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Dense deconvolution net: Multi path fusion and dense deconvolution for high resolution skin lesion segmentation.

Xinzi He1, Zhen Yu1, Tianfu Wang1

  • 1School of Biomedical Engineering, Health Science Center, Shenzhen University, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen, Guangdong, China.

Technology and Health Care : Official Journal of the European Society for Engineering and Medicine
|May 16, 2018
PubMed
Summary

This study introduces a novel deep dense deconvolution network for accurate skin lesion segmentation in dermoscopy images. The proposed method significantly improves segmentation accuracy, outperforming existing approaches.

Keywords:
Dermoscopy imagedeep residual networkdense deconvolution nethierarchical supervisionskin lesion segmentation

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Area of Science:

  • Medical image analysis
  • Computer vision
  • Dermatology

Background:

  • Dermoscopy imaging is crucial for skin lesion diagnosis.
  • Accurate segmentation is essential for automated assessment of dermoscopy images.
  • Variations in viewpoint and scale pose challenges for skin lesion segmentation.

Purpose of the Study:

  • To develop a novel skin lesion segmentation network to address viewpoint and scale variations.
  • To improve the accuracy and performance of automatic dermoscopy image assessment.

Main Methods:

  • A very deep dense deconvolution network was proposed for skin lesion segmentation.
  • Integration of deep dense layers and multi-path Deep RefineNet enhanced segmentation.
  • Skip connections aggregated deep representations for global feature maps.
  • Dense deconvolution layers captured diverse features and smoothed segmentation maps.

Main Results:

  • The proposed method achieved superior performance on public datasets (2016 & 2017 challenges).
  • Accuracy reached 96.0% and 93.9% on the respective datasets.
  • This represents a significant improvement over traditional methods (6.0% and 1.2% increase).

Conclusions:

  • The Dense Deconvolution Net framework offers high accuracy for skin lesion segmentation.
  • The method processes testing images efficiently, with an average time of 0.253 seconds per image.