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Semi-Supervised Nests of Melanocytes Segmentation Method Using Convolutional Autoencoders.
Dariusz Kucharski1, Pawel Kleczek1, Joanna Jaworek-Korjakowska1
1Department of Automatic Control and Robotics, AGH University of Science and Technology, al. A. Mickiewicza 30, 30-059 Krakow, Poland.
Sensors (Basel, Switzerland)
|March 15, 2020
Summary
This study introduces a novel deep learning method for segmenting nevus cell nests in skin histopathology images, even with limited data. The approach achieves state-of-the-art results in distinguishing benign from malignant skin lesions.
Area of Science:
- Dermatopathology
- Computer Vision
- Deep Learning
Background:
- Accurate segmentation of nevus cell nests is crucial for diagnosing skin lesions.
- Distinguishing benign from malignant lesions relies on border irregularity criteria.
- Existing methods struggle with limited ground-truth data for segmentation tasks.
Purpose of the Study:
- To develop a semi-supervised segmentation solution for histopathological images.
- To address the challenge of limited ground-truth data in segmentation tasks.
- To accurately segment nevus cell nests for improved dermatopathology diagnostics.
Main Methods:
- Utilized a convolutional autoencoder architecture for segmentation.
- Implemented a two-step learning process within the autoencoder framework.
- Applied the method to histopathological images of skin specimens for nevus cell nest detection.
Main Results:
- Achieved a Dice similarity coefficient of 0.81.
- Demonstrated a sensitivity of 0.76 and specificity of 0.94.
- The proposed deep learning tool effectively segments nevus areas, outperforming existing methods.
Conclusions:
- The developed semi-supervised segmentation approach is effective for histopathology.
- The method successfully segments nevus cell nests, even with small datasets.
- This computer-vision tool offers a novel solution for dermatopathology image analysis.
