Image Segmentation Using Hybrid Representations

• URL: http://arxiv.org/abs/2004.07071v1
• Date: Wed, 15 Apr 2020 13:07:35 GMT
• Title: Image Segmentation Using Hybrid Representations
• Authors: Alakh Desai, Ruchi Chauhan, Jayanthi Sivaswamy
• Abstract summary: We introduce an end-to-end U-Net based network called DU-Net for medical image segmentation. SC are translation invariant and Lipschitz continuous to deformations which help DU-Net outperform other conventional CNN counterparts. The proposed method shows remarkable improvement over the basic U-Net with performance competitive to state-of-the-art methods.
• Score: 2.414172101538764
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work explores a hybrid approach to segmentation as an alternative to a purely data-driven approach. We introduce an end-to-end U-Net based network called DU-Net, which uses additional frequency preserving features, namely the Scattering Coefficients (SC), for medical image segmentation. SC are translation invariant and Lipschitz continuous to deformations which help DU-Net outperform other conventional CNN counterparts on four datasets and two segmentation tasks: Optic Disc and Optic Cup in color fundus images and fetal Head in ultrasound images. The proposed method shows remarkable improvement over the basic U-Net with performance competitive to state-of-the-art methods. The results indicate that it is possible to use a lighter network trained with fewer images (without any augmentation) to attain good segmentation results.

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