Local Dataset Research Articles

UAV Geo-Localization Dataset and Method Based on Cross-View Matching

The stable flight of drones relies on Global Navigation Satellite Systems (GNSS). However, in complex environments, GNSS signals are prone to interference, leading to flight instability. Inspired by cross-view machine learning, this paper introduces the VDUAV dataset and designs the VRLM network architecture, opening new avenues for cross-view geolocation. First, to address the limitations of traditional datasets with limited scenarios, we propose the VDUAV dataset. By leveraging the virtual–real mapping of latitude and longitude coordinates, we establish a digital twin platform that incorporates 3D models of real-world environments. This platform facilitates the creation of the VDUAV dataset for cross-view drone localization, significantly reducing the cost of dataset production. Second, we introduce a new baseline model for cross-view matching, the Virtual Reality Localization Method (VRLM). The model uses FocalNet as its backbone and extracts multi-scale features from both drone and satellite images through two separate branches. These features are then fused using a Similarity Computation and Feature Fusion (SCFF) module. By applying a weighted fusion of multi-scale features, the model preserves critical distinguishing features in the images, leading to substantial improvements in both processing speed and localization accuracy. Experimental results demonstrate that the VRLM model outperforms FPI on the VDUAV dataset, achieving an accuracy increase to 83.35% on the MA@20 metric and a precision of 74.13% on the RDS metric.

VHL missense mutation delineate aggressive clear cell renal cell carcinoma subtype with favorable immunotherapeutic response

Backgroundvon Hippel-Lindau (VHL) harbors the highest mutational frequency in clear cell renal cell carcinoma (ccRCC). Although VHL mutational subtypes exert diverse impacts on the functionality of the VHL protein, the...

Multi-source information fused loose particle localization and material identification method for sealed electronic equipment

Sealed electronic equipment are1 an important component of aerospace defense systems, and loose particles pose a significant threat to their reliable operation. Loose particle detection is crucial. For sealed electronic equipment with large scale and complex structure, loose particle detection should not only include the judgment of existence, but also obtain location and material information to facilitate the cleaning and control work. In this paper, the authors proposed a multi-source information fused loose particle localization and material identification method. Firstly, the equipment model was designed, the loose particle samples were made, and loose particle signals were collected. Secondly, the two-stage adaptive energy threshold pulse extraction algorithm was newly proposed to extract effective pulses, and the threshold-judgement-search pulse matching algorithm was improved to match the effective pulse groups. Next, spectrograms were transformed from effective pulses to create the localization and material image set. The time-domain, frequency-domain and gray-level co-occurrence matrix features were used to construct the joint feature library. Then, the channel-weighting feature selection method was used to create the localization and material data set. Finally, PReLU-VGG19-Plus was trained on the localization and material image set to obtain the optimal localization and material neural network, while parameter-optimized XGBoost was trained on the localization and material data set to obtain the optimal localization and material classifier. On this basis, combined with the triple majority voting process, the combined localization and material framework were constructed. Extensive test results show that, the location-identification achieved by combined localization framework and the material-identification accuracy achieved by combined material framework are all 100%. The feasibility, stability, and superiority of the method proposed in this paper have been fully verified. It is an important supplement to the existing loose particle detection research, providing important reference for signal detection and classification research in similar fields, and effectively improving the reliability of sealed electronic equipment.

Open-Source Solutions for Real-Time 3D Geospatial Web Integration

Abstract. The recent development of Urban Digital Twin (UDT) structures and the spread of Internet of Things (IoT) technology have extended the concept of Digital Twin (DT) to urban environment representation, enabling real-time connections between geospatial representations and their real-world counterparts. Concurrently, advancements in surveying technologies and web geospatial services have facilitated the acquisition of extensive 2D and 3D geospatial data, making their spatial integration essential. While some proprietary software solutions have recently enabled initial examples of UDT by integrating various 3D geospatial data and real-time sensor acquisition, the development of open-source solutions for real-time web-based geospatial management remains a challenge. This work demonstrates a possible open-source solution for developing a UDT accessible via the web. The case study involves the web visualization platform of the new Faculty of ITC building at the University of Twente in Enschede (The Netherlands), which is connected in real-time with weather data services provided by Visual Crossing (1). The platform is based on an HTML5 framework and employs WebGL JavaScript graphic libraries to visualize different modules of 3D web visualization that integrate various local and remote 3D datasets. The framework adopted in this experiment can be used in the future for developing low-cost UDT web platforms beneficial for researchers, municipalities, and private companies interested in the real-time geospatial management of urban environments.

