Conference Proceedings

Deep semantic instance segmentation of tree-like structures using synthetic data

K Halupka, R Garnavi, S Moore

Proceedings - 2019 IEEE Winter Conference on Applications of Computer Vision, WACV 2019 | Published : 2019

Abstract

Tree-like structures, such as blood vessels, often express complexity at very fine scales, requiring high-resolution grids to adequately describe their shape. Such sparse morphology can alternately be represented by locations of centreline points, but learning from this type of data with deep learning is challenging due to it being unordered, and permutation invariant. In this work, we propose a deep neural network that directly consumes unordered points along the centreline of a branching structure, to identify the topology of the represented structure in a single-shot. Key to our approach is the use of a novel multi-task loss function, enabling instance segmentation of arbitrarily complex ..

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University of Melbourne Researchers

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