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Novel deep learning approaches for accurate liver and tumor segmentation in CT scans

algorithms-and-analysis-of-algorithms
artificial-intelligence
computer-vision
data-mining-and-machine-learning
neural-networks

Abstract

The segmentation of liver and tumor regions in computed tomography (CT) scans is crucial for the diagnosis and management of liver cancer. In this work, we design and assess the U-Net, U²-Net, and U³-Net CNNs for automated multi-class segmentation of liver and tumor tissues in CT scans.

The models were trained with annotated CT scan slices employing a multi-class schema with background, liver, and tumor tissues. In an effort to improve the generalization of the models, specific data augmentation strategies were employed, particularly for the scarcity of tumor sample data.

For the quantitative evaluation, the liver and tumor classes were assessed using the Dice similarity coefficient, Intersection over Union (IoU), accuracy, precision, recall, and F1-score. Out of all the architectures tested, U³-Net marketed the highest performance with a Dice score of 0.97 for liver segmentation [LS] and 0.95 for tumor segmentation [TS]. U²-Net also improved over the baseline U-Net, showing the advantage of deeper hierarchical feature extraction.

Also, qualitative evaluation of the segmentation masks reinforced the small and irregular tumor region segments, illustrating the models’ capability in marking complex anatomical borders. The proposed approach demonstrates strong potential for integration into clinical.

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