Liver Tumor Segmentation with Deep Learning: A Comparative Analysis of CNN-, Transformer-, and YOLO-Based Models on the ATLAS MRI

A comprehensive evaluation was conducted on the ATLAS MRI dataset, including 2D- and 3D-CNN, transformer-based architectures, and single-stage YOLO-based segmentation frameworks. All models were trained using consistent preprocessing, patient-level data splits, and standardized evaluation metrics, including Dice coefficient, Intersection over Union (IoU), precision, recall, and F1-score.

Volumetric convolutional models achieved the highest segmentation accuracy, with the 3D nnU-Net yielding superior performance for both liver (Dice: 0.946) and tumor (Dice: 0.892) segmentation. Transformer-based models demonstrated competitive results, particularly in capturing global contextual information and improving boundary delineation, while YOLO-based approaches provided balanced accuracy with substantially reduced computational cost.