Abstract
Recently, one earliest skip connected networks named Lmser was revisited and its convolutional layer based version named CLmser was proposed. This paper studies CLmser for segmentation (shortly CLmser-S) of Electron Microscopy (EM) images and also one further development. First, we experimentally show that CLmser-S outperforms the popular U-Net and save many free parameters. Second, we combine one newest formulation named Flexible Lmser (F-Lmser) and CLmser-S into a version called F-CLmser-S, together with learned masks replacing the similarity based one used in F-Lmser for implementing fast-lane skip connections. Experimental results on the ISBI 2012 EM dataset show that F-CLmser-S improves CLmser and achieves competitive performance with state-of-the-art results.
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