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arXiv · 2002.09809

Random Bundle: Brain Metastases Segmentation Ensembling through Annotation Randomization

Darvin Yi·Endre Grøvik·Michael Iv·Elizabeth Tong·Greg Zaharchuk·Daniel Rubin
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Abstract

We introduce a novel ensembling method, Random Bundle (RB), that improves performance for brain metastases segmentation. We create our ensemble by training each network on our dataset with 50% of our annotated lesions censored out. We also apply a lopsided bootstrap loss to recover performance after inducing an in silico 50% false negative rate and make our networks more sensitive. We improve our network detection of lesions's mAP value by 39% and more than triple the sensitivity at 80% precision. We also show slight improvements in segmentation quality through DICE score. Further, RB ensembling improves performance over baseline by a larger margin than a variety of popular ensembling strategies. Finally, we show that RB ensembling is computationally efficient by comparing its performance to a single network when both systems are constrained to have the same compute.

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Darvin Yi, Endre Grøvik, Michael Iv, Elizabeth Tong, Greg Zaharchuk, Daniel Rubin. 2020-02-23. Random Bundle: Brain Metastases Segmentation Ensembling through Annotation Randomization. https://arxiv.org/abs/2002.09809

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