基于通道残差嵌套U结构的CT影像肺结节分割方法

doi:10.11996/JG.j.2095-302X.2023050879

图学学报 ›› 2023, Vol. 44 ›› Issue (5): 879-889.DOI: 10.11996/JG.j.2095-302X.2023050879

• 图像处理与计算机视觉 • 上一篇下一篇

基于通道残差嵌套U结构的CT影像肺结节分割方法

蒋武君¹(), 支力佳¹^,²^,³(), 张少敏¹^,²^,³, 周涛¹^,³

1.北方民族大学计算机科学与工程学院，宁夏银川 750021
2.宁夏回族自治区人民医院医学影像中心，宁夏银川 750000
3.北方民族大学图像图形智能处理国家民委重点实验室，宁夏银川 750021

收稿日期:2023-05-25 接受日期:2023-08-19 出版日期:2023-10-31 发布日期:2023-10-31
通讯作者: 支力佳(1977-)，男，讲师，博士。主要研究方向为计算机视觉、医学图像分析处理等。E-mail：zhilj@nun.edu.cn
作者简介:蒋武君(1998-)，男，硕士研究生。主要研究方向为计算机视觉、医学图像分析处理。E-mail：184611875@qq.com
基金资助:
宁夏自然科学基金项目(2021AAC03198);宁夏自然科学基金项目(2023AAC03263);国家自然科学基金项目(61561002);国家自然科学基金项目(62062003);宁夏医学影像临床研究中心创新平台建设项目(2018DPG05006)

CT image segmentation of lung nodules based on channel residual nested U structure

JIANG Wu-jun¹(), ZHI Li-jia¹^,²^,³(), ZHANG Shao-min¹^,²^,³, ZHOU Tao¹^,³

1. School of Computer Science and Engineering, North Minzu University, Yinchuan Ningxia 750021, China
2. Medical Imaging Center, Ningxia Hui Autonomous Region People's Hospital, Yinchuan Ningxia 750000, China
3. The Key Laboratory of Images & Graphics Intelligent Processing of State Ethnic Affairs Commission, Yinchuan Ningxia 750021, China

Received:2023-05-25 Accepted:2023-08-19 Online:2023-10-31 Published:2023-10-31
Contact: ZHI Li-jia (1977-), lecturer, Ph.D. His main research interests cover computer vision, medical image analysis and processing, etc. E-mail：zhilj@nun.edu.cn
About author:JIANG Wu-jun (1998-), master student. His main research interests cover computer vision, medical image analysis and processing. E-mail：184611875@qq.com
Supported by:
Ningxia Natural Science Foundation China(2021AAC03198);Ningxia Natural Science Foundation China(2023AAC03263);National Natural Science Foundation of China(61561002);National Natural Science Foundation of China(62062003);Ningxia Medical Imaging Clinical Research Center Innovation Platform Construction Project(2018DPG05006)

摘要/Abstract

摘要：

早诊断早治疗对提升肺癌的存活率至关重要。肺结节是肺癌早期主要表现，但其异质性特征增加了计算机断层扫描对肺结节的检测难度，降低了分割结果的精确度。为提高肺结节分割结果的完整性和精确度，提出三维通道残差嵌套U网络(CR U²Net)。浅层特征同时包含病灶细节和噪声信息，提出浅层信息处理U结构平衡噪声信息的干扰；为加强不同层特征信息的交互，丰富特征表达和传递，提出通道残差结构，配合嵌套U结构实现特征信息的提取优化；考虑到浅层特征包含空间细节信息而深层特征具有语义抽象性, 设计通道挤压U结构实现不同语义级别特征有效融合；将上述模块集成到UNet中构建出基于嵌套U结构的肺结节分割模型。提出的模型在Lung Image Database Consortium and Image Database Resource Initiative数据集中进行训练，达到了83.83%的Dice系数。优于多数现有肺结节分割方法且与UNet，UNet++以及PCAMNet网络相比领先了3.98%，1.96%和1.26%；针对网络结构进行有效性验证，结果表明各模块均发挥作用，在可接受参数量和计算量的情况下达到最优性能。

关键词: 深度神经网络, 肺结节分割, 通道残差结构, 嵌套U结构, 通道挤压模块

Abstract:

Early diagnosis and treatment are pivotal in elevating the chances of lung cancer survival. Early-stage lung cancer often manifests through lung nodules. However, their heterogeneity poses a challenge in their detection of lung nodules via computed tomography, subsequently diminishing the accuracy of segmentation results. To improve the completeness and accuracy of lung nodule segmentation results, a 3D channel residual nested U-network (CR U²Net) was proposed for lung nodule segmentation. The shallow information processing U-structure (SIPU) was proposed to address the challenge of managing the interference of noise information while simultaneously incorporating key lesion details within shallow features. To enhance the interaction across different layers of feature information, and to enrich feature expression and transfer, the Channel Residual structure was introduced in conjunction with the nested U-structure to extract and optimize feature information. Acknowledging the spatial detail information found in shallow features and the semantic abstraction in deep features, the channel extrusion U-structure (CEU) was designed to effectively fuse features at different semantic levels. By integrating the proposed modules into UNet, a lung nodule segmentation model based on nested U-structures was constructed. The proposed model was trained on the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset. And chieved the best Dice Similarity Coefficient performance, reaching 83.83%. This outperformed UNet, UNet++, and PCAMNet networks by 3.98%, 1.96%, and 1.26%, respectively. In addition, ablation experiments were conducted to evaluate the structural validity of the proposed CR U²Net, demonstrating that each module within the proposed segmentation algorithm contributes to achieving optimal performance while adhering to acceptable parameter and computational constraints.

