  State-of-the-art image segmentation algorithms generally consist of at least
two successive and distinct computations: a boundary detection process that
uses local image information to classify image locations as boundaries between
objects, followed by a pixel grouping step such as watershed or connected
components that clusters pixels into segments. Prior work has varied the
complexity and approach employed in these two steps, including the
incorporation of multi-layer neural networks to perform boundary prediction,
and the use of global optimizations during pixel clustering. We propose a
unified and end-to-end trainable machine learning approach, flood-filling
networks, in which a recurrent 3d convolutional network directly produces
individual segments from a raw image. The proposed approach robustly segments
images with an unknown and variable number of objects as well as highly
variable object sizes. We demonstrate the approach on a challenging 3d image
segmentation task, connectomic reconstruction from volume electron microscopy
data, on which flood-filling neural networks substantially improve accuracy
over other state-of-the-art methods. The proposed approach can replace complex
multi-step segmentation pipelines with a single neural network that is learned
end-to-end.
