# A Joint Deep Learning Approach for Automated Liver and Tumor Segmentation

## TL;DR

This paper compares single-step and two-step deep learning architectures for automated liver and tumor segmentation in CT images, aiming to improve accuracy and efficiency in clinical assessments.

## Contribution

It introduces and evaluates a dual-network approach for liver and tumor segmentation, demonstrating its effectiveness over a single-network method.

## Key findings

- Dual-network approach improves segmentation accuracy
- Two-step method outperforms single-step in tumor detection
- Networks trained on LiTS dataset show promising results

## Abstract

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer in adults, and the most common cause of death of people suffering from cirrhosis. The segmentation of liver lesions in CT images allows assessment of tumor load, treatment planning, prognosis and monitoring of treatment response. Manual segmentation is a very time-consuming task and in many cases, prone to inaccuracies and automatic tools for tumor detection and segmentation are desirable. In this paper, we compare two network architectures, one that is composed of one neural network and manages the segmentation task in one step and one that consists of two consecutive fully convolutional neural networks. The first network segments the liver whereas the second network segments the actual tumor inside the liver. Our networks are trained on a subset of the LiTS (Liver Tumor Segmentation) Challenge and evaluated on data.