Key words: deep neural network, lung nodule segmentation, channel residual structure, nested U structure, channel extrusion module

中图分类号:

TP391

蒋武君, 支力佳, 张少敏, 周涛. 基于通道残差嵌套U结构的CT影像肺结节分割方法[J]. 图学学报, 2023, 44(5): 879-889.

JIANG Wu-jun, ZHI Li-jia, ZHANG Shao-min, ZHOU Tao. CT image segmentation of lung nodules based on channel residual nested U structure[J]. Journal of Graphics, 2023, 44(5): 879-889.

图/表 14

参考文献 35

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方法		数据集	输入尺寸	样本数量	交叉验证	DSC	样本选取策略
传统肺结节分割方法	文献[29]	模拟数据 LIDC-IDRI	-	108 82	否	93.30 90.10	管电流：30~197 mA，切片厚度：0.625 mm 管电流：40~582 mA，切片厚度：0.625~3.000 mm
传统肺结节分割方法	文献[30]	LIDC-IDRI LC015	-	2 651 1 186	否	69.90 76.00	-
基于深度学习的肺结节分割方法	文献[20]	LIDC-IDRI	64×64×32	728	否	88.18	切片厚度≤2.5 mm、肺结节直径≥3 mm
	文献[7]	LIDC-IDRI 私有	256×256×3	2 635 3 200	5-折交叉	82.05 81.61	依据LIDC结节尺寸报告由至少2名经验丰富的医生筛选
	文献[1]	LIDC-IDRI	48×48×16	1 074	否	80.50	至少3名医生进行标注
	文献[3]	LIDC-IDRI	64×64×64	-	否	83.00	随机选取300个CT文件
	文献[8]	Luna16 ILND	64×64×32	835 200	否	80.74 76.36	切片厚度≤2.5 mm、肺结节直径≥3 mm 丢弃切片数量太少或数据不完整的CT
	文献[5]	LIDC-IDRI	64×64×64	1 979	否	86.75	肺结节直径≥3 mm
	文献[6]	Luna16	64×64×32	1 086	否	79.60	切片厚度≤2.5 mm、肺结节直径≥3 mm
	文献[4]	LIDC-IDRI	64×64×64	2 885	否	75.00	7 mm肺结节直径≤45 mm
	文献[19]	LIDC-IDRI	-	1 131	否	80.89	肺结节直径≥3 mm
	本文方法	LIDC-IDRI	64×64×64	1 186	5-折交叉	83.83	切片厚度≤2.5 mm、肺结节直径≥3 mm

方法		数据集	输入尺寸	样本数量	交叉验证	DSC	样本选取策略
传统肺结节分割方法	文献[29]	模拟数据 LIDC-IDRI	-	108 82	否	93.30 90.10	管电流：30~197 mA，切片厚度：0.625 mm 管电流：40~582 mA，切片厚度：0.625~3.000 mm
传统肺结节分割方法	文献[30]	LIDC-IDRI LC015	-	2 651 1 186	否	69.90 76.00	-
基于深度学习的肺结节分割方法	文献[20]	LIDC-IDRI	64×64×32	728	否	88.18	切片厚度≤2.5 mm、肺结节直径≥3 mm
	文献[7]	LIDC-IDRI 私有	256×256×3	2 635 3 200	5-折交叉	82.05 81.61	依据LIDC结节尺寸报告由至少2名经验丰富的医生筛选
	文献[1]	LIDC-IDRI	48×48×16	1 074	否	80.50	至少3名医生进行标注
	文献[3]	LIDC-IDRI	64×64×64	-	否	83.00	随机选取300个CT文件
	文献[8]	Luna16 ILND	64×64×32	835 200	否	80.74 76.36	切片厚度≤2.5 mm、肺结节直径≥3 mm 丢弃切片数量太少或数据不完整的CT
	文献[5]	LIDC-IDRI	64×64×64	1 979	否	86.75	肺结节直径≥3 mm
	文献[6]	Luna16	64×64×32	1 086	否	79.60	切片厚度≤2.5 mm、肺结节直径≥3 mm
	文献[4]	LIDC-IDRI	64×64×64	2 885	否	75.00	7 mm肺结节直径≤45 mm
	文献[19]	LIDC-IDRI	-	1 131	否	80.89	肺结节直径≥3 mm
	本文方法	LIDC-IDRI	64×64×64	1 186	5-折交叉	83.83	切片厚度≤2.5 mm、肺结节直径≥3 mm

