# Synthesize and Segment: Towards Improved Catheter Segmentation via Adversarial Augmentation
## 1 Introduction  
In minimally invasive surgery (MIS), catheters and guidewires are often used for precise and targeted interventions offering several advantages over conventional procedures, such as faster recovery and less pain for patients (Hereon, 'catheter' and 'guidewire' will be used interchangeably). In general, MIS requires real-time imaging to visualize anatomy and accurately manipulate the tools that are involved in delivering treatment. For example, in cardiac catheterization, a catheter is inserted into the body with real-time continuous monitoring and guidance via X-ray imaging. However, because catheters are thin flexible tubes of varying stiffness, they can be easily mistaken for anatomy during placement and lead to severe complications for patients. Moreover, difficulties in segmenting the catheter may occur as dye is injected into the vein for the improved visibility of blocked or narrowed veins. Expert radiologists are often required to carefully monitor catheters in X-ray for the accurate maneuvering of surgical tools with minimal errors. Thus, an automated system is vital in augmenting the surgical ability of experts during interventions. An automatic system is desired to detect, track, and segment the catheter in X-ray images to mitigate risks for both experts and patients during interventions. However, precise and reliable detection of catheters in these systems is a challenging task. Although most catheters provide radio-opaque markers to ease detection, they may be less visible due to projection angles [1]. The catheter can also be easily confused for wire-like structures and anatomies, such as surgical sutures, stipples, vessels, and ribs, etc., often showing similarities in structural appearance. Thus, existing works focus on improving visual analysis to reduce ambiguity via improved segmentation and detection techniques [2; 3; 4]. For example, [PERSON] et al. [5] proposed an approach to localize surgical tools in X-ray; however, they solely rely on primitive low level features, such as intensity, and often fail to accurately localize the catheter and other tools due to shape complexity and appearance ambiguity. To mitigate this, information regarding the shape of the catheter is used by hessian based line filtering [6] to improve performance. Following this line of work, [PERSON] et al. [7] developed a more sophisticated approach that is based on segment-like features (SEG-lets) to resolve large deformations between successive frames in video. However, this method requires domain knowledge and does not guarantee precise results under noisy conditions. Latest advances in deep convolutional neural networks (CNN) have enabled significant improvements in the performance of catheter detection systems [8; 9]. It is worth noting that the success of these deep learning based methods primarily depends on the availability of large and high-quality training data. Nevertheless, several areas, including medical imaging, suffer from a lack of sufficient data and require precise expert annotation to guarantee quality. Annotation is often non-trivial due to the heterogeneity and complexity of data per modality. To cope with these challenges, recent methods for segmentation follow the following trends; (i) employ extensive augmentation techniques when data is limited [10], (ii) the use of synthetic samples [11], or (iii) creating annotations that are based on simple low-level morphological operators [12]. However, training deep learning models with assumptions such as (iii) may reduce the ability of the models to generalize. Our work falls under categories (i) and (ii), and we aim to design a more general solution via Adversarial based augmentation. In the literature, generative adversarial networks (GANs) [13] are the existing standard for synthetic image generation, with applications in both X-ray translation and pseudo data augmentation. For example, [PERSON] et al. [14] proposed transfering the style of real X-ray acquired during interventions into artificial phantom arteries via CycleGAN [15].