‘Project & Excite’ Modules for Segmentation of Volumetric Medical Scans
Rickmann AM, Roy AG, Sarasua I, Navab N, Wachinger C (2019)
Publication Type: Conference contribution
Publication year: 2019
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 11765 LNCS
Pages Range: 39-47
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Shenzhen, CHN
ISBN: 9783030322441
DOI: 10.1007/978-3-030-32245-8_5
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 (formula presented). 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.
Involved external institutions
Germany (DE)How to cite
APA:
Rickmann, A.-M., Roy, A.G., Sarasua, I., Navab, N., & Wachinger, C. (2019). ‘Project & Excite’ Modules for Segmentation of Volumetric Medical Scans. In Dinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 39-47). Shenzhen, CHN: Springer Science and Business Media Deutschland GmbH.
MLA:
Rickmann, Anne-Marie, et al. "‘Project & Excite’ Modules for Segmentation of Volumetric Medical Scans." Proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, Shenzhen, CHN Ed. Dinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou, Springer Science and Business Media Deutschland GmbH, 2019. 39-47.
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