Explores techniques for delineation, including Hough transform, gradient orientation, and shape detection, emphasizing the importance of combining graph-based techniques and machine learning.
Delves into the mathematical foundations and importance of directional cues in image processing, exploring computational challenges and selectivity to orientation.
Explores uncertainty quantification and label error detection in deep learning for semantic segmentation, focusing on challenges and methods for error detection.
Explores neuron classification in in silico neuroscience, emphasizing challenges in reconstructing neuronal morphologies and the importance of accurate classifications.
Delves into neuron types, classification, challenges in reconstruction, staining techniques, and artifact correction, highlighting the importance of understanding brain complexity.
