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Deep Neural Networks for Medical Image Segmentation.

Priyanka Malhotra1, Sheifali Gupta1, Deepika Koundal2

  • 1Chitkara University Institute of Engineering and Technology, Chitkara University, Chandigarh, Punjab, India.

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Summary

This review explores deep convolutional neural networks for medical image segmentation. It covers datasets, evaluation metrics, and challenges, offering insights into state-of-the-art deep learning solutions for analyzing medical images.

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Area of Science:

  • Digital image processing
  • Medical image analysis
  • Computer vision

Background:

  • Medical image segmentation is crucial for disease analysis, treatment planning, and radiation dosage control.
  • Challenges include artifacts in medical images, demanding accurate segmentation.
  • Deep neural networks show significant promise in addressing these challenges.

Purpose of the Study:

  • To review the literature on medical image segmentation using deep convolutional neural networks (CNNs).
  • To examine widely used medical image datasets and evaluation metrics.
  • To discuss challenges and state-of-the-art solutions in the field.

Main Methods:

  • Literature review of deep learning strategies for medical image segmentation.
  • Analysis of various CNN-based network performances.
  • Examination of commonly used medical image datasets and segmentation metrics.

Main Results:

  • Deep learning, particularly CNNs, has achieved high performance in medical image segmentation tasks.
  • Identified key datasets, evaluation metrics, and CNN architectures relevant to the field.
  • Highlighted existing challenges and contemporary solutions in medical image segmentation.

Conclusions:

  • Deep CNNs are a powerful tool for advancing medical image segmentation.
  • Further research is needed to address persistent challenges and enhance segmentation accuracy.
  • This review provides a comprehensive overview for researchers in medical image analysis.