Fast registration-based automatic segmentation of serial section images for high-resolution 3-D plant seed modeling

We propose a deformation-based approach for fast and robust segmentation of histological section images into multiple tissues. Derived from deformable registration techniques, it does not solely rely on information present in the image, but uses a-priori information in terms of reference segmentations. The experimental evaluation against state-of-the-art feature based classifiers demonstrates the high performance in segmentation accuracy and the effectiveness of this approach. This serves as basis for processing high-resolution serial section datasets comprising several thousand images towards three-dimensional atlases of plant organs.