Project and Excite

Title: Project and Excite Modules for Segmentation of Volumetric Medical Scans

Type: Master Thesis

Student: Anne-Marie Rickmann

Supervisor: Abhijit Guha Roy, Ignacio Sarasua, Nassir Navab, Christian Wachinger

Status: Finished, 15.04.2019



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.


MICCAI paper

Download Thesis (pdf)