DV. Target

by Deepvoxel Inc.  · Based in United States → — AI-powered auto-contouring for precise radiation therapy planning.
Oncology Radiology
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Regulatory Status Disclosed

Overview

DV. Target is a standalone software application developed by Deepvoxel Inc. It is designed to enable the routing of DICOM-compliant data, specifically CT images, to automatic image processing workflows. Utilizing machine learning-based algorithms, DV. Target automatically delineates organs-at-risk (OARs) on these CT images. The contours generated by DV. Target are intended to be used as an input to clinical workflows for treatment planning in radiation therapy.

The system supports automatic delineation of approximately 50 major OARs across four body parts: head-and-neck, thorax, abdomen, and pelvis. This includes both OARs of virtual clinical value and challenging, small OARs such as the optic chiasm. DV. Target goes beyond traditional atlas-based OARs contouring solutions by leveraging state-of-the-art AI technology and extensive big data collected from global medical institutions to deliver rapid and accurate OARs segmentation predictions.

It is intended for use by trained medical professionals, including radiologists, radiation oncologists, dosimetrists, and physicists. It is specifically for normal organ contouring and not for tumor or clinical target volume contouring. The software requires use in conjunction with appropriate software, such as a treatment planning system (TPS), to review, edit, and approve all generated contours.

Reviewed by Pouyan Golshani, MD — Interventional Radiologist

Key Features

  • Automatic delineation of Organs-at-Risk (OARs) on CT images
  • Utilizes machine learning-based algorithms
  • DICOM-compliant data input and output (CT Images, RTSTRUCT)
  • Supports Head & Neck, Thorax, Abdomen & Pelvis anatomical sites
  • Facilitates radiation therapy workflows
  • Intended for normal organ contouring (not tumor or clinical target volume contouring)
  • Requires review, edit, and approval of contours by trained medical professionals
  • Beyond traditional atlas-based OARs contouring solutions
  • Integration with medical data acquisition flow and TPS system
  • File organization system and built-in delineation editor

Use Cases

  • Radiation therapy planning
  • Automated organ-at-risk (OAR) contouring
  • Enhancing accuracy and efficiency of radiation therapy planning
  • Input for clinical workflows in treatment planning

What Physicians Need to Know

Key Capabilities
Automated contouring of approximately 50 major Organs-at-Risk (OARs) on CT images across head-and-neck, thorax, abdomen, and pelvic regions, including challenging and small OARs like the optic chiasm. Utilizes innovative deep learning algorithms for superior performance in time saving and contouring accuracy, moving beyond traditional atlas-based solutions.
Clinical Utility
Significantly enhances the accuracy and efficiency of radiation therapy planning by providing rapid and precise OAR delineation. Improves diagnostic accuracy and overall efficiency for clinicians in treatment planning, addressing the critical need for instantaneous and precise localization of OARs in radiotherapy.
Integration Options
Designed for easy interaction with existing medical data acquisition flows and Treatment Planning Systems (TPS). Supports processing of DICOM-compliant image data and includes a built-in delineation editor and file organization system for a streamlined workflow.
Compliance Status
HuraRT (DV.Target) is an FDA-approved and FDA-cleared deep learning product, indicating adherence to regulatory standards for medical devices.
Pricing Model
Specific pricing for DV.Target is not publicly detailed on the Hura Imaging website. General AI in medical imaging often utilizes subscription or fee-per-study models.
User Experience
Offers a clean and powerful user experience through its file organization system and built-in delineation editor. The tool is intended for use by trained and qualified clinical personnel, aiming to improve their efficiency in treatment planning.
Implementation Complexity
Designed for straightforward implementation, facilitating interaction with existing medical data workflows and Treatment Planning Systems (TPS). Its reliance on DICOM standards for data processing further simplifies integration into current radiology and radiation oncology environments.
Evidence Base
Built upon years of original deep learning research and supported by multiple peer-reviewed publications in prominent journals like Nature Machine Intelligence and Medical Physics. The underlying technologies have also received funding from NIH STTR/SBIR grants.
Physician Tip

Leverage DV.Target for automated OAR contouring to significantly reduce manual contouring time and improve consistency in radiation therapy planning. Utilize its ability to delineate small and challenging OARs for more precise treatment planning, potentially leading to reduced toxicity. Integrate the tool seamlessly with existing TPS and DICOM workflows to optimize efficiency. Review the strong evidence base, including peer-reviewed publications and FDA clearance, to understand the scientific rigor behind the AI algorithms.

DV.Target is designed for straightforward integration with standard medical data acquisition flows and Treatment Planning Systems (TPS), supporting DICOM-compliant image data. This facilitates its incorporation into existing radiology and radiation oncology workflows, enhancing efficiency without major overhauls.

Details

Category Oncology AI, Radiology & Imaging AI
Pricing Unknown
DeploymentLocal network with existing hospital-grade IT system, installed on a specialized server supporting deep learning processing. Users access via browsers.
Compliance
BAA AvailableUnknown AI-estimated
HIPAA CompliantUnknown AI-estimated
FDA Status 1 AI-estimated

DV. Target received FDA 510(k) clearance (K202928) on April 2, 2021, as a software application for automatic delineation of organs-at-risk (OARs) on CT images for radiation therapy planning.
Integrations
EHR Not specified
Specialties Oncology, Radiology

What the Web Says

DV.Target by Hura Imaging is an AI-powered auto-segmentation software primarily used in radiation therapy for contouring organs at risk (OARs) and target volumes. It is FDA cleared and CE marked, indicating its compliance with medical device regulations. The software aims to streamline the delineation process, which is traditionally time-consuming for radiation oncologists and dosimetrists.

