Vasculature Segmentation in MRA Images Using Gradient Compensated Geodesic Active Contours

Precise segmentation of vasculature from three-dimensional (3D) magnetic resonance angiography (MRA) images is playing an important role in image-guided neurosurgery, pre-operation planning and clinical analysis. Active Contour based evolution algorithms are being widely applied to MRA data sets, however existing approaches exhibit some difficulties in extracting tiny parts of the vessels. Our objective is to develop an automated segmentation scheme to accurately extract vasculature of the brain, especially tiny vessels. Inspired by the intrinsic properties of MRA, we have proposed a scheme called the gradient compensated geodesic active contours (GCGAC), which compensates for low gradients near edges of thin vessels. The GCGAC, which is implemented based on level set, has been tested on both synthetic volumetric image and real 3D MRA images. Our experiments show that the introduced gradient compensation can facilitate more accurate segmentation of tiny blood vessels.     

Endoglin/CD105-Based Imaging of Cancer and Cardiovascular Diseases: A Systematic Review

Molecular imaging of pathologic lesions can improve efficient detection of cancer and cardiovascular diseases. A shared pathophysiological feature is angiogenesis, the formation of new blood vessels. Endoglin (CD105) is a coreceptor for ligands of the Transforming Growth Factor-β (TGF-β) family and is highly expressed on angiogenic endothelial cells. Therefore, endoglin-based imaging has been explored to visualize lesions of the aforementioned diseases. This systematic review highlights the progress in endoglin-based imaging of cancer, atherosclerosis, myocardial infarction, and aortic aneurysm, focusing on positron emission tomography (PET), single-photon emission computed tomography (SPECT), magnetic resonance imaging (MRI), near-infrared fluorescence (NIRF) imaging, and ultrasound imaging. PubMed was searched combining the following subjects and their respective synonyms or relevant subterms: “Endoglin”, “Imaging/Image-guided surgery”. In total, 59 papers were found eligible to be included: 58 reporting about preclinical animal or in vitro models and one ex vivo study in human organs. In addition to exact data extraction of imaging modality type, tumor or cardiovascular disease model, and tracer (class), outcomes were described via a narrative synthesis. Collectively, the data identify endoglin as a suitable target for intraoperative and diagnostic imaging of the neovasculature in tumors, whereas for cardiovascular diseases, the evidence remains scarce but promising.     

On the reconstruction of sequences of sparse signals – The Weighted-CS

In this paper, we study the problem of recursively reconstructing time sequences of sparse signals, where sparsity changes smoothly with time. The idea is to use the signal/image of the previous time instance to extract an estimated probability model for the signal/image of interest, and then use this model to guide the reconstruction process. We examine and illustrate the performance of our approach, “Weighted-CS”, with both synthetic and real medical signals/images. It is shown that we can achieve significant performance improvement, using fewer number of samples, compared to other state-of-art Compressive Sensing methods.