Imagine you're part of a large life sciences organization. Your researchers are running complex clinical trials, and your radiologists capture thousands of CT, MRI, and PET scans monthly. Each scan generates hundreds of megabytes, often gigabytes, of DICOM data. And now, you've been asked to process and analyze all of it, integrate it with genomics and EHR data, and generate patient-level insights. The trouble? Your infrastructure is already maxed out, your analysts are buried under manual processes, and your time-to-insight is slowing down critical trials.
That's precisely the challenge one of our clients faced. A global medical research center struggled with the growing volume of medical imaging data. Their on-premises infrastructure couldn't keep up, radiomics pipelines were delayed, and researchers couldn't access harmonized data fast enough to guide trial decisions.
We'll explain how Mactores helped this client implement a scalable medical imaging data pipeline using Amazon EMR and how you can do the same to accelerate your imaging workflows, improve operational efficiency, and enable AI/ML-driven insights at scale.
Medical imaging is no longer limited to diagnosis. In life sciences, it plays a pivotal role in drug development, personalized medicine, and real-time monitoring. Modern trials often utilize imaging biomarkers to detect disease progression or therapeutic response before any clinical symptoms appear.
Technologies like radiomics allow you to extract hundreds of quantitative features from a single image, revealing patterns not visible to the naked eye. These features can be used to identify predictive biomarkers, segment patient cohorts, and train machine learning models to support treatment recommendations.
But here's the catch. These images are large, complex, and unstructured. A single oncology trial might produce petabytes of DICOM files. Without a scalable, automated data pipeline, this data becomes a bottleneck instead of a breakthrough.
Medical imaging is now one of the wealthiest data sources in life sciences. From MRI scans capturing intricate brain structures to PET scans revealing metabolic changes in real-time, the insights hidden within these images transform diagnostics, drug discovery, and treatment strategies. But here's the reality: generating the images is just the beginning. What moves the needle is the ability to efficiently ingest, process, analyze, and integrate this data with other sources like clinical records, genomic profiles, or lab results.
As organizations scale their research or clinical operations, imaging data volumes grow exponentially. With that growth comes the need for pipelines that aren't just fast but also resilient, secure, and intelligent, that reduce human effort, eliminate delays, and adapt dynamically to changing workloads, in other words, pipelines that can scale as fast as science.
Let's take a closer look at why scalable pipelines aren't just a nice-to-have, but a necessity in modern medical imaging workflows:
Amazon EMR (Elastic MapReduce) is a cloud-native platform designed to run big data workloads like Apache Spark, Hadoop, Hive, and Presto—all fully managed and auto-scaled.
Here's how Amazon EMR helps life sciences organizations handle imaging data:
Amazon EMR can automatically ingest DICOM files from on-premises PACS systems or Amazon S3. Spark jobs can convert DICOM to more query-efficient formats like Parquet, extract relevant metadata, and perform image normalization or de-identification.
With Amazon EMR, you can run distributed image processing algorithms across a cluster, which is ideal for batch-processing radiomics features, applying ML models, or training AI models at scale.
Amazon S3 can be a persistent store for raw and processed image data. EMR clusters can be spun up and down as needed, so you only pay for compute when actively running jobs.
Processed data from EMR can feed directly into AWS HealthImaging, Amazon SageMaker for ML, or Amazon Redshift for analytics. You get a fully integrated pipeline with end-to-end traceability and security.
To better illustrate how scalable imaging pipelines can be implemented in real-world scenarios, let's look at a recent engagement in which Mactores partnered with a global life sciences research center. This organization grappled with massive medical imaging data from ongoing clinical trials. Their internal systems couldn't keep up with the pace of research, and delays in image processing were directly impacting critical timelines.
By leveraging Amazon EMR, Mactores helped the client transform their imaging workflow into a scalable, automated, and cloud-native pipeline that could handle petabyte-scale data, meet compliance standards, and drastically reduce time-to-insight. Here's how we did it:
A global life sciences research center conducted large-scale oncology trials and captured thousands of high-resolution medical images weekly. These included CT, MRI, and PET scans, all stored in DICOM format. The volume of data was growing rapidly, and their existing infrastructure could no longer support the pace of analysis. Researchers were experiencing delays in accessing harmonized datasets for modeling and decision-making.
Mactores designed a cloud-native pipeline using Amazon EMR and Apache Spark. The pipeline automatically ingested DICOM files from Amazon S3, extracted key metadata, and converted the images into Parquet format for faster querying.
We implemented automated de-identification for compliance, distributed preprocessing to normalize image contrast and resolution, and parallelized feature extraction using Spark. The output was stored in Amazon S3 and indexed in Amazon Athena for downstream analytics. Additionally, we enabled downstream integration with Amazon SageMaker for AI/ML model training.
The client accelerated their radiomics analysis workflow, enabling researchers to access processed datasets in near real-time. Time-to-insight for clinical trials improved significantly, directly contributing to faster iteration in drug development.
Medical imaging holds the key to unlocking faster, more precise clinical insights, but only if you have the infrastructure to process and analyze it. That's where Amazon EMR steps in.
By building scalable data pipelines with EMR, life sciences organizations can automate ingestion, transform images at scale, integrate with AI/ML models, and keep all data secure and compliant. Whether conducting cancer research, analyzing population health trends, or optimizing treatment plans, cloud-native solutions like EMR are your foundation for innovation.
If you're ready to bring automation, scalability, and intelligence to your medical imaging workflows, Mactores can help you build a future-ready data platform on AWS.