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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/19675

Title: S-CycleGAN: Semantic Segmentation Enhanced CT-Ultrasound Image-to-Image Translation for Robotic Ultrasonography
Authors: Song, Yuhan
Chong, Nak Young
Issue Date: 2024-11-20
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Magazine name: 2024 IEEE International Conference on Cyborg and Bionic Systems (CBS)
Start page: 115
End page: 120
DOI: 10.1109/CBS61689.2024.10860598
Abstract: Ultrasound imaging is pivotal in various medical diagnoses due to its non-invasive nature and safety. In clinical practice, the accuracy and precision of ultrasound image analysis are critical. Recent advancements in deep learning are showing great capacity of processing medical images. However, the data hungry nature of deep learning and the shortage of high-quality ultrasound image training data suppress the development of deep learning based ultrasound analysis methods. To address these challenges, we introduce an advanced deep learning model, dubbed S-CycleGAN, which generates high-quality synthetic ultrasound images from computed tomography (CT) data. This model incorporates semantic discriminators within a CycleGAN framework to ensure that critical anatomical details are preserved during the style transfer process. The synthetic images are utilized to enhance various aspects of our development of the robot-assisted ultrasound scanning system. The data and code will be available at https://github.com/yhsong98/ct-usi2i-translation.
Rights: This is the author's version of the work. Copyright (C) 2024 IEEE. 2024 IEEE International Conference on Cyborg and Bionic Systems (CBS), Nagoya, Japan, pp. 115-120. DOI: https://doi.org/10.1109/CBS61689.2024.10860598. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
URI: http://hdl.handle.net/10119/19675
Material Type: author
Appears in Collections:b11-1. 会議発表論文・発表資料 (Conference Papers)

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