# Synthesize and Segment: Towards Improved Catheter Segmentation via Adversarial Augmentation
## 1 Introduction
However, artifacts are noticeable in the generated images and the method fails to represent fine details that are related to the structure of the arteries. Additionally, precise parameter tuning is required for effective generation. More recently, [PERSON] et al. [16] designed a framework for data augmentation that adjusts a set of X-ray images with arbitrary intensity distribution to match the specific intensity distribution of chest X-ray images via GANs. Although impressive, GANs are known to be difficult to train and often require careful optimization of the adversarial objectives for stability. In this work, we investigate and evaluate a deep learning based method for catheter synthesis and segmentation. In particular, we focus on the translation of in-painted catheter X-ray images to realistic X-ray images via the CycleGAN architecture as a form of augmentation for improved segmentation in limited settings. Aside from the standard cycle loss, we incorporate: (i) a perceptual loss to generate high-quality synthetic X-ray images and (ii) include a similarity loss to avoid large deformations from the original distribution of the real X-ray image as a constraint. A thorough evaluation of the generated images using state-of-the-art segmentation models demonstrates that the proposed method can indeed improve catheter segmentation performance. The main contributions of this work are highlighted below:  
* Synthetic X-ray from labels: we propose to generate synthetic X-ray from in-painted catheter masks via adversarial learning with CycleGAN as data augmentation for segmentation. * Improved generation with perceptual losses: to achieve more realistic generation from in-painted catheter masks, we incorporate a perceptual loss alongside the standard cycle loss. * Enforcing semantic similarity: we further propose a similarity loss to alleviate large deviations in the semantic quality of the generated images from the original. * Empirical results and several ablations show the effectiveness of the proposed training scheme with segmentation performance improving as synthetic augmentation is increased. The presented work is an extended version of the study presented at a conference [17]. Herein, (i) the proposed CycleGAN X-ray translation method is further improved by using catheter masks in-painted in X-ray images with no real catheter present to mitigate the problem of generated images showing a progressively vanished catheter as training proceeds, (ii) catheter segmentation in fluoroscopic X-ray images is further explored based on the generated images, (iii) thorough quantitative and qualitative results are reported to validate the proposed methods, and (iv) several ablation studies are presented in order to assess the performance of catheter segmentation with the generated data. The rest of the paper is organized, as follows. First, we revisit prior works regarding catheter segmentation, detection, and translation in Section 2. We present our proposed method in Section 3, and show the evaluation results regarding catheter synthesis and segmentation in Section 4. We conclude in Section 6.