S-CycleGAN: Semantic Segmentation Enhanced CT-Ultrasound Image-to-Image Translation for Robotic Ultrasonography

TL;DR

This paper introduces S-CycleGAN, a novel deep learning model that generates high-quality synthetic ultrasound images from CT data, enhancing robotic ultrasonography by preserving anatomical details during image translation.

Contribution

The paper presents S-CycleGAN, which integrates semantic discriminators into CycleGAN to improve ultrasound image synthesis from CT scans, addressing data scarcity in medical imaging.

Findings

Generated images retain critical anatomical details.

Enhanced ultrasound image quality from CT data.

Improved performance in robotic ultrasonography applications.

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-us-i2i-translation.