arXiv subjects

Andrey Fedorov

Publications and source records attributed to Andrey Fedorov.

At least 19 recordsLinked to original sources

arXiv2026

Annotating anatomy and pathology in the National Lung Screening Trial computed tomography images

Large-scale public medical imaging datasets contribute critically to translational research. When accompanied by rich clinical and multi-omics data, they can stimulate exploratory research and enable secondary analyses. Expert annotations of such imaging collections can support the development of new image analysis tools. Continuous enrichment of images with image-derived data makes them more usable for researchers without expertise in image analysis or access to large-scale computational resources. The National Lung Screening Trial (NLST) released a rich longitudinal dataset that includes Computed Tomography (CT) images for over 26,000 patients. We introduce three Digital Imaging and Communications in Medicine (DICOM) formatted datasets, complementing NLST CT images, shared as analysis results in the National Cancer Institute Imaging Data Commons (IDC). Two of those (IDC NLSTSeg and IDC NLSTSybil) contain DICOM-harmonized annotations and extracted measurements (for 581 and 601 NLST patients, respectively) shared earlier using research formats (Sybil and NLSTseg). The third one (TotalSegmentator-CT-Segmentations) contains volumetric segmentations generated using TotalSegmentator and radiomics features for each segment for 26,194 NLST patients.

eess.IV↗

arXiv2026

Coronary artery calcification assessment in National Lung Screening Trial CT images (DeepCAC2)

Coronary artery calcification (CAC) is a strong predictor of cardiovascular risk but remains underutilized in clinical routine thoracic imaging due to the need for dedicated imaging protocols and manual annotation. We present DeepCAC2, a publicly available dataset containing automated CAC segmentations, coronary artery calcium scores, and derived risk categories generated from low-dose chest CT scans of the National Lung Screening Trial (NLST). Using a fully automated deep learning pipeline trained on expert-annotated cardiac CT data, we processed 127,776 CT scans from 26,228 individuals and generated standardized CAC segmentations and risk estimates for each acquisition. We already provide a public dashboard as a simple tool to visually inspect a random subset of 200 NLST patients of the dataset. The dataset will be released with DICOM-compatible segmentation objects and structured metadata to support reproducible downstream analysis. The deep learning pipeline will be made publicly available as a DICOM-compatible MHub.ai container. DeepCAC2 provides a transparent, large-scale, public, fully reproducible resource for research in cardiovascular risk assessment, opportunistic screening, and imaging biomarker development.

eess.IV↗

arXiv2026

MHub.ai: A Simple, Standardized, and Reproducible Platform for AI Models in Medical Imaging

Artificial intelligence (AI) has the potential to transform medical imaging by automating image analysis and accelerating clinical research. However, research and clinical use are limited by the wide variety of AI implementations and architectures, inconsistent documentation, and reproducibility issues. Here, we introduce MHub$.$ai, an open-source, container-based platform that standardizes access to AI models with minimal configuration, promoting accessibility and reproducibility in medical imaging. MHub$.$ai packages models from peer-reviewed publications into standardized containers that support direct processing of DICOM and other formats, provide a unified application interface, and embed structured metadata. Each model is accompanied by publicly available reference data that can be used to confirm model operation. MHub$.$ai includes an initial set of state-of-the-art segmentation, prediction, and feature extraction models for different modalities. The modular framework enables adaptation of any model and supports community contributions. We demonstrate the utility of the platform in a clinical use case through comparative evaluation of lung segmentation models. To further strengthen transparency and reproducibility, we publicly release the generated segmentations and evaluation metrics and provide interactive dashboards that allow readers to inspect individual cases and reproduce or extend our analysis. By simplifying model use, MHub$.$ai enables side-by-side benchmarking with identical execution commands and standardized outputs, and lowers the barrier to clinical translation.

cs.AI↗

arXiv2025

In search of truth: Evaluating concordance of AI-based anatomy segmentation models