Black Carbon in Climate Studies: A Bibliometric Analysis of Research Trends and Topics

Black carbon is a short-lived climate warming agent and serves as a crucial factor influencing the climate. Numerous models, observations, and laboratory studies have been conducted to quantify black carbon’s direct or indirect impacts on the climate. Here, we applied bibliometric analysis to identify research trends and key topics on black carbon in the climate field. Based on the Web of Science (WOS) Core Collection database, a total of 4903 documents spanning the period from 2000 to 2023 were retrieved and screened, focusing on the topic of black carbon in the climate field, resulting in the Black-Carbon Climate Local (BCL) dataset. Our study examines the influence and trends of major countries, institutions, and authors in this field. The results show that China and the United States hold leading positions in terms of the number of publications. Based on keyword networks, the BCL dataset is segmented into six distinct research directions, and representative keywords of each direction include biomass burning, radiative forcing, air pollution, aerosol optical depth, optical properties, and biochar. This study helps to identify the current research status and trends of black carbon in the climate, highlighting main research directions and emerging topics.

Comparative Analysis of the Response Accuracies of Large Language Models in the Korean National Dental Hygienist Examination Across Korean and English Questions.

Large language models such as Gemini, GPT-3.5, and GPT-4 have demonstrated significant potential in the medical field. Their performance in medical licensing examinations globally has highlighted their capabilities in understanding and processing specialized medical knowledge. This study aimed to evaluate and compare the performance of Gemini, GPT-3.5, and GPT-4 in the Korean National Dental Hygienist Examination. The accuracy of answering the examination questions in both Korean and English was assessed. This study used a dataset comprising questions from the Korean National Dental Hygienist Examination over 5 years (2019-2023). A two-way analysis of variance (ANOVA) test was employed to investigate the impacts of model type and language on the accuracy of the responses. Questions were input into each model under standardized conditions, and responses were classified as correct or incorrect based on predefined criteria. GPT-4 consistently outperformed the other models, achieving the highest accuracy rates across both language versions annually. In particular, it showed superior performance in English, suggesting advancements in its training algorithms for language processing. However, all models demonstrated variable accuracies in subjects with localized characteristics, such as health and medical law. These findings indicate that GPT-4 holds significant promise for application in medical education and standardized testing, especially in English. However, the variability in performance across different subjects and languages underscores the need for ongoing improvements and the inclusion of more diverse and localized training datasets to enhance the models' effectiveness in multilingual and multicultural contexts.

Optimizing Catheter Verification

Purpose Accurate detection of central venous catheter (CVC) misplacement is crucial for patient safety and effective treatment. Existing artificial intelligence (AI) often grapple with the limitations of label inaccuracies and output interpretations that lack clinician-friendly comprehensibility. This study aims to introduce an approach that employs segmentation of support material and anatomy to enhance the precision and comprehensibility of CVC misplacement detection. Materials and Methods The study utilized 2 datasets: the publicly accessible RANZCR CLiP dataset and a bespoke in-house dataset of 1006 annotated supine chest x-rays. Three deep learning models were trained: a classification network, a segmentation network, and a combination of both. These models were evaluated using receiver operating characteristic analysis, area under the curve, DICE similarity coefficient, and Hausdorff distance. Results The combined model demonstrated superior performance with an area under the curve of 0.99 for correctly positioned CVCs and 0.95 for misplacements. The model maintained high efficacy even with reduced training data from the local dataset. Sensitivity and specificity rates were high, and the model effectively managed the segmentation and classification tasks, even in images with multiple CVCs and other support materials. Conclusions This study illustrates the potential of AI-based models in accurately and reliably determining CVC placement in chest x-rays. The proposed method shows high accuracy and offers improved interpretability, important for clinical decision-making. The findings also highlight the importance of dataset quality and diversity in training AI models for medical image analysis.

