In this work, we propose a deep-learning approach for aligning cross-spectral images. Our approach utilizes a learned descriptor invariant to different spectra. Multi-modal images of the same scene capture different c...
详细信息
Volumetric examinations of the aorta are nowadays of crucial importance for the management of critical pathologies such as aortic dissection, aortic aneurism, and other pathologies, which affect the morphology of the ...
详细信息
The aortic vessel tree is composed of the aorta and its branching arteries, and plays a key role in supplying the whole body with blood. Aortic diseases, like aneurysms or dissections, can lead to an aortic rupture, w...
详细信息
We present an approach for fully automatic urinary bladder segmentation in CT images with artificial neural networks in this study. Automatic medical image analysis has become an invaluable tool in the different treat...
详细信息
In this contribution, we propose an automatic ground truth generation approach that utilizes Positron Emission Tomography (PET) acquisitions to train neural networks for automatic urinary bladder segmentation in Compu...
详细信息
In this work we propose a fully end-to-end approach for multi-spectral image registration and fusion. Our fusion method combines images from different spectral channels into a single fused image using approaches for l...
详细信息
In this work we propose a fully end-to-end approach for multi-spectral image registration and fusion. Our fusion method combines images from different spectral channels into a single fused image using approaches for low and high frequency signals. A prerequisite of fusion is the geometric alignment between the spectral bands, commonly referred to as registration. Unfortunately, common methods for image registration of a single spectral channel might prove inaccurate on images from different modalities. For that end, we introduce a new algorithm for multi-spectral image registration, based on a novel edge descriptor of feature points. Our method achieves an accurate alignment allowing us to further fuse the images. It is experimentally shown to produce a high quality of multi-spectral image registration and fusion under challenging scenarios.
Deep learning has remarkably impacted several different scientific disciplines over the last few years. For example, in image processing and analysis, deep learning algorithms were able to outperform other cutting-edg...
详细信息
The aortic vessel tree, composed of the aorta and its branches, is crucial for blood supply to the body. Aortic diseases, such as aneurysms and dissections, can lead to life-threatening ruptures, often requiring open ...
详细信息
The aortic vessel tree, composed of the aorta and its branches, is crucial for blood supply to the body. Aortic diseases, such as aneurysms and dissections, can lead to life-threatening ruptures, often requiring open surgery. Therefore, patients commonly undergo treatment under constant monitoring, which requires regular inspections of the vessels through medical imaging techniques. Overlapping and comparing aortic vessel tree geometries from consecutive images allows for tracking changes in both the aorta and its branches. Manual reconstruction of the vessel tree is time-consuming and impractical in clinical settings. In contrast, automatic or semi-automatic segmentation algorithms can perform this task much faster, making them suitable for routine clinical use. This paper systematically reviews methods for the automatic and semi-automatic segmentation of the aortic vessel tree, concluding with a discussion on their clinical applicability, the current research landscape, and ongoing challenges.
Deep learning belongs to the field of artificial intelligence, where machines perform tasks that typically require some kind of human intelligence. Deep learning tries to achieve this by drawing inspiration from the l...
详细信息
Deep learning belongs to the field of artificial intelligence, where machines perform tasks that typically require some kind of human intelligence. Deep learning tries to achieve this by drawing inspiration from the learning of a human brain. Similar to the basic structure of a brain, which consists of (billions of) neurons and connections between them, a deep learning algorithm consists of an artificial neural network, which resembles the biological brain structure. Mimicking the learning process of humans with their senses, deep learning networks are fed with (sensory) data, like texts, images, videos or sounds. These networks outperform the state-of-the-art methods in different tasks and, because of this, the whole field saw an exponential growth during the last years. This growth resulted in way over 10,000 publications per year in the last years. For example, the search engine PubMed alone, which covers only a sub-set of all publications in the medical field, provides already over 11,000 results in Q3 2020 for the search term 'deep learning', and around 90% of these results are from the last three years. Consequently, a complete overview over the field of deep learning is already impossible to obtain and, in the near future, it will potentially become difficult to obtain an overview over a subfield. However, there are several review articles about deep learning, which are focused on specific scientific fields or applications, for example deep learning advances in computervision or in specific tasks like object detection. With these surveys as a foundation, the aim of this contribution is to provide a first high-level, categorized meta-survey of selected reviews on deep learning across different scientific disciplines and outline the research impact that they already have during a short period of time. The categories (computervision, language processing, medical informatics and additional works) have been chosen according to the underlying data sources (image,
暂无评论