Review of deep learning based methods for detecting focal liver lesions

doi:10.11996/JG.j.2095-302X.2026010001

Abstract

Abstract:

The detection of Focal Liver Lesions (FLLs) is crucial for disease diagnosis and treatment. Traditional detection methods face many challenges, and the application of deep-learning technology brings new opportunities. In view of this, this paper systematically reviewed the deep-learning-based FLLs detection methods, and provided specific research directions for the development of FLLs detection technology by analyzing the advantages and disadvantages of related technologies. First, the public datasets of liver radiological images were organized and summarized, and the key role of data preprocessing in improving model performance was expounded. Secondly, the 2D and 3D detection algorithms based on convolutional neural networks, Transformer, knowledge distillation, and other technologies were compared and analyzed, revealing the technical evolution path from local feature modeling to global spatio-temporal correlation. In addition, the temporal feature fusion methods for multi-phase images were examined in depth, providing new ideas for dynamic lesion characterization. The review showed that existing methods had achieved breakthroughs in detection accuracy and efficiency, but still faced challenges such as insufficient sensitivity to small lesions, weak cross-device generalization, and lack of clinical verification. Future research was recommended to accelerate the clinical transformation and application of deep learning in auxiliary diagnosis of liver lesions through multi-center data collaboration, lightweight algorithm design, and enhanced interpretability.

Key words: deep learning, focal liver lesions, object detection, computerized tomography scan, multi-phase

CLC Number:

DONG Wenyi, YANG Weidong, TANG Binghui, WANG Qi, XIAO Hongyu. Review of deep learning based methods for detecting focal liver lesions[J]. Journal of Graphics, 2026, 47(1): 1-16.

Figures/Tables 12

References 70

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数据集名称	成像手段	维度	数据量/例	文件格式	任务类型	简介
SLIVER07^[6]	CT	3D	30	mhd	分割	早期经典数据集，样本量较小，常与其他数据集结合用于肝脏分割与肿瘤检测研究
3D-ircadb^[7]	CT	3D	22	dicom,vk	分割	样本量较小但标注质量高，含肝脏及肿瘤区域手动标注，广泛应用于肝脏影像算法验证
LiTS^[8]	CT	3D	201	.nii	分割	高分辨率CT数据集，为肝脏肿瘤分割领域常用基准数据集
CHAOS^[9]	CT, MRI	3D	40	dicom	分割	腹部多器官综合数据集，提供肝脏、肾脏、脾脏真实掩码
MSD^[10]	CT, MRI	3D	644	.nii.gz	分割	包含10个医学影像数据集，其中肝脏相关数据集样本量较大，但存在血管标注不准确问题
JFR^[11]	US	2D	367	/	检测	首个公开的肝脏超声检测数据集
ATLAS^[12]	CE-MRI	3D	90	.nii.gz	分割	采用对比增强MRI成像，提供肝脏及肿瘤分割标注
TriALS 2024 Task1^[13]	CT	3D	60	.nii.gz	分割	针对非洲人群设计，专注于门静脉期肝脏病变分割
HCC-TACE-Seg^[14]	CT	3D	628	.dcm	分割	包含105例确诊的肝细胞癌(HCC)患者CT数据，用于HCC相关检测与分割研究
LLD-MMRI2023^[15]	MRI	3D	394	.nii.gz	检测	多模态MRI数据集，涵盖7种肝脏病变类型

数据集名称	成像手段	维度	数据量/例	文件格式	任务类型	简介
SLIVER07^[6]	CT	3D	30	mhd	分割	早期经典数据集，样本量较小，常与其他数据集结合用于肝脏分割与肿瘤检测研究
3D-ircadb^[7]	CT	3D	22	dicom,vk	分割	样本量较小但标注质量高，含肝脏及肿瘤区域手动标注，广泛应用于肝脏影像算法验证
LiTS^[8]	CT	3D	201	.nii	分割	高分辨率CT数据集，为肝脏肿瘤分割领域常用基准数据集
CHAOS^[9]	CT, MRI	3D	40	dicom	分割	腹部多器官综合数据集，提供肝脏、肾脏、脾脏真实掩码
MSD^[10]	CT, MRI	3D	644	.nii.gz	分割	包含10个医学影像数据集，其中肝脏相关数据集样本量较大，但存在血管标注不准确问题
JFR^[11]	US	2D	367	/	检测	首个公开的肝脏超声检测数据集
ATLAS^[12]	CE-MRI	3D	90	.nii.gz	分割	采用对比增强MRI成像，提供肝脏及肿瘤分割标注
TriALS 2024 Task1^[13]	CT	3D	60	.nii.gz	分割	针对非洲人群设计，专注于门静脉期肝脏病变分割
HCC-TACE-Seg^[14]	CT	3D	628	.dcm	分割	包含105例确诊的肝细胞癌(HCC)患者CT数据，用于HCC相关检测与分割研究
LLD-MMRI2023^[15]	MRI	3D	394	.nii.gz	检测	多模态MRI数据集，涵盖7种肝脏病变类型

方法	真实图像	生成图像	生成图像类型
DCGAN和ACGAN	CT	CT	新的病例
FRGAN	CT	CT	新的病例
cGAN	CT	PET	同一病例的不同模态
cGAN和cycleGAN	MRI	CT	同一病例的不同模态
Tripartite-GAN	MRI	CEMRI	注射了对比剂的图像
GRMM-GAN	MRI	CEMRI	注射了对比剂的图像
Pix-GRL	MRI	GDMRI	注射了对比剂的图像

方法	真实图像	生成图像	生成图像类型
DCGAN和ACGAN	CT	CT	新的病例
FRGAN	CT	CT	新的病例
cGAN	CT	PET	同一病例的不同模态
cGAN和cycleGAN	MRI	CT	同一病例的不同模态
Tripartite-GAN	MRI	CEMRI	注射了对比剂的图像
GRMM-GAN	MRI	CEMRI	注射了对比剂的图像
Pix-GRL	MRI	GDMRI	注射了对比剂的图像