Segmentation of magnetic resonance images with deep learning for the diagnosis of hepatocellular carcinoma

Aim: The aim of this study is to systematically perform a comparative analysis of deep learning-based segmentation models in order to enhance accuracy in the diagnosis of hepatocellular carcinoma (HCC) using magnetic resonance imaging (MRI). The analysis specifically evaluates the impact of different imaging sequences (T1- and T2-weighted), resolution levels, and preprocessing techniques (Gaussian filtering and histogram equalization) on segmentation performance. Methods: In this study, T1-weighted MR images from the ATLAS dataset and T2-weighted MR images obtained from the institution were utilized. Both datasets were processed at three different resolutions (128×128, 256×256, and 512×512). The images were subjected to Gaussian filtering (sigma 1–5) and two distinct histogram equalization techniques: standard Histogram Equalization (HE) and Contrast-Limited Adaptive Histogram Equalization (CLAHE). The performance of the segmentation models was evaluated using several metrics, including the Dice Similarity Coefficient (DICE), Intersection over Union (IoU), Pixel Accuracy (PA), Recall (REC), Sensitivity (SEN), and Specificity (SPE). The results were compared using multivariate statistical tests. Results: The T1-weighted ATLAS dataset demonstrated more consistent and higher overall segmentation performance across standard evaluation metrics. However, the institution's T2-weighted dataset yielded competitive and in some cases superior results for specific metrics. Notably, the T2-weighted images achieved a high Recall score of 0.969 at 128×128 resolution and a high Specificity score of 0.9936 at 512×512 resolution. Gaussian filtering produced optimal results in the sigma 1–2 range, while a noticeable decline in performance was observed at sigma 5. Furthermore, the influence of resolution and filter level on certain metrics particularly Specificity was found to be statistically significant (p < 0.05). Conclusion: The study demonstrates that resolution, MR sequence type, and preprocessing methods must be jointly considered in liver tumor segmentation. Unlike most studies that report only DICE and IoU, this work includes statistical comparisons of multiple metrics. Results show that optimized preprocessing improves both segmentation accuracy and computational efficiency. Thus, the study provides methodological and applied contributions to medical image segmentation. Keywords: Hepatocellular Carcinoma, Deep Learning, Magnetic Resonance Imaging, T1-Weighted Sequence, T2-Weighted Sequence, Segmentation.