  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
