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Cell Segmentation & Abnormality Risk Estimation

The Challenge:

  • Manufacturer of leading FISH imaging platform required robust nuclei segmentation and abnormality risk estimation modules for their integrated software
  • Tightly clumped and overlapping cells
  • Precise interpretation is necessary to overcome ambiguity, avoid false positives, and detect all rare events

Our Approach:

  • Customized our state-of-the-art deep learning based segmentation technology for client dataset image characteristics
  • Created robust segmentation & marker assignment algorithms for overlapping cells
  • Implemented custom abnormality risk measure

Results:

  • Nuclei detection: 98% precision, recall, and F1 score
  • Nuclei segmentation: 0.98 mean Dice score
  • Key marker detection: 95% precision, recall, and F1 score

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