Overall: Mixed

Strengths

  • FDA cleared and CE marked
  • Automated segmentation of organs at risk and target volumes
  • Aids in streamlining the delineation process in radiation therapy
  • Potentially reduces workload for RTTs and doctors

Limitations

  • Segmentation networks require maintenance
  • Trained networks may not be robust to changes in acquisition settings
  • RTTs and doctors are still needed to review and correct segmentations
  • Limited public reviews available from physicians, healthcare IT, tech reviewers, Reddit, G2, or Capterra.

Based on reviews from: Catharina Ziekenhuis

Last updated: 2026-07-18

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Press & Coverage

Hura Imaging
Hura Imaging | Solutions for Medical Imaging Processing Power By AI
Hura Imaging highlights DV.Target (HuraRT) as a top FDA-approved deep learning product for auto-contouring organs-at-risk (OARs) in radiation therapy planning, emphasizing its superior performance in time-saving and contouring results. The company's timeline shows FDA clearance for HuraRT (DV.Target) in November 2022.
2022-11
Hura Imaging
Home: DV.Target | Hura Imaging
This page details HuraRT (DV.Target) as a full-fledged software utilizing innovative deep learning algorithms for automatic segmentation of approximately 50 major OARs across various body parts, significantly enhancing the accuracy and efficiency of radiation therapy planning. It also lists several peer-reviewed publications supporting the technology.
unknown
Orthogonal
SaMD Cleared by the FDA: The Ultimate Running List - Orthogonal
Orthogonal's comprehensive list of FDA-cleared Software as a Medical Devices (SaMD) includes DV.Target, cleared on April 2, 2021, for radiology applications. This list compiles information from publicly available FDA reports and regulatory search tools.
2021-04
Catharina Ziekenhuis
Klinische implementatie van AI - Catharina Ziekenhuis
A presentation from Catharina Ziekenhuis on the clinical implementation of AI in radiotherapy lists Hura Imaging's DV.Target as an FDA-approved commercial AI application for segmentation across various anatomical locations including head and neck, thorax, abdomen, and pelvis.
2024-03

Videos

Product demos, reviews, and walkthroughs for DV. Target.

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Frequently Asked Questions

DV. Target, depending on its specific application, can integrate with medical data acquisition flows and treatment planning systems (TPS) to streamline processes. For instance, HuraRT (DV.Target) is designed for auto-contouring organs-at-risk (OARs) on CT images, significantly enhancing the accuracy and efficiency of radiation therapy planning. Other applications, like the Discovery Viewer (DV) platform, provide an environment for developing and deploying medical AI models for tasks such as segmentation, regression, and classification, which can then be integrated into clinical settings.
While specific compliance details for 'DV. Target' would depend on the particular product, any healthcare AI platform handling Protected Health Information (PHI) must adhere to stringent regulations like HIPAA in the US and GDPR in Europe. Platforms like the Discovery Viewer (DV) address PHI concerns by facilitating the sharing of pre-trained models developed using private datasets, rather than direct sharing of raw patient data. This approach helps mitigate the risk of PHI breaches by only providing learned weights that encapsulate relevant data knowledge in terms of predictions.
Alternatives to DV. Target would vary based on its specific function, ranging from traditional manual methods to other AI-powered solutions for tasks like image segmentation or risk stratification. For OAR contouring, HuraRT (DV.Target) differentiates itself by going beyond traditional atlas-based solutions, utilizing state-of-the-art AI technology and big data for rapid and accurate predictions. For AI model development, platforms like the Discovery Viewer (DV) aim to democratize access to AI model creation and deployment, offering an adaptable environment for testing and fine-tuning models.
Specific pricing models for 'DV. Target' are not detailed in the provided information, but healthcare AI solutions generally offer various pricing structures. These can include subscription-based models, per-use fees, or tiered licensing based on the size of the institution or the scope of deployment. It is common for vendors to offer different options tailored to individual practitioners, clinics, or large hospital systems to accommodate varying needs and budgets.
Like all AI systems, DV. Target's algorithms may have limitations, including potential biases stemming from the data used for training, which could affect performance across diverse patient populations. For example, AI applied to ECGs for structural heart disease screening can have high false positive rates, necessitating targeted deployment strategies. Physicians should always use clinical judgment to interpret AI recommendations, especially in complex or atypical cases, and understand the specific contexts and populations for which the AI was validated.
The extent of independent clinical validation and reported accuracy for 'DV. Target' depends on the specific application. For instance, the Discovery Viewer (DV) platform has shown promising results in developing models, with a classification model achieving 0.94 accuracy and a segmentation model attaining a Dice Score of 0.93 in specific projects. Similarly, TARGET-AI, an AI-enabled pipeline for SHD screening, demonstrated significant increases in F1 scores and reductions in false positives compared to untargeted screening. It is crucial for physicians to review the validation studies pertinent to the specific DV. Target application they are considering.
DV. Target solutions typically require various types of medical data for optimal performance, such as CT images for OAR contouring or longitudinal electronic health record (EHR) data combined with ECG images for risk stratification. Platforms like the Discovery Viewer (DV) are designed to integrate multiple databases with different medical data types, including clinical imaging and electronic health records. Secure data handling is paramount, often involving anonymization of medical data and web-based, zero-footprint access to protect patient information during processing and model training.

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Suggest an Edit → | Last Verified: 2026-04-18 | First Added: 2026-04-18
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