BuyTheDips: PathLoss for improved topology-preserving deep
learning-based image segmentation
- URL: http://arxiv.org/abs/2207.11446v1
- Date: Sat, 23 Jul 2022 07:19:30 GMT
- Title: BuyTheDips: PathLoss for improved topology-preserving deep
learning-based image segmentation
- Authors: Minh On Vu Ngoc, Yizi Chen, Nicolas Boutry, Jonathan Fabrizio and
Clement Mallet
- Abstract summary: We propose a new deep image segmentation method which relies on a new leakage loss: the Pathloss.
Our method outperforms state-of-the-art topology-aware methods on two representative datasets of different natures.
- Score: 1.8899300124593648
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Capturing the global topology of an image is essential for proposing an
accurate segmentation of its domain. However, most of existing segmentation
methods do not preserve the initial topology of the given input, which is
detrimental for numerous downstream object-based tasks. This is all the more
true for deep learning models which most work at local scales. In this paper,
we propose a new topology-preserving deep image segmentation method which
relies on a new leakage loss: the Pathloss. Our method is an extension of the
BALoss [1], in which we want to improve the leakage detection for better
recovering the closeness property of the image segmentation. This loss allows
us to correctly localize and fix the critical points (a leakage in the
boundaries) that could occur in the predictions, and is based on a
shortest-path search algorithm. This way, loss minimization enforces
connectivity only where it is necessary and finally provides a good
localization of the boundaries of the objects in the image. Moreover, according
to our research, our Pathloss learns to preserve stronger elongated structure
compared to methods without using topology-preserving loss. Training with our
topological loss function, our method outperforms state-of-the-art
topology-aware methods on two representative datasets of different natures:
Electron Microscopy and Historical Map.
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