Abstract
Fully Convolutional Neural Networks (F-CNNs) achieve state-of-the-art performance for image segmentation in medical imaging. Recently, squeeze and excitation (SE) modules and variations thereof have been introduced to recalibrate feature maps channel- and spatial-wise, which can boost performance while only minimally increasing model complexity. So far, the development of SE has focused on 2D images. In this paper, we propose ‘Project & Excite’ (PE) modules that base upon the ideas of SE and extend them to operating on 3D volumetric images. ‘Project & Excite’ does not perform global average pooling, but squeezes feature maps along different slices of a tensor separately to retain more spatial information that is subsequently used in the excitation step. We demonstrate that PE modules can be easily integrated in 3D U-Net, boosting performance by \(5\%\) Dice points, while only increasing the model complexity by \(2\%\). We evaluate the PE module on two challenging tasks, whole-brain segmentation of MRI scans and whole-body segmentation of CT scans. Code: https://github.com/ai-med/squeeze_and_excitation.
A.-M. Rickmann, A.G. Roy and I. Sarasua—Have contributed equally.
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Acknowledgements
This research was partially supported by the Bavarian State Ministry of Science and the Arts in the framework of the Centre Digitisation.Bavaria (ZD.B). We thank NVIDIA corporation for GPU donation.
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Rickmann, AM., Roy, A.G., Sarasua, I., Navab, N., Wachinger, C. (2019). ‘Project & Excite’ Modules for Segmentation of Volumetric Medical Scans. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11765. Springer, Cham. https://doi.org/10.1007/978-3-030-32245-8_5
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DOI: https://doi.org/10.1007/978-3-030-32245-8_5
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