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Psoriasis skin biopsy image segmentation using Deep Convolutional Neural Network.

Anabik Pal1, Utpal Garain1, Aditi Chandra2

  • 1CVPR Unit, Indian Statistical Institute, Kolkata 700108, India.

Computer Methods and Programs in Biomedicine
|April 14, 2018
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Summary
This summary is machine-generated.

This study introduces automatic segmentation of psoriasis skin biopsy images using deep learning. Convolutional Neural Network (CNN) approaches significantly outperform traditional methods, paving the way for machine-assisted psoriasis analysis.

Keywords:
Data set and EvaluationDeep Convolutional Neural Network (DCNN)Dermis-EpidermisFully Convolutional Neural Network (FCN)Psoriasis Biopsy imageSimple Linear Iterative Clustering (SLIC)

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Area of Science:

  • Medical Imaging
  • Computational Pathology
  • Dermatology

Background:

  • Accurate segmentation of psoriasis skin biopsy images is crucial for developing machine-assisted diagnostic tools.
  • Challenges include complex cellular structures, imaging artifacts, and staining variations.
  • This research pioneers an automatic segmentation approach for psoriasis images.

Purpose of the Study:

  • To develop and evaluate deep neural network architectures for automatic segmentation of psoriasis skin biopsy images.
  • To compare the performance of deep learning models against traditional feature-based classifiers.
  • To establish a prerequisite for advanced machine-assisted analysis of psoriasis.

Main Methods:

  • Exploration of several deep neural network architectures for image segmentation.
  • Classification of super-pixels generated by Simple Linear Iterative Clustering (SLIC) using deep models.
  • Implementation of a U-shaped Fully Convolutional Neural Network (FCN) for end-to-end segmentation.
  • Comparison with traditional classifiers: K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest (RF).

Main Results:

  • Development of an annotated dataset of 90 psoriasis skin biopsy images.
  • Evaluation using Jaccard's Coefficient (JC) and Ratio of Correct Pixel Classification (RCPC).
  • Convolutional Neural Network (CNN) based methods demonstrated superior performance over traditional approaches.

Conclusions:

  • Deep learning, particularly CNNs, shows significant promise for accurate psoriasis image segmentation.
  • The developed methods lay the foundation for practical machine-assisted analysis systems for psoriasis.
  • This advancement can aid in clinical decision-making for psoriasis patients.