Abstract
Simultaneous localisation and categorization of objects in medical images, also referred to as medical object detection, is of high clinical relevance because diagnostic decisions often depend on rating of objects rather than e.g. pixels. For this task, the cumbersome and iterative process of method configuration constitutes a major research bottleneck. Recently, nnU-Net has tackled this challenge for the task of image segmentation with great success. Following nnU-Net’s agenda, in this work we systematize and automate the configuration process for medical object detection. The resulting self-configuring method, nnDetection, adapts itself without any manual intervention to arbitrary medical detection problems while achieving results en par with or superior to the state-of-the-art. We demonstrate the effectiveness of nnDetection on two public benchmarks, ADAM and LUNA16, and propose 11 further medical object detection tasks on public data sets for comprehensive method evaluation. Code is at https://github.com/MIC-DKFZ/nnDetection.
M. Baumgartner and P. F. Jäger—Equal contribution.
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Acknowledgements
Part of this work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – 410981386 and the Helmholtz Imaging Platform (HIP), a platform of the Helmholtz Incubator on Information and Data Science.
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Baumgartner, M., Jäger, P.F., Isensee, F., Maier-Hein, K.H. (2021). nnDetection: A Self-configuring Method for Medical Object Detection. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12905. Springer, Cham. https://doi.org/10.1007/978-3-030-87240-3_51
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