Effortless, robust, adaptable and code free. Our platform enables users to seamlessly construct image processing pipelines by integrating predefined operations, all within an intuitive visual programming interface. For those who want to add code you can easily deploy your own features in Heros interface.
Visual programming is the core of Hero Imaging. Designing a workflow is trivially easy and extremely fast using drag-and-drop, with hundreds of functions available at your fingertips. Try out new ideas and compare different approaches to quickly determine the best solution to any challenge.
Create custom plugin nodes to run Python code (Matlab coming soon) directly from Hero, with full debugging support.
Import neural networks into Hero and access cutting edge functionality with drag-and-drop simplicity. The best part is that you can easily share networks and plugins with other users.
Seamless integration into the DICOM ecosystem. Forget about USB and portable hard drives – it’s all about simplicity and efficiency. Hero also allows you to organise data into private or shared databases, simplifying collaborations and data structuring.
Participate in discussions, ask for tips or share your knowledge, nodes or workflows with other users.
Hero has been constructed to facilitate collaboration - within and between teams. Users can easily share workflows, AI models and bespoke plugins/add-ons with colleagues and partners. Hero also comes with efficient database tools to make your projects run smoothly.
Great science is meant to be shared. We're happy to show you how it works and how it can leverage your projects.
New, successful ideas drive us forward as a society. We proudly serve imaging heroes around the globe that strive to make the world a better place. Please let us know if your acts of heroism aren’t listed.
Polymer gel dosimetry with MRI-readout for 3D dose verification-detector characteristics and clinical applications Thi Guldhill
Learn moreDenoising and uncertainty estimation in parameter mapping with approximate Bayesian deep image priors
Learn moreOptimization of b-value Acquisition and Parameter Estimation of Intravoxel Incoherent Motion Measurements Ivan Rashid
Learn moreThe grade of individual prostate cancer lesions predicted by magnetic resonance imaging and positron emission tomography
Learn moreSensitivity of standardised radiomics algorithms to mask generation across different software platforms
Learn moreReal‐time motion‐including dose estimation of simulated multi‐leaf collimator‐tracked magnetic resonance‐guided radiotherapy
Learn moreInfluence of MR and clinical parameters on biochemical recurrence in re-irradiation of prostate cancer
Learn moreContouring practices and artefact management within a synthetic CT-based radiotherapy workflow for the central nervous system.
Learn moreHyaluronic acid spacer in prostate cancer radiotherapy: dosimetric effects, spacer stability and long-term toxicity and PRO in a phase II study
Learn moreImpact of attenuation correction of radiotherapy hardware for positron emission tomography‐magnetic resonance in ano‐rectal radiotherapy patients
Learn moreRadiation-dependent Demyelination in Normal Appearing White Matter in Glioma Patients, Determined Using Quantitative Magnetic Resonance Imaging
Learn moreHistopathology-validated lesion detection rates of clinically significant prostate cancer with mpMRI,[68Ga] PSMA-11-PET and [11C] Acetate-PET
Learn moreA hybrid multi-particle approach to range assessment-based treatment verification in particle therapy
Learn moreSynthetic computed tomography based dose calculation in prostate cancer patients with hip prostheses for magnetic resonance imaging-only radiotherapy
Learn moreGeometric impact and dose estimation of on-patient placement of a lightweight receiver coil in a clinical magnetic resonance imaging-only radiotherapy …
Learn moreThe influence of dual-energy computed tomography image noise in proton therapy treatment planning
Learn moreMotion management optimization in radiotherapy: From the most common to the most uncommon patient
Learn moreImplementation and Evaluation of Uncertainty Estimation for Advanced Pharmacokinetic Models in DCE-MRI
Learn moreEvaluating the quality of patient-specific deformable image registration in adaptive radiotherapy using a digitally enhanced head and neck phantom
Learn morePre-trial quality assurance of diffusion-weighted MRI for radiomic analysis and the role of harmonisation
Learn moreHead and neck cancer patient positioning using synthetic CT data in MRI‐only radiation therapy
Learn moreNon-Invasive Evaluation of Intradiscal Deformation during Axial Loading of the Spine Using Deformation-Field Magnetic Resonance Imaging: A Potential Tool for …
Learn moreDaily dose evaluation based on synthetic CTs for breast cancer patients: accuracy of dose and complication risk assessment
Learn moreNeural network-assisted automated image registration for MRI-guided adaptive brachytherapy in cervical cancer
Learn moreHead and neck cancer patient positioning using synthetic CT data in MRI‐only radiation therapy
Learn moreAn MRI sequence independent convolutional neural network for synthetic head CT generation in proton therapy
Learn moreDeep learning-based automatic delineation of anal cancer gross tumour volume: a multimodality comparison of CT, PET and MRI
Learn moreDeep learning‐based classification and structure name standardization for organ at risk and target delineations in prostate cancer radiotherapy
Learn moreTake a second to listen to other heroes, and how we've leveraged their work.
This is a high-end framework to register and analyze multimodal medical image information. Hero allows us to assess potential new image diagnostic tools in a professional environment where both the simplicity of an idea and the complexity of its implementation needs to be considered.
Eirik Malinen 🇳🇴
Professor in medical physics, University of Oslo, Oslo, Norway
Using Hero for data evaluation is highly appealing to students and researchers, as it involves little start-up difficulties. The graphical way of programming the analysis comes naturally.
Peter Kuess 🇦🇹
Phd, Department of Radiotherapy, Medical University of Vienna
Previously we either used a number of radiotherapy applications to piece together the required approach, or utilised other, less intuitive proprietary programming environments to perform image analysis. Hero Imaging provides a single interface which, so far, has solved all our image and data analysis requirements.
Hazel McCallum 🇬🇧
Consultant Clinical Scientist, Northern Centre for Cancer Care, Newcastle upon Tyne, UK