Enhancing Boundary Segmentation for Topological Accuracy with Skeleton-based Methods
arxiv(2024)
摘要
Topological consistency plays a crucial role in the task of boundary
segmentation for reticular images, such as cell membrane segmentation in neuron
electron microscopic images, grain boundary segmentation in material
microscopic images and road segmentation in aerial images. In these fields,
topological changes in segmentation results have a serious impact on the
downstream tasks, which can even exceed the misalignment of the boundary
itself. To enhance the topology accuracy in segmentation results, we propose
the Skea-Topo Aware loss, which is a novel loss function that takes into
account the shape of each object and topological significance of the pixels. It
consists of two components. First, the skeleton-aware weighted loss improves
the segmentation accuracy by better modeling the object geometry with
skeletons. Second, a boundary rectified term effectively identifies and
emphasizes topological critical pixels in the prediction errors using both
foreground and background skeletons in the ground truth and predictions.
Experiments prove that our method improves topological consistency by up to 7
points in VI compared to 13 state-of-art methods, based on objective and
subjective assessments across three different boundary segmentation datasets.
The code is available at https://github.com/clovermini/Skea_topo.
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