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Deep Learning Techniques for Medical Image Segmentation: Achievements and Challenges.

Mohammad Hesam Hesamian1,2, Wenjing Jia3, Xiangjian He3

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Summary

Deep learning excels at medical image segmentation, crucial for diagnosis and treatment. This review critically appraises popular deep learning methods, challenges, and solutions for this vital task.

Keywords:
CNNDeep learningMedical image segmentationOrgan segmentation

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Area of Science:

  • Medical imaging
  • Computer vision
  • Artificial intelligence

Background:

  • Deep learning-based image segmentation is a robust tool.
  • It is widely used to separate homogeneous areas in medical images.
  • This separation is a critical component of diagnosis and treatment pipelines.

Purpose of the Study:

  • To critically appraise popular deep learning methods for medical image segmentation.
  • To summarize common challenges encountered in medical image segmentation using deep learning.
  • To suggest possible solutions for these challenges.

Main Methods:

  • Review and critical appraisal of existing literature on deep learning for medical image segmentation.
  • Analysis of popular deep learning techniques applied to medical imaging.
  • Identification and categorization of common challenges and their proposed solutions.

Main Results:

  • Deep learning methods offer robust solutions for medical image segmentation.
  • Common challenges include data scarcity, annotation variability, and model generalizability.
  • Various solutions are proposed, including transfer learning, data augmentation, and specialized network architectures.

Conclusions:

  • Deep learning is a powerful technique for medical image segmentation.
  • Addressing challenges in data, annotation, and generalization is key to advancing the field.
  • Continued research into novel methods and solutions will improve diagnostic and treatment pipelines.