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Decision Support System for Liver Lesion Segmentation Based on Advanced Convolutional Neural Network Architectures.

Dan Popescu1, Andrei Stanciulescu1, Mihai Dan Pomohaci1

  • 1Faculty of Automatic Control and Computers, University POLITEHNICA of Bucharest, 060042 Bucharest, Romania.

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Summary

This study introduces an intelligent system for segmenting liver and tumors using four neural networks. The integrated system achieved superior segmentation results compared to individual networks.

Keywords:
computed tomographydecision fusiondecision support systemliver lesionsneural networkssemantic segmentation

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Liver cancer is a significant global health concern, ranking as the third leading cause of cancer-related death worldwide.
  • Accurate medical image segmentation is crucial for diagnosis and treatment planning, especially with advancements in imaging and processing technologies.

Purpose of the Study:

  • To develop and evaluate an intelligent decision system for segmenting liver and hepatic tumors using a combination of deep learning models.
  • To improve the accuracy and reliability of liver and tumor segmentation in computed tomography (CT) images.

Main Methods:

  • Integration of four neural networks: ResNet152, ResNeXt101, DenseNet201, and InceptionV3 for semantic segmentation.
  • Utilized the public LiTS17 database of CT images for training, validation, and testing.
  • Employed preprocessing techniques to enhance liver tissue and tumor visibility, and postprocessing to reduce artifacts.

Main Results:

  • The proposed intelligent decision system demonstrated improved segmentation performance, as measured by the Dice coefficient, compared to individual neural networks.
  • The system's results were found to be comparable to those reported in recent state-of-the-art works in liver and tumor segmentation.
  • The integration of multiple networks and a global decision-making approach enhanced segmentation accuracy.

Conclusions:

  • The developed intelligent decision system offers a promising approach for accurate liver and hepatic tumor segmentation in CT images.
  • Combining multiple deep learning models can lead to more robust and effective medical image segmentation solutions.
  • This work contributes to the advancement of AI-driven tools for liver cancer diagnosis and management.