Purpose AI-based methods for anatomy segmentation can help automate characterization of large imaging datasets. The growing number of similar in functionality models raises the challenge of evaluating them on datasets that do not contain ground truth annotations. We introduce a practical framework to assist in this task. Approach We harmonize the segmentation results into a standard, interoperable representation, which enables consistent, terminology-based labeling of the structures. We extend 3D Slicer to streamline loading and comparison of these harmonized segmentations, and demonstrate how standard representation simplifies review of the results using interactive summary plots and browser-based visualization using OHIF Viewer. To demonstrate the utility of the approach we apply it to evaluating segmentation of 31 anatomical structures (lungs, vertebrae, ribs, and heart) by six open-source models - TotalSegmentator 1.5 and 2.6, Auto3DSeg, MOOSE, MultiTalent, and CADS - for a sample of Computed Tomography (CT) scans from the publicly available National Lung Screening Trial (NLST) dataset. Results We demonstrate the utility of the framework in enabling automating loading, structure-wise inspection and comparison across models. Preliminary results ascertain practical utility of the approach in allowing quick detection and review of problematic results. The comparison shows excellent agreement segmenting some (e.g., lung) but not all structures (e.g., some models produce invalid vertebrae or rib segmentations). Conclusions The resources developed are linked from https://imagingdatacommons.github.io/segmentation-comparison/ including segmentation harmonization scripts, summary plots, and visualization tools. This work assists in model evaluation in absence of ground truth, ultimately enabling informed model selection.

eess.IV↗

arXiv2025

From Data to Diagnosis: A Large, Comprehensive Bone Marrow Dataset and AI Methods for Childhood Leukemia Prediction

Leukemia diagnosis primarily relies on manual microscopic analysis of bone marrow morphology supported by additional laboratory parameters, making it complex and time consuming. While artificial intelligence (AI) solutions have been proposed, most utilize private datasets and only cover parts of the diagnostic pipeline. Therefore, we present a large, high-quality, publicly available leukemia bone marrow dataset spanning the entire diagnostic process, from cell detection to diagnosis. Using this dataset, we further propose methods for cell detection, cell classification, and diagnosis prediction. The dataset comprises 246 pediatric patients with diagnostic, clinical and laboratory information, over 40 000 cells with bounding box annotations and more than 28 000 of these with high-quality class labels, making it the most comprehensive dataset publicly available. Evaluation of the AI models yielded an average precision of 0.96 for the cell detection, an area under the curve of 0.98, and an F1-score of 0.61 for the 33-class cell classification, and a mean F1-score of 0.90 for the diagnosis prediction using predicted cell counts. While the proposed approaches demonstrate their usefulness for AI-assisted diagnostics, the dataset will foster further research and development in the field, ultimately contributing to more precise diagnoses and improved patient outcomes.

cs.LG↗

arXiv2025

CADS: A Comprehensive Anatomical Dataset and Segmentation for Whole-Body Anatomy in Computed Tomography

Accurate delineation of anatomical structures in volumetric CT scans is crucial for diagnosis and treatment planning. While AI has advanced automated segmentation, current approaches typically target individual structures, creating a fragmented landscape of incompatible models with varying performance and disparate evaluation protocols. Foundational segmentation models address these limitations by providing a holistic anatomical view through a single model. Yet, robust clinical deployment demands comprehensive training data, which is lacking in existing whole-body approaches, both in terms of data heterogeneity and, more importantly, anatomical coverage. In this work, rather than pursuing incremental optimizations in model architecture, we present CADS, an open-source framework that prioritizes the systematic integration, standardization, and labeling of heterogeneous data sources for whole-body CT segmentation. At its core is a large-scale dataset of 22,022 CT volumes with complete annotations for 167 anatomical structures, representing a significant advancement in both scale and coverage, with 18 times more scans than existing collections and 60% more distinct anatomical targets. Building on this diverse dataset, we develop the CADS-model using established architectures for accessible and automated full-body CT segmentation. Through comprehensive evaluation across 18 public datasets and an independent real-world hospital cohort, we demonstrate advantages over SoTA approaches. Notably, thorough testing of the model's performance in segmentation tasks from radiation oncology validates its direct utility for clinical interventions. By making our large-scale dataset, our segmentation models, and our clinical software tool publicly available, we aim to advance robust AI solutions in radiology and make comprehensive anatomical analysis accessible to clinicians and researchers alike.

