基于深度学习的肝脏局灶性病变检测方法综述

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

摘要/Abstract

摘要：

肝脏局灶性病变(FLLs)检测对疾病诊断和治疗至关重要。传统检测方法面临诸多挑战，深度学习技术的应用为其带来新契机。鉴于此，系统综述了基于深度学习的FLLs检测方法，通过深入分析相关技术的优势与不足，为FLLs检测技术的发展提供了具体的研究方向。首先对肝脏放射影像的公开数据集进行了整理归纳，阐述数据预处理对提升模型性能的关键作用。其次，对比分析了基于卷积神经网络、Transformer以及知识蒸馏等技术的2D与3D检测算法，揭示了从局部特征建模到全局时空关联的技术演进路径。此外，深入探讨了针对多期相影像的时序特征融合方法，为动态病变表征提供了新思路。研究表明，现有方法在检测精度与效率上取得突破，但仍面临小病灶敏感性不足、跨设备泛化性弱及临床验证缺乏等挑战。未来研究需通过多中心数据协同、轻量化算法设计及可解释性增强等途径，加速深度学习在肝脏病变辅助诊断中的临床转化与应用。

关键词: 深度学习, 肝脏局灶性病变, 目标检测, 计算机断层扫描, 多期相

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

中图分类号:

董文益, 杨伟东, 唐冰慧, 王琦, 肖宏宇. 基于深度学习的肝脏局灶性病变检测方法综述[J]. 图学学报, 2026, 47(1): 1-16.

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.

图/表 12

参考文献 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