Level-Set Based Artery-Vein Separation in Blood Pool Agent CE-MR Angiograms

Blood pool agents (BPAs) for contrast-enhanced (CE) magnetic-resonance angiography (MRA) allow prolonged imaging times for higher contrast and resolution. Imaging is performed during the steady state when the contrast agent is distributed through the complete vascular system. However, simultaneous venous and arterial enhancement in this steady state hampers interpretation. In order to improve visualization of the arteries and veins from steady-state BPA data, a semiautomated method for artery-vein separation is presented. In this method, the central arterial axis and central venous axis are used as initializations for two surfaces that simultaneously evolve in order to capture the arterial and venous parts of the vasculature using the level-set framework. Since arteries and veins can be in close proximity of each other, leakage from the evolving arterial (venous) surface into the venous (arterial) part of the vasculature is inevitable. In these situations, voxels are labeled arterial or venous based on the arrival time of the respective surface. The evolution is steered by external forces related to feature images derived from the image data and by internal forces related to the geometry of the level sets. In this paper, the robustness and accuracy of three external forces (based on image intensity, image gradient, and vessel-enhancement filtering) and combinations of them are investigated and tested on seven patient datasets. To this end, results with the level-set-based segmentation are compared to the reference-standard manually obtained segmentations. Best results are achieved by applying a combination of intensity- and gradient-based forces and a smoothness constraint based on the curvature of the surface. By applying this combination to the seven datasets, it is shown that, with minimal user interaction, artery-vein separation for improved arterial and venous visualization in BPA CE-MRA can be achieved

Breast Ultrasound Image Segmentation Based on Uncertainty Reduction and Context Information

Breast cancer frequently occurs in women over the world. It was one of the most serious diseases and the second common cancer among women in 2019. The survival rate of stages 0 and 1 of breast cancer is closed to 100%. It is urgent to develop an approach that can detect breast cancer in the early stages. Breast ultrasound (BUS) imaging is low-cost, portable, and effective; therefore, it becomes the most crucial approach for breast cancer diagnosis. However, BUS images are of poor quality, low contrast, and uncertain. The computer-aided diagnosis (CAD) system is developed for breast cancer to prevent misdiagnosis. There have been many types of research for BUS image segmentation based on classic machine learning and computer vision methods, e.g., clustering methods, thresholding methods, level set, active contour, and graph cut. Since deep neural networks have been widely utilized in nature image semantic segmentation and achieved good results, deep learning approaches are also applied to BUS image segmentation. However, the previous methods still suffer some shortcomings. Firstly, the previous non-deep learning approaches highly depend on the manually selected features, such as texture, frequency, and intensity. Secondly, the previous deep learning approaches do not solve the uncertainty and noise in BUS images and deep learning architectures. Meanwhile, the previous methods also do not involve context information such as medical knowledge about breast cancer. In this work, three approaches are proposed to measure and reduce uncertainty and noise in deep neural networks. Also, three approaches are designed to involve medical knowledge and long-range distance context information in machine learning algorithms. The proposed methods are applied to breast ultrasound image segmentation. In the first part, three fuzzy uncertainty reduction architectures are designed to measure the uncertainty degree for pixels and channels in the convolutional feature maps. Then, medical knowledge constrained conditional random fields are proposed to reflect the breast layer structure and refine the segmentation results. A novel shape-adaptive convolutional operator is proposed to provide long-distance context information in the convolutional layer. Finally, a fuzzy generative adversarial network is proposed to reduce uncertainty. The new approaches are applied to 4 breast ultrasound image datasets: one multi-category dataset and three public datasets with pixel-wise ground truths for tumor and background. The proposed methods achieve the best performance among 15 BUS image segmentation methods on the four datasets

Neutro-Connectedness Theory, Algorithms and Applications

Connectedness is an important topological property and has been widely studied in digital topology. However, three main challenges exist in applying connectedness to solve real world problems: (1) the definitions of connectedness based on the classic and fuzzy logic cannot model the “hidden factors” that could influence our decision-making; (2) these definitions are too general to be applied to solve complex problem; and (4) many measurements of connectedness are heavily dependent on the shape (spatial distribution of vertices) of the graph and violate the intuitive idea of connectedness. This research focused on solving these challenges by redesigning the connectedness theory, developing fast algorithms for connectedness computation, and applying the newly proposed theory and algorithms to solve challenges in real problems. The newly proposed Neutro-Connectedness (NC) generalizes the conventional definitions of connectedness and can model uncertainty and describe the part and the whole relationship. By applying the dynamic programming strategy, a fast algorithm was proposed to calculate NC for general dataset. It is not just calculating NC map, and the output NC forest can discover a dataset’s topological structure regarding connectedness. In the first application, interactive image segmentation, two approaches were proposed to solve the two most difficult challenges: user interaction-dependence and intense interaction. The first approach, named NC-Cut, models global topologic property among image regions and reduces the dependence of segmentation performance on the appearance models generated by user interactions. It is less sensitive to the initial region of interest (ROI) than four state-of-the-art ROI-based methods. The second approach, named EISeg, provides user with visual clues to guide the interacting process based on NC. It reduces user interaction greatly by guiding user to where interacting can produce the best segmentation results. In the second application, NC was utilized to solve the challenge of weak boundary problem in breast ultrasound image segmentation. The approach can model the indeterminacy resulted from weak boundaries better than fuzzy connectedness, and achieved more accurate and robust result on our dataset with 131 breast tumor cases