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Posted on Sep 17

AWS HealthImaging: A Developer Guide

Medical imaging systems generate large volumes of DICOM data that applications need to store, retrieve, process, and display efficiently. Building this infrastructure from scratch can introduce significant storage, integration, and operational complexity.

AWS HealthImaging provides a cloud-native approach for storing, accessing, and managing medical imaging data. The service is designed to store medical images at scale and provides APIs for working with image sets, metadata, and pixel data.

For developers, the important part is understanding how DICOM data moves through HealthImaging and how its APIs fit into an imaging application architecture.

What Is AWS HealthImaging?

AWS HealthImaging is an AWS service for storing, analyzing, and sharing medical images in the cloud. Instead of treating every DICOM file as an isolated object, the service organizes imported imaging data into image sets.

An image set is an AWS concept used to group related medical imaging data. During import, DICOM P10 data is transformed into image sets containing metadata and image frames. HealthImaging attempts to organize this data according to the DICOM Study, Series, and Instance hierarchy.

This model gives developers APIs for working with imaging data without having to build every storage and retrieval mechanism themselves.

Understanding the HealthImaging Data Flow

A typical implementation can follow this workflow:

With AWS HealthImaging, developers can create a data store, import DICOM files, search image sets, retrieve metadata, and access image frames through AWS APIs.

During import, HealthImaging performs pixel-data verification and transforms DICOM P10 files into image sets.

Creating a HealthImaging Data Store

The data store is the foundation of an imaging implementation.

Developers can create a data store using the AWS Management Console, AWS CLI, SDKs, or API operations. The CreateDatastore action creates a HealthImaging data store for importing DICOM P10 files.

A data store can also be configured with a lossless storage format. AWS documentation currently identifies High Throughput JPEG 2000 (HTJ2K) as the default storage format for HealthImaging data stores.

The architecture should define:

Data store strategy
AWS Region
Encryption requirements
IAM permissions
Import workflow
Application access patterns
Monitoring requirements

These decisions affect how applications interact with imaging data after deployment.

Importing DICOM Data

Once a data store exists, developers can import DICOM P10 files from an Amazon S3 input bucket.

An import job processes .dcm files and converts them into image sets containing metadata and image frames. Developers can start and monitor import jobs using AWS SDKs, AWS CLI, or the AWS Management Console.

HealthImaging also attempts to organize imported instances according to Study UID, Series UID, and Instance UID.

If imported metadata conflicts with an existing primary image set, HealthImaging can create a non-primary image set instead. This provides a mechanism for handling duplicate or inconsistent imaging data.

Working With Image Sets

Image sets are central to AWS HealthImaging development.

After importing imaging data, developers can search for image sets and retrieve their properties, metadata, and image frames. AWS provides runtime actions such as:

SearchImageSets
GetImageSet
GetImageSetMetadata
GetImageFrame
ListImageSetVersions
UpdateImageSetMetadata
CopyImageSet
DeleteImageSet

These runtime actions are exposed through a separate HealthImaging runtime endpoint.

This separation is useful when designing backend services that need to distinguish data-store management from runtime image access.

Accessing Metadata

Medical imaging applications frequently need metadata before retrieving pixel data.

The GetImageSetMetadata operation allows applications to retrieve metadata associated with an image set. HealthImaging returns the normalized metadata as a compressed JSON object.

A backend service can use this metadata to:

Identify the relevant study.
Determine the available series.
Resolve instance identifiers.
Build viewer requests.
Apply application-level authorization.
Retrieve required image frames.

This approach prevents applications from unnecessarily loading complete imaging objects when metadata is enough for the current workflow.

Using DICOMweb

Healthcare applications often need standards-based interfaces for interoperability. AWS HealthImaging provides representations of DICOMweb APIs for working with imaging data.

Developers can use DICOMweb capabilities for storing, searching, and retrieving DICOM information. AWS provides representations of standards such as STOW-RS, QIDO-RS, and WADO-RS.

For example, QIDO-RS can be used to search studies, series, and instances. WADO-RS supports retrieving DICOM data at series and instance levels.

This can be useful when integrating HealthImaging with existing imaging applications or DICOM-aware viewers.

Building a Medical Imaging Viewer

A typical viewer architecture can place a backend API between the frontend and HealthImaging.

The frontend should not automatically receive unrestricted access to imaging resources.

Instead, the backend can authenticate users, validate access permissions, identify the requested image set, and retrieve the appropriate imaging data.

This architecture also provides a central location for application logging, business rules, caching, and integration with other healthcare systems.

Security and Access Control

Medical imaging data can contain sensitive patient information, so access control should be part of the architecture from the beginning.

An implementation should consider:

AWS Identity and Access Management (IAM)
Least-privilege permissions
Encryption
Secure API access
Audit logging
Data retention
Application-level authorization
Environment separation

When using HTTP requests, HealthImaging API operations use service-specific endpoints. AWS also documents authentication requirements for DICOMweb requests, including OpenID Connect (OIDC) authentication options.

AWS HealthImaging and AI Workflows

Medical imaging data is increasingly used in AI and machine learning workflows.

A potential architecture can connect HealthImaging with an AI pipeline:

The exact architecture depends on the model, regulatory requirements, preprocessing workflow, and how results need to be stored or displayed.

The important engineering principle is to keep image storage, processing, model execution, and application workflows clearly separated.

Handling Metadata Updates

Real-world imaging systems may require metadata corrections, additions, removals, or de-identification workflows.

HealthImaging provides UpdateImageSetMetadata for modifying metadata associated with primary image sets. AWS documents this as an asynchronous operation that can add, update, or remove metadata attributes.

Developers should therefore design applications to handle asynchronous operations and verify the resulting image-set state rather than assuming an update is immediately complete.

Common Development Challenges

Working with AWS HealthImaging still requires careful engineering.

Common challenges include:

Understanding DICOM hierarchy
Mapping existing imaging workflows
Handling primary and non-primary image sets
Managing large imaging datasets
Designing secure viewer access
Supporting DICOMweb integrations
Handling asynchronous import operations
Managing metadata normalization
Controlling cloud costs
Testing interoperability

Legacy applications may also require additional integration layers when they do not directly support HealthImaging APIs or DICOMweb.

A Practical Development Approach

At Oodles, we approach AWS HealthImaging projects by first understanding the imaging workflow and existing infrastructure.

Our development process can include:

Imaging workflow analysis
DICOM data assessment
HealthImaging data-store architecture
Import pipeline development
Backend API development
DICOMweb integration
Medical imaging viewer integration
Security and access-control implementation
Testing and monitoring
Cloud deployment and optimization

This approach helps connect the AWS infrastructure with the actual application workflow rather than treating HealthImaging as an isolated storage service.

Final Thoughts

AWS HealthImaging gives developers a managed foundation for working with medical imaging data in the cloud. Its image-set model, native APIs, DICOMweb capabilities, and integration options can support modern imaging applications.

The key is to design the surrounding architecture carefully. Data ingestion, metadata handling, image retrieval, authorization, viewer integration, and AI workflows should work together as one system.

At Oodles, we help healthcare businesses design and develop cloud-based imaging applications around their existing workflows, integrations, and product requirements.

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