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    A comparative study of deep learning models for automated liver and tumor segmentation in 2d contrast-enhanced MRI images
    (IEEE, 2025) Tokatlı, Nazlı; Bilmez, Yakuphan; Bayram, Mücahit; Bayır, Beyzanur; Özalkan, Helin; Tekin, Zeynep; Örmeci, Necati; Altun, Halis
    This paper presents a comprehensive investigation into deep learning techniques for the automated segmentation of the liver and tumors from 2D abdominal contrast-enhanced Magnetic Resonance Imaging (MRI) slices. Addressing a significant challenge in medical image analysis, our study leverages the public ATLAS dataset [1], using a selection of 60 3D abdominal MRI scans, from which we extracted approximately 3,750 2D slices for model training and evaluation. The core objective was the precise identification and delineation of both the liver organ and any intrahepatic lesions. A comparative analysis was conducted on three U-Net-based architectures: the standard Attention U-Net model incorporating EfficientNet-b3 and CBAM but without Focal Loss, the Attention U-Net model with integrated Focal Loss, and the ResNet34-Based U-Net model. To optimize performance, we explored the efficacy of different loss functions, namely DiceLoss and a hybrid DiceLoss with Focalcoss. Our findings are promising: Among the evaluated models, the ResNet34-Based U-Net demonstrated the highest performance with a Dice score of 91.36% and an IoU score of 89.52%. It was followed by the Attention U-Net with Focal Loss, which achieved 86.41% Dice and 81.61% IoU scores, and the standard Attention U-Net, which obtained 85.93% Dice and 81.19% IoU scores. These results underscore the significant potential of our 2D-based methodology to enhance the precision and efficiency of liver and tumor detection from abdominal scans, offering a valuable tool to support clinicians in early diagnosis and to alleviate their workload.