Instance Segmentation of Biomedical Images with an Object-aware Embedding Learned with Local Constraints
13 Oct 2019
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Authors: Long Chen, Martin Strauch and Dorit Merhof
Published in: International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2019
Abstract: Automatic instance segmentation is a problem that occurs in many biomedical applications. State-of-the-art approaches either perform semantic segmentation or re ne object bounding boxes obtained from detection methods. Both su er from crowded objects to varying degrees, merging adjacent objects or suppressing a valid object. In this work, we assign an embedding vector to each pixel through a deep neural network. The network is trained to output embedding vectors of similar directions for pixels from the same object, while adjacent objects are orthogonal in the embedding space, which e ectively avoids the fusion of objects in a crowd. Our method yields state-of-the-art results even with a light-weighted backbone network on a cell segmentation (BBBC006 + DSB2018) and a leaf segmentation data set (CVPPP2017).