Biomedical Imaging

Speed

Current imaging techniques tend to be slow. This may cause discomfort and radiation risk for patients under examination. It can delay throughput and image interpretation for both research and clinical applications.

Quantification

The lack of quantitative imaging information is a problem for interpretation and also limits our abilities to develop algorithms and tools that automate interpretation.

Cost

Biomedical imaging instruments can be expensive, using specialized hardware and infrastructure. Along with their low throughput, these factors add to the cost of biomedical imaging.

Connected Imaging Instrument

Microsoft’s Connected Imaging Instrument project is focused on developing technologies that will allow imaging instruments to leverage the cloud for on-demand processing and scalable storage. We believe the pivot to cloud computing for medical image signal processing will enable faster deployment of new techniques that will improve the patient experience and expand access to advanced diagnostics.

Machine Learning for Image Reconstruction

Microsoft is developing machine learning based techniques that can reconstruct medical images from raw datasets (opens in new tab) that are incomplete (undersampled) or corrupted by artifacts. We believe that machine learning will play a critical role in accelerating medical image acquisitions and improving the patient experience, and are particularly interested in how well machine learning based image reconstruction does in reconstruction of clinically relevant details and lesions. We have curated annotations for clinically relevant lesions (opens in new tab) and are using such annotations to optimize reconstructions and workflow.

Radiotherapy image segmentation

Project InnerEye (opens in new tab) is research conducted in the Health Intelligence team at Microsoft Research Cambridge (UK) (opens in new tab) that is exploring how machine learning (ML) has the potential to assist clinicians in planning their radiotherapy (RT) treatments so that they can spend more time with their patients. Our recent peer-reviewed research published in JAMA Network Open (opens in new tab) shows that clinicians using ML assistance can segment images up to 13 times faster than doing it manually, with an accuracy that is within the bounds of human expert variability. Cambridge University Hospitals NHS Foundation Trust is working with University Hospitals Birmingham NHS Foundation Trust on an NHSX AI Award project (opens in new tab) to deploy their own radiotherapy image segmentation ML models using Project InnerEye OSS and Microsoft Azure to help cut the time patients wait for their cancer treatment.

Opthalmology

As part of Microsoft’s work with Novartis, data scientists from Microsoft Research and research teams from Novartis have worked together to investigate how AI can help unlock transformational new approaches in several areas. The Health Intelligence team at Microsoft Research Cambridge (UK) has worked with Novartis to look at how machine learning might be applied for personalized treatment for macular degeneration – a leading cause of irreversible blindness.

Digital Pathology

There are key opportunities to improve patient diagnosis and care by augmenting digital pathology with AI. Looking at the challenge of shortage of medical expertise in medical image analysis in developing countries, researchers at MSR Asia have worked with partners to explore solutions to assist doctors to make diagnosis with computer vision, including a collaboration with Pfizer.

Our MSR New England Biomedical ML team (opens in new tab) has partnered and Volsatra Therapeutics (opens in new tab) to develop tools that help detect drivers of cancer metastasis. We will develop automated machine learning tools capable of rapidly and accurately integrating insights across multiple datasets, including pathology slides and three-dimensional tumor-derived organoids.

Pandemic Preparedness

The Project InnerEye team is supporting two projects as part of Microsoft’s Studies in Pandemic Preparedness research program. University Hospitals Birmingham NHS Foundation Trust is using Project InnerEye OSS to develop ML models to support clinicians to diagnose and assess the severity of Covid-19 using AI and Chest X-rays with a human-interpretable class hierarchy that aligns with the radiological decision process. The team used data-driven error analysis to uncover blind spots of the model, providing further transparency on its clinical utility.

Researchers at University College London (UCL) and UCL Hospitals NHS Foundation Trust (opens in new tab) are also using Project InnerEye OSS and Azure Machine Learning to see if chest imaging data can identify better who should shield in potential future COVID-19 outbreaks. This project uses the UK National COVID-19 Chest Imaging Database (NCCID), led by Dr Joe Jacob at UCL (opens in new tab).