Plug and play: nnUNet for the Auto-Segmentation of Head & Neck Tumors and Lymph Nodes on 3D FDG PET/CT Scans

Abstract

CNNs have immense potential for transforming conventional image processing methods in medical imaging. They are gaining popularity among radiologists and physicians for specializing in processing image data but the development of such intelligent systems for use in the healthcare industry in general, is bottlenecked due to challenges that include limited data availability and data privacy laws like Health Insurance Portability and Accountability Act (HIPAA) in the US. We propose the use of self-configuring pipelines like nnUNet which can empower healthcare professionals like physicians and technologists to train and deploy models locally, without expert domain knowledge in Machine Learning and with no need to share confidential patient data. This research work exploits and evaluates a self-optimizing deep learning framework, nnUNet for the automated semantic segmentation of tumors and lymph nodes on 3D FDG PET/CT images of Head and Neck. nnUNet is an adaptive segmentation method that examines the provided training examples and customizes a UNet-based segmentation pipeline, tailored to the requirements of the given dataset. Since manual delineation of cancerous lesions is time consuming and error prone, automated segmentation can flag concerning areas on the images, allowing radiation oncologists to optimize the treatment plan in a shorter time and with increased reliability. Our method achieved an aggregated dice score of 0.77 for GTVp and 0.67 for GTVn (0.72 on average) using nnUNet, which unveiled comparable performance to the best performing models for Head and Neck tumor segmentation. By automating common tasks like data preprocessing, network architecture selection and hyperparameter optimization, self-configuring methods can enhance the adoption of AI in the healthcare industry by making healthcare professionals embrace the latest AI technologies, bridging the gap between AI industry and healthcare.