Detection of Nuclei in H&E Stained Sections using Convolutional Neural Networks

Mina Khoshdeli, Richard Cong1, Bahram Parvin1

  • 1University of Nevada, Reno



Contributed Paper (Oral)


5. Imaging Informatics


16:00 - 17:30 | Thu 16 Feb | Salon 5 | ThC1

Thu1.3: Imaging Informatics


Detection of nuclei is an important step in phenotypic profiling of histology sections that are usually imaged in bright field. However, nuclei can have multiple phenotypes, which are difficult to model. It is shown that convolutional neural networks (CNN)s can learn different phenotypic signatures for nuclear detection, and that the performance is improved with the feature-based representation of the original image. The feature-based representation utilizes Laplacian of Gaussian (LoG) filter, which accentuates blob-shape objects. Several combinations of input data representations are evaluated to show that by LoG representation, detection of nuclei is advanced. In addition, the efficacy of CNN for vesicular and hyperchromatic nuclei is evaluated. In particular, the frequency of detection of nuclei with the vesicular and apoptotic phenotypes is increased. The overall system has been evaluated against manually annotated nuclei and the F-Scores for alternative representations have been reported.

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