eess.IV↗

arXiv2025

Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation

Recent advances in deep learning have led to robust automated tools for segmentation of abdominal computed tomography (CT). Meanwhile, segmentation of magnetic resonance imaging (MRI) is substantially more challenging due to the inherent signal variability and the increased effort required for annotating training datasets. Hence, existing approaches are trained on limited sets of MRI sequences, which might limit their generalizability. To characterize the landscape of MRI abdominal segmentation tools, we present here a comprehensive benchmarking of the three state-of-the-art and open-source models: MRSegmentator, MRISegmentator-Abdomen, and TotalSegmentator MRI. Since these models are trained using labor-intensive manual annotation cycles, we also introduce and evaluate ABDSynth, a SynthSeg-based model purely trained on widely available CT segmentations (no real images). More generally, we assess accuracy and generalizability by leveraging three public datasets (not seen by any of the evaluated methods during their training), which span all major manufacturers, five MRI sequences, as well as a variety of subject conditions, voxel resolutions, and fields-of-view. Our results reveal that MRSegmentator achieves the best performance and is most generalizable. In contrast, ABDSynth yields slightly less accurate results, but its relaxed requirements in training data make it an alternative when the annotation budget is limited. The evaluation code and datasets are given for future benchmarking at https://github.com/deepakri201/AbdoBench, along with inference code and weights for ABDSynth.

eess.IV↗

arXiv2025

A Tale of Two Shocks

Energetic particles in interplanetary space are normally measured at time scales that are long compared to the ion gyroperiod. Such observations by necessity average out the microphysics associated with the acceleration and transport of 10s - 100s keV particles. We investigate previously unseen non-equilibrium features that only become observable at very high time resolution, and discuss possible explanations of these features. We use unprecedentedly high-time-resolution data that were acquired by the in situ instruments on Solar Orbiter in the vicinity of two interplanetary shocks observed on 2023-11-29 07:51:17 UTC and 2023-11-30 10:47:26 UTC at $\sim 0.83$ astronomical units from the Sun. The solar-wind proton beam population follows the magnetic field instantaneously, on time scales which are significantly shorter than a gyro-period. Energetic particles, despite sampling large volumes of space, vary on remarkably short time scales, typically on the order of the convection time of their gyro-radius. Non-equilibrium features such as bump-on-tail distributions of energetic particles are formed by small-scale magnetic structures in the IMF. High-time-resolution observations show previously unobserved microphysics in the vicinity of two traveling interplanetary shocks, including ion reflection at a current sheet, which may explain where ions are reflected in shock acceleration.

astro-ph.SR↗

arXiv2025

How does the limited resolution of space plasma analyzers affect the accuracy of space plasma measurements?

We investigate the systematic errors in measured plasma velocity distribution functions and their corresponding velocity moments, arising from the limited energy and angular resolution of top-hat electrostatic analyzers. For this purpose, we develop a forward model of a concept analyzer that simulates observations of typical solar wind proton plasma particles with their velocities following a Maxwell distribution function. We then review the standard conversion of the observations to physical parameters and evaluate the errors arising from the limited resolution of the modeled instrument. We show that the limited resolution of the instrument results in velocity distributions that underestimate the core and overestimate the tails of the actual Maxwellian plasma velocity distribution functions. As a consequence, the velocity moments of the observed plasma underestimate the proton density and overestimate the proton temperature. Moreover, we show that the examined errors become significant for cold and fast plasma protons. We finally determine a mathematical formula that predicts these systematic inaccuracies based on specific plasma inputs and instrument features. Our results inform and contextualize future evaluations of observations by analyzers in various plasma regimes.

physics.space-ph↗

arXiv2024

Ion-Scale Solitary Structures in the Solar Wind Observed by Solar Orbiter and Parker Solar Probe

We investigate a class of ion-scale magnetic solitary structures in the solar wind, characterized by distinct magnetic field enhancements and bipolar rotations over spatial scales of several proton inertial lengths. These structures are revisited using high-resolution data from the Solar Orbiter and Parker Solar Probe missions. Using a machine learning-based method, we identified nearly a thousand such structures, providing new insights into their evolution and physical properties. Statistical analysis shows that these structures are more abundant closer to the Sun, with occurrence rates peaking around (30 - 40, R_sun) and decreasing farther out. High-cadence measurements reveal that these structures are predominantly found in low-beta (beta <= 1) environments, with consistent fluctuations in density, velocity, and magnetic field. Magnetic field enhancements are often accompanied by plasma density drops, which, under near pressure balance, limit field increases. This leads to small fractional field enhancements near the Sun (approximately 0.01 at 20 R_sun), making detection challenging. Magnetic field variance analysis indicates that these structures are primarily oblique to the local magnetic field. Alfv\'enic velocity-magnetic field correlations suggest that most of these structures, unlike most near-Sun solar wind fluctuations, exhibit sunward-directed Alfv\'enic polarization in the plasma frame. We compare these findings with previous studies, discussing possible generation mechanisms and their implications for the turbulent cascade in the near-Sun Alfv\'enic solar wind. While these structures might be Alfv\'enic solitons, vortices, or flux ropes, we refrain from a definitive classification pending further evidence. Further high-resolution observations and simulations are needed to fully understand their origins and impacts.

