Implicit U-Net for volumetric medical image segmentation

• URL: http://arxiv.org/abs/2206.15217v1
• Date: Thu, 30 Jun 2022 12:00:40 GMT
• Title: Implicit U-Net for volumetric medical image segmentation
• Authors: Sergio Naval Marimont and Giacomo Tarroni
• Abstract summary: Implicit U-Net adapts the efficient Implicit Representation paradigm to supervised image segmentation tasks. Our implicit U-Net has 40% less parameters than the equivalent U-Net. When comparing to an equivalent fully convolutional U-Net, Implicit U-Net reduces by approximately 30% inference and training time.
• Score: 0.6294759639481189
• License: http://creativecommons.org/licenses/by/4.0/
• 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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