Agata Justyna Mosinska

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Detection of curvilinear structures has long been of interest due to its wide range of applications. Large amounts of imaging data could be readily used in many fields, but it is practically not possible to analyze them manually. Hence, the need for automa ...

Detection of curvilinear structures in images has long been of interest. One of the most challenging aspects of this problem is inferring the graph representation of the curvilinear network. Most existing delineation approaches first perform binary segment ...

The difficulty of obtaining annotations to build training databases still slows down the adoption of recent deep learning approaches for biomedical image analysis. In this paper, we show that we can train a Deep Net to perform 3D volumetric delineation giv ...

Delineation of curvilinear structures is an important problem in Computer Vision with multiple practical applications. With the advent of Deep Learning, many current approaches on automatic delineation have focused on finding more powerful deep architectur ...

Many state-of-the-art delineation methods rely on supervised machine learning algorithms. As a result, they require manually annotated training data, which is tedious to obtain. Furthermore, even minor classification errors may significantly affect the top ...

Agata Justyna Mosinska

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Learning Approach to Delineation of Curvilinear Structures in 2D and 3D Images

Joint Segmentation and Path Classification of Curvilinear Structures

Tracing in 2D to reduce the annotation effort for 3D deep delineation of linear structures

Beyond the Pixel-Wise Loss for Topology-Aware Delineation

Active Learning and Proofreading for Delineation of Curvilinear Structures