Balancing Efficiency and Accuracy: Enhanced Visual Simultaneous Localization and Mapping Incorporating Principal Direction Features

In visual Simultaneous Localization and Mapping (SLAM), operational efficiency and localization accuracy are equally crucial evaluation metrics. We propose an enhanced visual SLAM method to ensure stable localization accuracy while improving system efficiency. It can maintain localization accuracy even after reducing the number of feature pyramid levels by 50%. Firstly, we innovatively incorporate the principal direction error, which represents the global geometric features of feature points, into the error function for pose estimation, utilizing Pareto optimal solutions to improve the localization accuracy. Secondly, for loop-closure detection, we construct a feature matrix by integrating the grayscale and gradient direction of an image. This matrix is then dimensionally reduced through aggregation, and a multi-layer detection approach is employed to ensure both efficiency and accuracy. Finally, we optimize the feature extraction levels and integrate our method into the visual system to speed up the extraction process and mitigate the impact of the reduced levels. We comprehensively evaluate the proposed method on local and public datasets. Experiments show that the SLAM method maintained high localization accuracy after reducing the tracking time by 24% compared with ORB SLAM3. Additionally, the proposed loop-closure-detection method demonstrated superior computational efficiency and detection accuracy compared to the existing methods.

Real-time robust liver and gallbladder segmentation during laparoscopic cholecystectomy using convolutional neural networks: an analysis

Aim: Images in different laparoscopic cholecystectomy datasets are acquired using various camera models, parameters, and settings, with the annotation methods varying by institution. These factors result in inconsistent inference performance of the network model. This study aims to identify the optimal network model architecture for liver and gallbladder segmentation from several options. Then, the performance and robustness of the optimal network model are evaluated using an independent dataset that is not included in the training. Methods: The public dataset, CholecSeg8k, was utilized as the input for the network model training, validation, and testing. A local private dataset from KPJ Damansara Hospital, Selangor, Malaysia, was used for testing purposes only. For the implementation of liver and gallbladder segmentation, segmentation models, a public Python library was employed. Results: Among the experiments, highly accurate liver and gallbladder segmentation results were achieved using the feature pyramid network (FPN) architecture as the network model, with the Inception-ResNet-v2 architecture as the network backbone. The best-trained network model resulted in a loss of 0.070955, a mean intersection over union (IoU) score of 0.95896, and a mean F1-score of 0.9773 on the test set. However, visualized results for the private dataset contained considerable false-negative areas. Conclusion: The proposed automated technique has the potential to serve as an alternative to the conventional indocyanine green injection along with near-infrared fluorescence imaging (ICG-NIRF)-based method for liver and gallbladder segmentation during laparoscopic cholecystectomy. Future work will focus on enhancing the results of the private dataset. Additionally, a surgeon-assistant robotic arm that will use the liver and gallbladder segmentation results for camera steering will be analyzed.

Classification Of Plants By Their Fruits And Leaves Using Convolutional Neural Networks

The population growth of the world is exponential, this makes it imperative that we have an increase in food production. In this light, farmers, industries and researchers are struggling with identifying and classifying food plants. Over the years, there have been challenges that come with identifying fruits manually. It is time-consuming, labour intensive and requires experts to identify fruits because of the similarity in fruit’s leaves (citrus family), shapes, sizes and colour. A computerized detection technique is needed for the classification of fruits. Existing solutions to fruits classifications are majorly based on fruit or leave used as input. A new model using Convolutional Neural Network (CNN) is proposed for fruits classification. A dataset of 5 classes of fruits and fresh dry leaves plants (Mango, African almond, Guava, Avocado and Cashew) comprising of 1000 images each. The proposed model hyperparameters were: Conv2D layer, activation layer, dense layer, a learning and dropout rates of 0.001 and 0.5 respectively were used for the experiment. Various performances for accuracies of 91%, 97%, 78% and 97% were obtained for proposed model on local dataset, proposed model on benchmark dataset, benchmark model on local dataset and benchmark model on benchmark dataset. The proposed model is robust on both local and benchmark datasets and can be used for effective classification of plants

Practical and Robust Federated Learning With Highly Scalable Regression Training.

Privacy-preserving federated learning, as one of the privacy-preserving computation techniques, is a promising distributed and privacy-preserving machine learning (ML) approach for Internet of Medical Things (IoMT), due to its ability to train a regression model without collecting raw data of data owners (DOs). However, traditional interactive federated regression training (IFRT) schemes rely on multiple rounds of communication to train a global model and are still under various privacy and security threats. To overcome these problems, several noninteractive federated regression training (NFRT) schemes have been proposed and applied in a variety of scenarios. However, there are still several challenges: 1) how to protect the privacy of DOs' local dataset; 2) how to realize highly scalable regression training without linear dependence on sample dimension; 3) how to tolerate DOs' dropout; and 4) how to enable DOs to verify the correctness of aggregated results returned from the cloud service provider (CSP). In this article, we propose two practical noninteractive federated learning schemes with privacy-preserving for IoMT, named homomorphic encryption based NFRT (HE-NFRT) and double-masking protocol based NFRT (Mask-NFRT), respectively, which are based on a comprehensive consideration of NFRT, privacy concerns, high-efficiency, robustness, and verification mechanism. The security analyses display that our proposed schemes are able to protect the privacy of DOs' local training data, resist collusion attack, and support strong verification to each DO. The performance evaluation results demonstrate that our proposed HE-NFRT scheme is desirable for a high-dimensional and high-security IoMT application while Mask-NFRT scheme is desirable for a high-dimensional and large-scale IoMT application.

