INTEGRATING RESUNET-BASED SEGMENTATION, RADIOMICS, AND GRAPH NEURAL NETWORKS FOR INTELLIGENT LIVER TUMOR CHARACTERIZATION IN COMPUTED TOMOGRAPHY

ne of the main causes of cancer-related deaths in the world is liver cancer, the early diagnosis is important and correct demarcation of the tumour is vital in making accurate clinical decisions. Computed Tomography (CT) imaging is commonly utilized in the diagnosis of
liver tumors but it usually encounters the problem of low contrast, noise, and diffuse appearance of tumor and unclear margin which frequently restricts the functionality
of traditional image processing and machine learning methods. In order to solve these issues, the paper will introduce a strong and unified framework to combine
deep learning-based segmentation, radiomic feature retrieval, and graph neural
network (GNN) analysis in the evaluation of liver tumors.
First, CT scan images are processed in a sophisticated pre-processing pipeline
that includes noise reduction, intensity-related normalization, and image quality
enhancement based on boundaries to enhance the quality of the image and interpatient variability. Then, two deep learning models, i.e. U-Net and Residual U-Net
(ResUNet) are used to segment the liver tumor accurately. The evaluation of the
performance of segmentation is calculated by the Dice similarity coefficient on which
the proposed ResUNet model demonstrates a better validation Dice score of 0.981, in
comparison with the standard U-Net model. After segmentation, radiomic features
are derived out of the segmented tumor areas of interest, first-order statistical
features and texture-based features based on Gray Level Co-occurrence Matrix
(GLCM) analysis.
These characteristics present tumor heterogeneity and structural feature
patterns, which cannot be readily identified by visual inspection. To further analyze
the patients at the individual level, the resulted radiomic features are represented as
the nodes within a graph, and inter-tumoral relationships are trained with the help of
a Graph Neural Network. This graph representation allows the efficient study of
tumor similarity and provides the evaluation of tumor stage based on the patient.