physics.space-ph↗

arXiv2024

Rule-based outlier detection of AI-generated anatomy segmentations

There is a dire need for medical imaging datasets with accompanying annotations to perform downstream patient analysis. However, it is difficult to manually generate these annotations, due to the time-consuming nature, and the variability in clinical conventions. Artificial intelligence has been adopted in the field as a potential method to annotate these large datasets, however, a lack of expert annotations or ground truth can inhibit the adoption of these annotations. We recently made a dataset publicly available including annotations and extracted features of up to 104 organs for the National Lung Screening Trial using the TotalSegmentator method. However, the released dataset does not include expert-derived annotations or an assessment of the accuracy of the segmentations, limiting its usefulness. We propose the development of heuristics to assess the quality of the segmentations, providing methods to measure the consistency of the annotations and a comparison of results to the literature. We make our code and related materials publicly available at https://github.com/ImagingDataCommons/CloudSegmentatorResults and interactive tools at https://huggingface.co/spaces/ImagingDataCommons/CloudSegmentatorResults.

eess.IV↗

arXiv2024

Automatic classification of prostate MR series type using image content and metadata

With the wealth of medical image data, efficient curation is essential. Assigning the sequence type to magnetic resonance images is necessary for scientific studies and artificial intelligence-based analysis. However, incomplete or missing metadata prevents effective automation. We therefore propose a deep-learning method for classification of prostate cancer scanning sequences based on a combination of image data and DICOM metadata. We demonstrate superior results compared to metadata or image data alone, and make our code publicly available at https://github.com/deepakri201/DICOMScanClassification.

eess.IV↗

arXiv2024

Towards Automatic Abdominal MRI Organ Segmentation: Leveraging Synthesized Data Generated From CT Labels

Deep learning has shown great promise in the ability to automatically annotate organs in magnetic resonance imaging (MRI) scans, for example, of the brain. However, despite advancements in the field, the ability to accurately segment abdominal organs remains difficult across MR. In part, this may be explained by the much greater variability in image appearance and severely limited availability of training labels. The inherent nature of computed tomography (CT) scans makes it easier to annotate, resulting in a larger availability of expert annotations for the latter. We leverage a modality-agnostic domain randomization approach, utilizing CT label maps to generate synthetic images on-the-fly during training, further used to train a U-Net segmentation network for abdominal organs segmentation. Our approach shows comparable results compared to fully-supervised segmentation methods trained on MR data. Our method results in Dice scores of 0.90 (0.08) and 0.91 (0.08) for the right and left kidney respectively, compared to a pretrained nnU-Net model yielding 0.87 (0.20) and 0.91 (0.03). We will make our code publicly available.

eess.IV↗

arXiv2023

Real-Time Dynamic Data Driven Deformable Registration for Image-Guided Neurosurgery: Computational Aspects

Current neurosurgical procedures utilize medical images of various modalities to enable the precise location of tumors and critical brain structures to plan accurate brain tumor resection. The difficulty of using preoperative images during the surgery is caused by the intra-operative deformation of the brain tissue (brain shift), which introduces discrepancies concerning the preoperative configuration. Intra-operative imaging allows tracking such deformations but cannot fully substitute for the quality of the pre-operative data. Dynamic Data Driven Deformable Non-Rigid Registration (D4NRR) is a complex and time-consuming image processing operation that allows the dynamic adjustment of the pre-operative image data to account for intra-operative brain shift during the surgery. This paper summarizes the computational aspects of a specific adaptive numerical approximation method and its variations for registering brain MRIs. It outlines its evolution over the last 15 years and identifies new directions for the computational aspects of the technique.

eess.IV↗

arXiv2023

Evaluation of software impact designed for biomedical research: Are we measuring what's meaningful?

