Implicit U-Net for volumetric medical image segmentation

Sergio Naval Marimont; Giacomo Tarroni

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arXiv · 2206.15217

Implicit U-Net for volumetric medical image segmentation

Sergio Naval Marimont·Giacomo Tarroni

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Abstract

U-Net has been the go-to architecture for medical image segmentation tasks, however computational challenges arise when extending the U-Net architecture to 3D images. We propose the Implicit U-Net architecture that adapts the efficient Implicit Representation paradigm to supervised image segmentation tasks. By combining a convolutional feature extractor with an implicit localization network, our implicit U-Net has 40% less parameters than the equivalent U-Net. Moreover, we propose training and inference procedures to capitalize sparse predictions. When comparing to an equivalent fully convolutional U-Net, Implicit U-Net reduces by approximately 30% inference and training time as well as training memory footprint while achieving comparable results in our experiments with two different abdominal CT scan datasets.

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Sergio Naval Marimont, Giacomo Tarroni. 2022-06-30. Implicit U-Net for volumetric medical image segmentation. https://arxiv.org/abs/2206.15217

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