Tumor Lesion Segmentation from 3D PET Using a Machine Learning Driven Active Surface
One of the key challenges facing wider adoption of positron emission tomography (PET) as an imaging biomarker of disease is the development of reproducible quantitative image interpretation tools. Quantifying changes in tumor tissue, due to disease progression or treatment regimen, often requires accurate and reproducible delineation of lesions. Lesion segmentation is necessary for measuring tumor proliferation/shrinkage and radiotracer-uptake to quantify tumor metabolism. In this paper, we develop a fully automatic method for lesion delineation, which does not require user-initialization or parameter-tweaking, to segment novel PET images. To achieve this, we train a machine learning system on anatomically and physiologically meaningful imaging cues, to distinguish normal organ activity from tumorous lesion activity. The inferred lesion likelihoods are then used to guide a convex segmentation model, guaranteeing reproducible results. We evaluate our approach on datasets from The Cancer Imaging Archive trained on data from the Quantitative Imaging Network challenge that were delineated by multiple users. Our method not only produces more accurate segmentation than state-of-the art segmentation results, but does so without any user interaction.
Funding provided by the Canadian Institutes of Health Research (OQI-137993).
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