Thesis Description
Coronary Artery Disease (CAD) is one the most predominant contributors to cardiovascular disease
that stands as the major cause of death globally [1]. Usually, it manifests itself as narrowed or blocked
arteries caused by plaque buildup, a condition known as Atherosclerosis. Percutaneous Coronary In-
tervention (PCI) is a frequently used treatment for CAD in which the narrowed arteries are widened.
A typical type of PCI is revascularization using angioplasty with a stent [2]. Generally, as part of this
treatment, a thin flexible tube is inserted into the femoral artery through the groin. Once the tip is
properly positioned in the blockage site, a balloon which is surrounded by a stent graft is inflated to
compress the plaque against the arterial walls. After the procedure is completed and the balloon is
removed, the stent keeps the artery open and supports the blood flow.
Real-time 2D X-ray projections serve as the guidance method of choice for catheter-based interventions
like PCI to help the physicians visually determine the position and extent of the stents [3]. However,
visualizing stents in conventional X-ray images is challenging because of their low radio-opacity. Hence,
digital stent enhancement (DSE) methods have been developed to enhance the stent visibility in X-ray
image sequences [4]. Angioplasty balloons often incorporate two highly radio-opaque markers [5]. De-
tecting and tracking these markers enables DSE methods by registering all frames within the sequence
followed by a mean intensity projection [6]. Therefore, an accurate and robust detection of the stent
markers is a crucial component of all DSE methods. This task is usually performed automatically
using machine learning (ML). An additional challenge in this domain is that due to the high frame
rates of up to 30 frames per second the marker detection needs high computational efficiency.
Possible ML techniques for this task include landmark detection and object detection approaches.
These play a crucial role in the computer vision area, particularly in the field of medical image pro-
cessing [7]. They facilitate the identification of anatomical features, recognition and precise localization
of pathological conditions, and the accurate delineation of structures of interest within medical im-
ages [8]. A few of these methodologies have been employed for balloon marker detection and stent
localization [9–15]. Some approaches rely on conventional ML approaches such as template-based-
matching [14] and adaptive thresholding [9]. On the other hand, the continuous progress in deep
learning offers promising potential for further advancements in stent localization. U-net, a widely
adopted CNN architecture in medical image segmentation, has been employed as the backbone of
several state-of-the-art models to segment the catheter shaft [12] or generate markers heatmap by
treating each landmark as a 2D Gaussian distribution [15]. Although these approaches have improved
stent visualization significantly, they primarily focus on detecting and tracking single balloon marker
pairs in each frame. To address the challenge of stabilizing multiple stents, some researchers have
employed object detection methods through guidewire endpoint localization employing an extended
variant of the Faster R-CNN model [10, 11]. However, detecting objects with common architectures
like R-CNN family models is computationally demanding. Since real-time performance is crucial for
providing instant information about the position of stents to physicians, faster models like YOLO [16]
are needed to accelerate the process. Therefore, a model based on YOLOv3 is proposed that meets
the requirement for real-time guidewire detection and endpoint localization [13].
multiple balloon markers in fluoroscopic images. Firstly, the most promising approaches from the
literature need to be compared, also with the most recent developments in the field included. These
include the object detection networks of the YOLO family [17] as well as the heatmap-based point
regression approaches.
Secondly, the pre-processing of the fluoroscopic images can vary to a large extent as multiple algorithms
with a plethora of parameters can be altered. Therefore, it is of high interest to evaluate which influence
different pre-processing parameterization has on the performance of a marker detector.
Finally, the training of a robust network requires a large collection of data covering a large variation
of potential physical influences. To mitigate the need to collect a large amount of clinical data, an
evaluation of whether the use of suitable phantom data is sufficient shall be conducted.
The thesis will comprise the following work items:
• Literature overview of state-of-the-art automated landmark and object detection approaches
• Balloon marker detection
– Data annotation and preprocessing
– Train a network of the YOLO-family on phantom data
– Train a U-net model for heatmap regression
– Possibly: post-processing
• Analysis of the Deep Learning models
– Evaluate the effect of various pre-processing parameterizations on the marker detector
performance
– Evaluate and compare the performance of implemented algorithms on both phantom and
clinical data
References
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