FedDP-SA: Boosting Differentially Private Federated Learning via Local Data Set Splitting

FedDP-SA: Boosting Differentially Private Federated Learning via Local Data Set Splitting

Multi-round decentralized dataset distillation with federated learning for Low Earth Orbit satellite communication

Satellite communication and Low Earth Orbit (LEO) satellites are important components of the 6G network, widely used for Earth observation tasks due to their low cost and short return period, making them a key technology for 6G network connectivity. Due to limitations in satellite system technology and downlink bandwidth, it is not feasible to download all high-resolution image information to ground stations. Even in existing federated learning (FL) methods, sharing well-trained parts of the model can still bottleneck with increasing model size. To address these challenges, we propose a new federated learning framework (FL-M3D) for LEO satellite communication that employs multi-round decentralized dataset distillation techniques. It allows satellites to independently extract local datasets and transmit them to ground stations instead of exchanging model parameters. Communication costs depend only on the size of the synthesized dataset and do not increase with larger models. However, the heterogeneity of satellite datasets can lead to sample ambiguity and decreased model convergence speed. Therefore, we propose distilling the datasets to mitigate the negative effects of data heterogeneity. Through experiments using real-world image datasets, FL-M3D reduces communication volume in simulated satellite networks by approximately 49.84% and achieves improved model performance.

Specialized gray matter segmentation via a generative adversarial network: application on brain white matter hyperintensities classification.

White matter hyperintensities (WMH) observed in T2 fluid-attenuated inversion recovery (FLAIR) images have emerged as potential markers of neurodegenerative diseases like Multiple Sclerosis (MS). Lacking comprehensive automated WMH classification systems in current research, there is a need to develop accurate detection and classification methods for WMH that will benefit the diagnosis and monitoring of brain diseases. Juxtacortical WMH (JCWMH) is a less explored subtype of WMH, primarily due to the hard definition of the cortex in FLAIR images, which is escalated by the presence of lesions to obtain appropriate gray matter (GM) masks. In this study, we present a method to perform a specialized GM segmentation developed for the classification of WMH, especially JCWMH. Using T1 and FLAIR images, we propose a pipeline to integrate masks of white matter, cerebrospinal fluid, ventricles, and WMH to create a unique mask to refine the primary GM map. Subsequently, we utilize this pipeline to generate paired data for training a conditional generative adversarial network (cGAN) to substitute the pipeline and reduce the inputs to only FLAIR images. The classification of WMH is then based on the distances between WMH and ventricular and GM masks. Due to the lack of multi-class labeled WMH datasets and the need for extensive data for training deep learning models, we attempted to collect a large local dataset and manually segment and label some data for WMH and ventricles. In JCWMH classification, the proposed method exhibited a Dice similarity coefficient, precision, and sensitivity of 0.76, 0.69, and 0.84, respectively. With values of 0.66, 0.55, and 0.81, the proposed method clearly outperformed the approach commonly used in the literature, which uses extracted GM masks from registered T1 images on FLAIR. After training, the method proves its efficiency by providing results in less than one second. In contrast, the usual approach would require at least two minutes for registration and segmentation alone. The proposed method is automated and fast and requires no initialization as it works exclusively with FLAIR images. Such innovative methods will undoubtedly facilitate accurate and meaningful analysis of WMH in clinical practice by reducing complexity and increasing efficiency.

