S-CycleGAN: Semantic Segmentation Enhanced CT-Ultrasound Image-to-Image Translation for Robotic Ultrasonography
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910574258946048 |
|---|---|
| author | Song, Yuhan Chong, Nak Young |
| author_facet | Song, Yuhan Chong, Nak Young |
| contents | 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-us-i2i-translation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_01191 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | S-CycleGAN: Semantic Segmentation Enhanced CT-Ultrasound Image-to-Image Translation for Robotic Ultrasonography Song, Yuhan Chong, Nak Young Image and Video Processing Computer Vision and Pattern Recognition Machine Learning 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-us-i2i-translation. |
| title | S-CycleGAN: Semantic Segmentation Enhanced CT-Ultrasound Image-to-Image Translation for Robotic Ultrasonography |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2406.01191 |