# From CNNs to SAM: A Survey of Deep Learning Techniques for Liver Tumor Segmentation in CT Images

## TL;DR

This paper reviews deep learning techniques for segmenting liver tumors in CT images, comparing methods and highlighting challenges and trends in the field.

## Contribution

A comprehensive survey of over 100 papers on deep learning for liver tumor segmentation, analyzing methodological choices and trends.

## Key findings

- Deep learning models outperform traditional methods in liver tumor segmentation but face challenges due to tumor variability and image inconsistencies.
- Current approaches show promise but are limited by issues such as indistinct tumor boundaries and computational demands.
- The survey identifies key trends and methodological choices that influence segmentation accuracy and efficiency.

## Abstract

Accurate liver tumor segmentation is a critical component of clinical assessment, forming the basis for treatment planning, therapy response monitoring, prognostic assessment, and the delivery of precision medicine. However, in real-world clinical practice, this task remains particularly challenging. The intrinsic diversity of liver tumors —manifested in variations of shape, texture, size, and location—combined with the similarity of neighboring organs, indistinct tumor boundaries, and inconsistencies in image acquisition conditions, makes accurate liver lesion segmentation particularly difficult. In clinical practice, traditional segmentation methods are often used due to their interpretability and lower computational requirements. These approaches are labor-intensive and time-consuming, especially when dealing with 3D medical images, making them impractical for large-scale or real-time applications. In recent years, deep learning (DL) models have gained considerable attention for automating liver lesion segmentation. In this comprehensive review, we analyze over 100 research papers focused on DL-based segmentation of liver tumors from computed tomography (CT) images. This survey examines these studies across multiple dimensions, including input data, model architecture, and evaluation metrics. By exploring both pioneering contributions and emerging trends, we highlight the impact of various methodological choices and address the associated limitations of current approaches.