Reproduce “Flood-Filling Networks” on OpenResearch
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About this paper
Michał Januszewski, Jeremy Maitin-Shepard, Peter Li, Jörgen Kornfeld, Winfried Denk, Viren Jain
Flood-Filling Networks introduce a unified, end-to-end trainable recurrent 3D convolutional neural network for image segmentation, demonstrating 98.5% edge accuracy and uniquely achieving a 0.0% merge error rate on challenging connectomic reconstruction from volume electron microscopy data. The architecture integrates an object mask channel for explicit segmentation state memory and recurrent inference.
Key insights
- Unifying the segmentation process into a single, end-to-end learned model allows the network to optimize for global segment topology, effectively learning to 'fill' an object directly from image data.
- The object mask channel serves as a critical explicit memory mechanism, guiding the network to focus on extending a specific object and preventing re-segmentation of already defined regions.
- Recurrent application of a 3D convolutional network with a fixed field of view, iteratively extending an object mask, provides a robust strategy for handling objects of vast and arbitrary dimensions in 3D data.
Summary by alphaXiv.
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