MA-VoxelMorph: Multi-scale attention-based VoxelMorph for nonrigid registration of thoracoabdominal CT images

This paper aims to develop a nonrigid registration method of preoperative and intraoperative thoracoabdominal CT images in computer-assisted interventional surgeries for accurate tumor localization and tissue visualization enhancement. However, fine structure registration of complex thoracoabdominal organs and large deformation registration caused by respiratory motion is challenging. To deal with this problem, we propose a 3D multi-scale attention VoxelMorph (MA-VoxelMorph) registration network. To alleviate the large deformation problem, a multi-scale axial attention mechanism is utilized by using a residual dilated pyramid pooling for multi-scale feature extraction, and position-aware axial attention for long-distance dependencies between pixels capture. To further improve the large deformation and fine structure registration results, a multi-scale context channel attention mechanism is employed utilizing content information via adjacent encoding layers. Our method was evaluated on four public lung datasets (DIR-Lab dataset, Creatis dataset, Learn2Reg dataset, OASIS dataset) and a local dataset. Results proved that the proposed method achieved better registration performance than current state-of-the-art methods, especially in handling the registration of large deformations and fine structures. It also proved to be fast in 3D image registration, using about 1.5 s, and faster than most methods. Qualitative and quantitative assessments proved that the proposed MA-VoxelMorph has the potential to realize precise and fast tumor localization in clinical interventional surgeries.

The asymmetric distribution of rainfall frequency and amounts in India

Studies of rainfall usually focus on the total amount precipitating throughout a certain period. Compared to rain rates associated with extreme events, the rain rates associated with the most frequent events is understudied. In this study, the characteristics of daily precipitation in India are explored using two metrics − rain frequency peak (the most frequent non-zero rain rate) and rain amount peak (the rain rate at which the most amount of rain falls). These metrics are computed over India using local and global datasets to investigate the characteristics of typical daily precipitation accumulations. These values are sensitive to the dataset used for this analysis since the temporal and spatial resolution of the rainfall data will influence the rain frequency peak and rain amount peak. Our study reveals the rain frequency peak is highest during the summer monsoon, while the winter season exhibits lower values, particularly at higher latitudes. Similarly, the rain amount distribution indicates dominance of heavy rain rates during the monsoon and post-monsoon seasons, leading to high total precipitation. The maximum rain frequency peak for any region in India reaches up to a value of 35 mm/day while the maximum rain amount peak reaches up to a value as high as 90 mm/day. These metrics would be useful in systematically evaluating typical daily precipitation in regional climate models and assessing downstream impacts of uneven precipitation such as lower crop yields, flood-drought alterations, and fluctuating water availability.

An enzyme-inspired specificity in deep learning model for sleep stage classification using multi-channel PSG signals input: Separating training approach and its performance on cross-dataset validation for generalizability

Numerous automatic sleep stage classification systems have been developed, but none have become effective assistive tools for sleep technicians due to issues with generalization. Four key factors hinder the generalization of these models are instruments, montage of recording, subject type, and scoring manual factors. This study aimed to develop a deep learning model that addresses generalization problems by integrating enzyme-inspired specificity and employing separating training approaches. Subject type and scoring manual factors were controlled, while the focus was on instruments and montage of recording factors. The proposed model consists of three sets of signal-specific models including EEG-, EOG-, and EMG-specific model. The EEG-specific models further include three sets of channel-specific models. All signal-specific and channel-specific models were established with data manipulation and weighted loss strategies, resulting in three sets of data manipulation models and class-specific models, respectively. These models were CNNs. Additionally, BiLSTM models were applied to EEG- and EOG-specific models to obtain temporal information. Finally, classification task for sleep stage was handled by ‘the-last-dense’ layer. The optimal sampling frequency for each physiological signal was identified and used during the training process. The proposed model was trained on MGH dataset and evaluated using both within dataset and cross-dataset. For MGH dataset, overall accuracy of 81.05 %, MF1 of 79.05 %, Kappa of 0.7408, and per-class F1-scores: W (84.98 %), N1 (58.06 %), N2 (84.82 %), N3 (79.20 %), and REM (88.17 %) can be achieved. Performances on cross-datasets are as follows: SHHS1 200 records reached 79.54 %, 70.56 %, and 0.7078; SHHS2 200 records achieved 76.77 %, 66.30 %, and 0.6632; Sleep-EDF 153 records gained 78.52 %, 72.13 %, and 0.7031; and BCI-MU (local dataset) 94 records achieved 83.57 %, 82.17 %, and 0.7769 for overall accuracy, MF1, and Kappa respectively. Additionally, the proposed model has approximately 9.3 M trainable parameters and takes around 26 s to process one PSG record. The results indicate that the proposed model demonstrates generalizability in sleep stage classification and shows potential as a feasibility tool for real-world applications. Additionally, enzyme-inspired specificity effectively addresses the challenges posed by varying montage of recording, while the identified optimal frequencies mitigate instrument-related issues.

