# Deep Learning-Based Liver Tumor Segmentation from Computed Tomography Scans with a Gradient-Enhanced Network

## TL;DR

This paper presents a deep learning method for automatically segmenting liver tumors in CT scans, achieving high accuracy and outperforming existing models.

## Contribution

A novel gradient-enhanced network, G-UNETR++, is proposed for liver tumor segmentation with improved performance on public datasets.

## Key findings

- The method achieved an average dice score of 0.844 on the LiTS dataset.
- It also achieved a dice score of 0.832 on the 3DIRCADb dataset.
- The model outperformed state-of-the-art methods on both datasets.

## Abstract

Background/Objectives: This study aimed to develop a fully automatic method for liver tumor segmentation based on our previously developed gradient-enhanced network G-UNETR++. Methods: The proposed method consists of segmentation of the full liver region from computed tomography (CT) images using G-UNETR++, masking the CT images with the extracted liver region to exclude non-liver regions, and liver tumor segmentation from the masked CT images, also using G-UNETR++. To train and evaluate the model, a total of 131 CT scans (97 for training, 20 for validation, and 20 for testing) from the publicly available LiTS dataset were used. Furthermore, another public dataset, the 3DIRCADb dataset consisting of 20 CT scans was used for cross-validation of the effectiveness and generalizability of our method. Results: Experimental results showed that our method outperformed state-of-the-art models over both the LiTS dataset and the 3DIRCADb dataset, with an average dice score of 0.844 and 0.832 over the two datasets, respectively. Conclusions: The proposed method is effective in clinical application to help physicians with liver tumor diagnosis and treatment.