additionally, the practical application of the proposed method showcases its efficacy in effectively solving real-world problems.

Conditional generative adversarial networks for stripe artefact removal in high-resolution X-ray tomography

Tomographic imaging supports a great number of medical and material science applications. The collected projection data usually has different types of imaging artefacts and noise. Various image pre-processing and reconstruction methods are used to obtain volumetric datasets of high quality for further analysis. In order to minimise reconstruction artefacts, one can apply either filtering and/or data completion/inpainting techniques which can recover the data. Deep learning (DL) methods to remove artefacts and noise have been successfully applied in the past. In this paper, we present a novel approach based on conditional generative adversarial networks (cGANs) to remove stripe artefacts. The novelty of the presented technique is in how the training data for DL is extracted from the same tomographic dataset that needs recovery. We also provide new deterministic stripe detection and inpainting algorithms to support the development. The presented methods are compared with other stripe removal algorithms and applied to 3D and 4D high-resolution X-ray data collected at Diamond Light Source synchrotron, UK. The proposed DL method delivers reconstructed images with minimised ring artefacts while being a parameter-free approach. A similar DL strategy can also be applied to remove other types of artefacts in images.

Characterization and decontamination of background noise in droplet-based single-cell protein expression data with DecontPro.

Assays such as CITE-seq can measure the abundance of cell surface proteins on individual cells using antibody derived tags (ADTs). However, many ADTs have high levels of background noise that can obfuscate down-stream analyses. In an exploratory analysis of PBMC datasets, we find that some droplets that were originally called 'empty' due to low levels of RNA contained high levels of ADTs and likely corresponded to neutrophils. We identified a novel type of artifact in the empty droplets called a 'spongelet' which has medium levels of ADT expression and is distinct from ambient noise. ADT expression levels in the spongelets correlate to ADT expression levels in the background peak of true cells in several datasets suggesting that they can contribute to background noise along with ambient ADTs. We then developed DecontPro, a novel Bayesian hierarchical model that can decontaminate ADT data by estimating and removing contamination from these sources. DecontPro outperforms other decontamination tools in removing aberrantly expressed ADTs while retaining native ADTs and in improving clustering specificity. Overall, these results suggest that identification of empty drops should be performed separately for RNA and ADT data and that DecontPro can be incorporated into CITE-seq workflows to improve the quality of downstream analyses.