Segmentation Synergy with a Dual U-Net and Federated Learning with CNNRF Models for Enhanced Brain Tumor Analysis

Background: Brain tumours represent a diagnostic challenge, especially in the imaging area, where the differentiation of normal and pathologic tissues should be precise. The use of up-to-date machine learning techniques would be of great help in terms of brain tumor identification accuracy from MRI data. Objective: This research paper aims to check the efficiency of a federated learning method that joins two classifiers, such as convolutional neural networks (CNNs) and random forests (R.F.F.), with dual U-Net segmentation for federated learning. This procedure benefits the image identification task on preprocessed MRI scan pictures that have already been categorized. Methods: In addition to using a variety of datasets, federated learning was utilized to train the CNN-RF model while taking data privacy into account. The processed MRI images with Median, Gaussian, and Wiener filters are used to filter out the noise level and make the feature extraction process easy and efficient. The surgical part used a dual U-Net layout, and the performance assessment was based on precision, recall, F1-score, and accuracy. Results: The model achieved excellent classification performance on local datasets as CRPs were high, from 91.28% to 95.52% for macro, micro, and weighted averages. Throughout the process of federated averaging, the collective model outperformed by reaching 97% accuracy compared to those of 99%, which were subjected to different clients. The correctness of how data is used helps the federated averaging method convert individual model insights into a consistent global model while keeping all personal data private. Conclusion: The combined structure of the federated learning framework, CNN-RF hybrid model, and dual U-Net segmentation is a robust and privacypreserving approach for identifying MRI images from brain tumors. The results of the present study exhibited that the technique is promising in improving the quality of brain tumor categorization and provides a pathway for practical utilization in clinical settings.

Explainable deep learning for diabetes diagnosis with DeepNetX2

Diabetes is a leading health global health challenge because of its high blood sugar levels and the risk of extensive damage to other internal organs. Early and accurate identification of diabetes is important because it may cause other diseases including heart diseases and nerve damage. Despite the success of using machine learning, especially deep learning in automated diabetes diagnosis. These models are mostly black boxes which rarely offer comprehensive explanations and interpretations of the results. This study introduces DeepNetX2, a proposed custom deep neural network designed to overcome these challenges by integrating Explainable Artificial Intelligence (XAI) techniques, specifically Local Interpretable Model-agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP). These techniques make the decision-making process of the model transparent, thereby increasing the credibility of the predictions. The proposed methodology entails a comprehensive data preprocessing technique that involves a customized Spearman’s correlation coefficient feature selection strategy. This preprocessing restricts complexity to only relevant features that promote effectiveness, instead of oversimplification, to the point of decreasing efficiency. DeepNetX2 was rigorously tested on three datasets: the PIMA dataset, local private dataset, and Type-2 diabetes dataset, achieving test accuracies of 94.81%, 97.87%, and 97.50%, respectively. These results demonstrate not only the superior performance of DeepNetX2 compared with existing models, but also its enhanced interpretability. Based on the proposed model, an appropriate and rapid strategy for diabetes prediction was developed which is very useful for improving diagnostic integrity and patient health.

Three-color single-molecule localization microscopy in chromatin.

Super-resolution microscopy has revolutionized our ability to visualize structures below the diffraction limit of conventional optical microscopy and is particularly useful for investigating complex biological targets like chromatin. Chromatin exhibits a hierarchical organization with structural compartments and domains at different length scales, from nanometers to micrometers. Single molecule localization microscopy (SMLM) methods, such as STORM, are essential for studying chromatin at the supra-nucleosome level due to their ability to target epigenetic marks that determine chromatin organization. Multi-label imaging of chromatin is necessary to unpack its structural complexity. However, these efforts are challenged by the high-density nuclear environment, which can affect antibody binding affinities, diffusivity and non-specific interactions. Optimizing buffer conditions, fluorophore stability, and antibody specificity is crucial for achieving effective antibody conjugates. Here, we demonstrate a sequential immunolabeling protocol that reliably enables three-label studies within the dense nuclear environment. This protocol couples multiplexed localization datasets with a robust analysis algorithm, which utilizes localizations from one target as seed points for distance, density and multi-label joint affinity measurements to explore complex organization of all three targets. Applying this multi-plexed algorithm to analyze distance and joint density reveals that heterochromatin and euchromatin are not-distinct territories, but that localization of transcription and euchromatin couple with the periphery of heterochromatic clusters. This work is a crucial step in molecular imaging of the dense nuclear environment as multi-label capacity enables for investigation of complex multi-component systems like chromatin with enhanced accuracy.