Project InnerEye: Augmenting cancer radiotherapy workflows with deep learning and open source

Medical images offer vast opportunities to improve clinical workflows and outcomes. Specifically, in the context of cancer radiotherapy, clinicians need to go through computer tomography (CT) scans and manually segment (contour) anatomical structures. This is an extremely time-consuming task that puts a large burden on care providers. Deep learning (DL) models can help with these segmentation tasks. However, more understanding is needed regarding these models’ clinical utility, generalizability, and safety in existing workflows. Building these models also requires techniques that are not easily accessible to researchers and care providers.

In this webinar, Dr. Ozan Oktay and Dr. Anton Schwaighofer will analyze these challenges within the context of image-guided radiotherapy procedures and will present the latest research outputs of Project InnerEye in tackling these challenges. The first part of the webinar will focus on a research study that evaluates the potential clinical impact of DL models within the context of radiotherapy planning procedures. The discussion will also include the performance analysis of state-of-the-art DL models on datasets from different hospitals and cancer types, and we’ll explore how they compare with manual contours annotated by three clinical experts.

The second part of the talk will introduce the open-source InnerEye Deep Learning Toolkit and how it can provide tools to help enable users to build state-of-the-art medical image segmentation models in Microsoft Azure. There will be examples illustrating step-by-step how the toolkit can be used in different segmentation applications within Azure Machine Learning (Azure ML) infrastructure. This includes model specification, training run analysis, performance reporting, and model comparison.

In this webinar, we will explore:

The performance of DL segmentation models across images from multiple clinical sites and different radiotherapy domains, and how it compares with levels of inter-expert variability in radiotherapy contouring tasks.
The potential clinical impact of such models in terms of time savings, by augmenting existing radiotherapy dose planning procedures.
The features of the InnerEye Deep Learning Toolkit and how it can be used by developers to aid in building their own in-house medical image segmentation models from scratch.
Exploring the potential benefits of Azure ML cloud integration and model development process in Azure ML.

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