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DiskMask: Focusing Object Features for Accurate Instance Segmentation of Elongated or Overlapping Objects

IEEE International Symposium on Biomedical Imaging (ISBI), 2020
Abstract: Deep learning has enabled automated segmentation in a large variety of cases. Instance segmentation of touching and overlapping objects remains an open challenge. We present an end-to-end approach that focuses object detections and features to local regions in an encoder stage and derives accurate instance masks in a decoder. We avoid heavy pre- or post-processing, such as lifting or non-maximum suppression. The approach compares favorably to the current state-of-the-art on three challenging biological datasets.


Other associated files : ISBI20_DiskMask_finalSubmission_IEEE_CR.pdf [1.9MB]  

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BibTex reference

@InProceedings{BMB20,
  author       = "A. B{\"o}hm and N. Mayer and T. Brox",
  title        = "DiskMask: Focusing Object Features for Accurate Instance Segmentation of Elongated or Overlapping Objects",
  booktitle    = "IEEE International Symposium on Biomedical Imaging (ISBI)",
  month        = " ",
  year         = "2020",
  url          = "http://lmb.informatik.uni-freiburg.de/Publications/2020/BMB20"
}

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