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Automatic Liver Tumor Segmentation from CT Images Using Graph Convolutional Network.

Maryam Khoshkhabar1, Saeed Meshgini1, Reza Afrouzian2

  • 1Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 51666-16471, Iran.

Sensors (Basel, Switzerland)
|September 9, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a new deep learning method for accurately segmenting liver tumors and organs in CT scans, improving diagnostic precision. The model demonstrates high accuracy and robustness, even in noisy conditions, aiding radiologists in medical diagnosis.

Keywords:
CT imagesChebyshev graph convolutiondeep learningliver segmentation

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Accurate segmentation of liver and liver tumors in CT images is crucial for computer-aided diagnosis and biomarker quantification.
  • Challenges in segmentation arise from similar organ textures and intensity values, leading to misidentification and time-consuming manual processes.
  • Existing machine learning methods often lack precision, speed, and dependability for liver segmentation and tumor identification.

Purpose of the Study:

  • To develop a novel deep learning-based technique for precise segmentation of liver organs and tumors in computed tomography (CT) images.
  • To enhance the accuracy and reliability of automated liver segmentation and tumor identification processes.
  • To provide a robust solution that assists radiologists in clinical decision-making.

Main Methods:

  • A novel deep learning architecture utilizing four Chebyshev graph convolution layers and a fully connected layer was proposed.
  • The method was trained and evaluated using the publicly available LiTS17 database for liver tumor segmentation.
  • Performance was assessed under various noisy conditions, evaluating robustness across different signal-to-noise ratios (SNRs).

Main Results:

  • The proposed method achieved high performance metrics on the LiTS17 dataset, including 99.1% accuracy, 91.1% Dice coefficient, and 90.8% mean IoU.
  • Sensitivity, precision, and recall were reported at 99.4%, 99.4%, and 91.2%, respectively.
  • The model maintained approximately 90% accuracy for liver organ segmentation even at an SNR of -4 dB, demonstrating significant noise resilience.

Conclusions:

  • The developed deep learning technique offers a highly accurate and dependable solution for liver and liver tumor segmentation in CT images.
  • The model's robustness in noisy environments suggests its practical applicability in diverse clinical settings.
  • This approach is expected to significantly aid radiologists and specialist physicians in future medical diagnoses and treatment planning.