Automated segmentation of liver tumors from computed tomographic scans

The precision of liver tumor segmentation heavily depends on the doctor's expertise, hence it is required to produce an algorithm for automatic liver tumor segmentation to reduce the manual intervention in assessing liver disease identification. We propose a CNN-based UNet architecture designed to segment liver tumors from CT images of size 128×128. In this model, modifications were made to the encoder, decoder, and bridge paths to enhance feature extraction efficiency. The performance of the modified UNet was evaluated against an existing segmentation method using the same CT image size. The comparison focused on the Dice similarity coefficient and accuracy. Our proposed method demonstrated a high Dice similarity coefficient of 75.37 % and an accuracy of 99.75 % on the 3Dircadb dataset. These results indicate that our modified UNet achieved superior segmentation metrics compared to state-of-the-art methods, showcasing its effectiveness in liver tumor segmentation.