Software is vital for the advancement of biology and medicine. Analysis of usage and impact metrics can help developers determine user and community engagement, justify additional funding, encourage additional use, identify unanticipated use cases, and help define improvement areas. However, there are challenges associated with these analyses including distorted or misleading metrics, as well as ethical and security concerns. More attention to the nuances involved in capturing impact across the spectrum of biological software is needed. Furthermore, some tools may be especially beneficial to a small audience, yet may not have compelling typical usage metrics. We propose more general guidelines, as well as strategies for more specific types of software. We highlight outstanding issues regarding how communities measure or evaluate software impact. To get a deeper understanding of current practices for software evaluations, we performed a survey of participants in the Informatics Technology for Cancer Research (ITCR) program funded by the National Cancer Institute (NCI). We also investigated software among this community and others to assess how often infrastructure that supports such evaluations is implemented and how this impacts rates of papers describing usage of the software. We find that developers recognize the utility of analyzing software usage, but struggle to find the time or funding for such analyses. We also find that infrastructure such as social media presence, more in-depth documentation, the presence of software health metrics, and clear information on how to contact developers seem to be associated with increased usage rates. Our findings can help scientific software developers make the most out of evaluations of their software.

cs.SE↗

arXiv2023

Enrichment of the NLST and NSCLC-Radiomics computed tomography collections with AI-derived annotations

Public imaging datasets are critical for the development and evaluation of automated tools in cancer imaging. Unfortunately, many do not include annotations or image-derived features, complicating their downstream analysis. Artificial intelligence-based annotation tools have been shown to achieve acceptable performance and thus can be used to automatically annotate large datasets. As part of the effort to enrich public data available within NCI Imaging Data Commons (IDC), here we introduce AI-generated annotations for two collections of computed tomography images of the chest, NSCLC-Radiomics, and the National Lung Screening Trial. Using publicly available AI algorithms we derived volumetric annotations of thoracic organs at risk, their corresponding radiomics features, and slice-level annotations of anatomical landmarks and regions. The resulting annotations are publicly available within IDC, where the DICOM format is used to harmonize the data and achieve FAIR principles. The annotations are accompanied by cloud-enabled notebooks demonstrating their use. This study reinforces the need for large, publicly accessible curated datasets and demonstrates how AI can be used to aid in cancer imaging.

cs.CV↗

arXiv2023

The NCI Imaging Data Commons as a platform for reproducible research in computational pathology

Background and Objectives: Reproducibility is a major challenge in developing machine learning (ML)-based solutions in computational pathology (CompPath). The NCI Imaging Data Commons (IDC) provides >120 cancer image collections according to the FAIR principles and is designed to be used with cloud ML services. Here, we explore its potential to facilitate reproducibility in CompPath research. Methods: Using the IDC, we implemented two experiments in which a representative ML-based method for classifying lung tumor tissue was trained and/or evaluated on different datasets. To assess reproducibility, the experiments were run multiple times with separate but identically configured instances of common ML services. Results: The AUC values of different runs of the same experiment were generally consistent. However, we observed small variations in AUC values of up to 0.045, indicating a practical limit to reproducibility. Conclusions: We conclude that the IDC facilitates approaching the reproducibility limit of CompPath research (i) by enabling researchers to reuse exactly the same datasets and (ii) by integrating with cloud ML services so that experiments can be run in identically configured computing environments.

cs.CV↗

arXiv2023

Parallel Modeling of the Acoustic Signal Propagation in a Cased Well

A numerical method is proposed for carrying out a full-scale simulation of the process of propagation of an acoustic signal in a cased well. The main goal is to study the interaction of the wave field with the vertical boundary of the cement filling the annular and near-pipe space. Particular attention is paid to the analysis of the intensity of the wave reflected from this boundary, depending on the degree of hardening of the upper edge of the cement. A distinctive feature of the problem is the presence in it of several significantly different spatial scales. So, the length of the well can vary from several hundred meters to several kilometers, the diameter of the well is a few tens of centimeters, and the thickness of the casing string, as a rule, does not exceed a few first centimeters. It is this diversity of scale that requires the organization of parallel computing, the organization of which is based on the spatial decomposition of the region and the involvement of Message Passing Interface (MPI). The results of test calculations are presented and an analysis of the strong and weak scalability of the developed software is carried out.

physics.geo-ph↗