## 1 Introduction
Despite providing promising results, the time-consuming process of making masks for the ground truth labels prevented the long-term scalability of this work for other devices and expanding the dataset to allow for greater generalizability (multiple scanners, background images from patients, etc.). Thus, we propose pursuing a direct identification of the catheter tip without any masking annotation/analysis. There has also been some work involving coordinate regression in medical imaging in recent years, including localizing in areas related to neurology [54] and Arthology [55] sciences. [PERSON] et al. conducted segmentation and localization on the locus coeruleus (LC), a small nucleus in the brain stem. They proposed CoRe-Unet, a type of 3D U-Net to predict coordinates of a voxel in the input volume. The network relies on correlating prominent structures to detect the actual position of the LC through a localization network trained to detect the center-of-mass (COM) coordinates [56]. [PERSON] et al. [55] also focused on landmark detection in the temporomandibular joints (TJ) through a two-stage end-to-end localization network based on an attention-guided mechanism. Their method includes global and local stages, based on learning both local features around landmarks and estimating landmark coordinates. The network consists of a differentiable spatial to numerical transform (DSNT) layer attached to a 3D U-Net, enabling the conversion of heatmaps to coordinate detection [55]. Despite some achievements in the mentioned line of work, models based on heatmap analysis have two main drawbacks: they are computationally expensive and are sensitive to outliers, whilst coordinate regression provides a faster estimation of the landmark [57]. There has been work involving guidewire tracking, usually used in combination with catheter tracking in related studies. Researchers indicate that many frameworks used for catheter tracking can also be applied to guidewire tracking [58]. Traditional methods in endovascular and neurosurgery fields have guided researchers to use either intensity- and learning-based models or the segmentation and movement of the guidewire [58; 59; 60; 61; 62]. However, with emergent segmentation and deep learning techniques, new approaches have taken a turn in this direction. These techniques range from supervised to unsupervised methods. Supervised methods can consist of instance segmentation, region of interest [63; 64; 65; 66], two-stage region of interest, and target segmentation [67], whilst unsupervised methods have used optical flow and a U-Net trained in a Siamese network [68]. Although all these methods are relevant to the field, they include semantic segmentation, which falls out of the scope of this paper. In another work, researchers utilized a CNN to explore the possibility to detect motion between two fluoroscopic frames in catheterization procedures [69]. They were able to compare their CNN-based catheter tip detection with normalized cross correlation (CC) and found a mean absolute error (MAE) of 8.7 \(\pm\) 2.5 pixels or 3.0 \(\pm\) 0.9 mm between methods, with the CNN outperforming CC. However, the researchers state that the correlation between the predictions and tracking results is not obvious. In another study, automated catheter localization for ultrasound-guided high-dose-rate prostate brachytherapy was pursued. They used a U-NET model to localize implanted catheters on transverse images on 3500 manually localized implanted catheters. They reported 80% reconstruction accuracy within 2 mm along 90% of the catheter length; however, they also mentioned that the catheter tip was often not detected and required extrapolation [70]. To overcome the issues in the previously pursued works, this paper presents the implementation of a landmark localization method using coordinate regression for catheter detection from fluoroscopic images acquired during a mock procedure in a 3D-printed heart phantom.