May 26, 2017 · We propose a method to directly extract a medial representation of the vessels using Convolutional Neural Networks.
May 26, 2017 · Typically, vessels are extracted by either a segmentation and thinning pipeline, or by direct tracking. Neither of these methods are well suited ...
We explored the use of convolutional neural networks to segment 3D vessels within volumetric in vivo images acquired by multiphoton microscopy.
Aug 19, 2020 · Advances in imaging techniques enable high resolution 3D visualisation of vascular networks over time and reveal abnormal structural features ...
Mar 19, 2024 · A deep-learning-based framework for fine and automated extraction of tumor vessels from 3D light microscope images.
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Oct 20, 2023 · The aim of this article is to provide the basic concepts for the quantitative characterization of the vascular network structure and operational�...
Missing: Convolutional Recurrent
1 Excerpt. Extracting 3D Vascular Structures from Microscopy Images using Convolutional Recurrent Networks · Russell BatesB. Irving +4 authors. J. Schnabel.
May 23, 2024 · We developed 3DVascNet, a deep learning–based software for automated segmentation and quantification of 3D retinal vascular networks.
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We explored the use of convolutional neural networks to segment 3D vessels within volumetric in vivo images acquired by multiphoton microscopy. We evaluated ...
Aug 1, 2024 · In our study, we develop a novel approach that fully leverages the 3D spatial features of OCTA data using 3D convolutional neural networks (CNNs) ...