Cerebrovascular accidents are a world disease with a severe impact on patients and healthcare systems. Approximately 15 million people suffer an ischemic stroke each year worldwide [1, 2]. More detailed information about the condition of arterial vessels can play a critical role in both preventing stroke and improving stroke therapy [1, 3, 4].
Since about one third of patients die from the consequences of a stroke, it is of great interest to detect indications of cerebrovascular diseases as quickly and as efficiently as possible, enabling to intervene in time or even to take preventive measures [1, 4]. Currently, however, vascular imaging in clinical routine is primarily assessed by visual-qualitative means only. The technical difficulties in extracting cerebral arteries and quantifying their parameters have prevented this data from becoming part of routine clinical practice [1, 5].
Image segmentation in general remains challenging for many applications. In particular, advanced implementations such as ischemic infarct tissue segmentation require highly accurate results to ensure optimal patient care and treatment [6, 7]. Thus, if at all, segmentation of cerebral vessels to date are predominantly performed manually or semi-manually. Since manual vessel segmentation is time consuming, research has focused on developing faster and more general automatic vessel segmentation methods [1, 5].
In recent years, deep learning techniques have demonstrated to be a very useful approach to this problem, as they can, unlike traditional threshold approaches, incorporate spatial information into their predictions [8, 9]. Therefore, the current development trend is shifting away from the rule-based methods proposed in previous decades, such as vessel intensity distributions, geometric models and vessel extraction methods [10, 11]. Although most rule-based approaches such as midline tracing, active contour models, or region growth use various vessel image features for reconstruction [12, 10], they are either hand-crafted or insufficiently validated [11, 10]. Therefore, it is difficult to achieve the desired level of robustness in vessel segmentation, and none of the proposed methods has found widespread application in the clinical setting or in research .
However, even deep learning methods that have shown to be particularly powerful and adaptable have their specific drawbacks, as they demand a large amount of training data [13, 14]. Providing this data is challenging, because it usually contains sensitive personal data and therefore is not publicly available [15, 16, 17]. In addition, successful deep segmentation also requires ground truth data which is, as discussed earlier, both extremely time-consuming and thus costly to create [1, 5].
Recently, several alternative strategies to circumvent this lack of commentary have been explored. For example, methods for semi-supervised semantic segmentation have been successfully developed, based on the generative adversarial network (GAN) approach [17, 14, 18]. Subsequent work has further improved this approach by explicitly accounting particular issues, such as domain shift, during translation and utilizing contrastive learning for translating unpaired images [19, 20].
In addition, pretraining algorithms have emerged that promise to improve performance by preparing the model in an unsupervised manner. This is referred to as self-supervised learning. Its popularity can be traced back to well-known pretraining networks like [21, 22, 23, 24]. These networks are able to incorporate unlabeled samples into the training and thus make use of the entirety of the datasets despite the lack of annotations, ultimately increasing model performance [21, 22, 17].
An alternative approach eliminating this shortage of clinical annotations might involve accelerating the time consuming manual segmentation process. The idea of using deep learning methods to optimize this process has recently become more popular [25, 26, 27]. These interactive segmentations can be used not only for the creation of annotations, but also for the improvement of already existing ones. In doing so, a segmentation can be created in a first step and optimized in subsequent steps either automatically, interactively or manually. These changes are then automatically applied to the entire vessel, saving valuable time [25, 26].
For the reasons stated above, this work aims to investigate whether advanced model architectures can be successfully used for semi-supervised and unsupervised image segmentation, with the overall goal of improving deep vessel segmentation and will conduct an in-depth examination of the potential of pretraining methodologies to increase model performance. This work will investigate whether interactive segmentation might be applied in the medical field and how it can be integrated into the clinical workflow to reduce annotational workload.
- Literature overview of the current state of the art and collection of frameworks
- Pretraining methods
- Interactive segmentation strategies
- Expanding the current state of the art for carotid artery segmentation
- Utilizing semi-supervised contrastive learning mechanisms
- Enabling interactive segmentation
- Systematic analysis and evaluation of the developed deep learning approaches
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