方法		评估指标
方法		PRE	SEN	DSC	mIoU	Param	FLOPs
UNet		82.16	81.99	79.85	84.00	16.32	237.01
YNet		80.27	83.87	79.53	83.71	33.28	297.05
UNet++		83.49	84.32	82.27	85.37	9.64	74.54
WingsNet		83.02	84.79	82.44	85.42	1.47	38.55
ReconNet		82.94	82.22	80.46	84.34	4.08	59.64
PCAMNet		82.83	85.10	82.57	85.51	9.44	12.30
采用Transformer技术的3D分割模型	TransBTS	83.22	78.93	78.67	83.05	30.95	32.68
	Unetr	83.57	79.96	79.34	83.49	92.34	21.49
	ASA	82.17	84.57	81.50	84.86	85.29	52.86
本文方法		84.15	86.03	83.83	86.40	44.70	101.72

方法		评估指标
方法		PRE	SEN	DSC	mIoU	Param	FLOPs
UNet		82.16	81.99	79.85	84.00	16.32	237.01
YNet		80.27	83.87	79.53	83.71	33.28	297.05
UNet++		83.49	84.32	82.27	85.37	9.64	74.54
WingsNet		83.02	84.79	82.44	85.42	1.47	38.55
ReconNet		82.94	82.22	80.46	84.34	4.08	59.64
PCAMNet		82.83	85.10	82.57	85.51	9.44	12.30
采用Transformer技术的3D分割模型	TransBTS	83.22	78.93	78.67	83.05	30.95	32.68
	Unetr	83.57	79.96	79.34	83.49	92.34	21.49
	ASA	82.17	84.57	81.50	84.86	85.29	52.86
本文方法		84.15	86.03	83.83	86.40	44.70	101.72

方法	En₁/De₁	En₂/De₂	En₃/De₃	En₄/De₄	瓶颈层	Params	FLOPs	PRE	SEN	DSC	mIoU
Base	RSU₇	RSU₆	RSU₅	RSU₄	RSU_4F	51.52	133.32	83.91	84.41	82.77	85.67
Base_SIPU	SIPU(U₇)	RSU₆	RSU₅	RSU₄	RSU_4F	51.52	133.30	84.99	83.64	82.96	85.78
Base_Uall	SIPU	U₆	U₅	U₄	U₄F	51.52	133.30	83.36	85.57	83.08	85.87

基于通道残差嵌套U结构的CT影像肺结节分割方法

CT image segmentation of lung nodules based on channel residual nested U structure

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方法	En₁/De₁	Params	FLOPs	PRE	SEN	DSC	mIoU
UNet	基本块由2个CBR构成	16.32	237.01	82.16	81.99	79.85	84.00
ResidualUNet^[31]	基本块由2个CBR和残差结构构成	141.24	398.60	84.82	82.17	81.85	85.13
Base_U	基本块由一个CBR构成	8.21	43.68	82.54	84.17	81.44	84.86
Base_Res	基本块基础上添加残差连接	15.00	72.71	34.27	78.26	32.79	44.65
Base_CR	基本块基础上添加通道残差结构	8.45	50.30	84.91	84.17	83.04	85.93

方法	En₁/De₁	En₂/De₂	En₃/De₃	En₄/De₄	瓶颈层	Params	FLOPs	PRE	SEN	DSC	mIoU
Base_{CRSU_RSU4F}	SIPU	CRSU₆	CRSU₅	CRSU₄	RSU_4F	42.03	101.55	85.24	83.91	83.24	85.99
Base_{CRU_RSU4F}	SIPU	CRU₆	CRU₅	CRU₄	RSU_4F	42.03	101.55	84.28	84.62	83.11	85.87
Base_{CRSU_CRSU4F}	SIPU	CRSU₆	CRSU₅	CRSU₄	CRSU_4F	39.40	101.38	84.35	84.71	83.24	85.97

方法	En₁/De₁	En₂/De₂	En₃/De₃	En₄/De₄	瓶颈层		Params	FLOPs	PRE	SEN	DSC	mIoU
Base_4F	-	-	-	-	CRSU_4F		39.40	101.38	84.35	84.71	83.24	85.97
Base_CEU	-	-	-	-	CEU		39.82	101.40	84.37	84.73	83.27	85.99
Base_{4F_CA}	-	-	-	-	CRSU_4F	CA	39.40	101.38	84.49	84.77	83.31	86.02
Base_{4F_ResCBAM}	-	-	-	-	CRSU_4F	ResCBAM	42.95	101.60	85.14	84.31	83.42	86.12
Base_{4F_CBAM}^[32]	SIPU	CRSU₆	CRSU₅	CRSU₄	CRSU_4F	CBAM	39.41	101.38	84.21	84.31	82.92	85.76
Base_{4F_SE}^[33]	-	-	-	-	CRSU_4F	SE	39.40	101.38	83.55	84.29	82.52	85.47
Base_{4F_SK}^[34]	-	-	-	-	CRSU_4F	SK	40.49	101.44	83.37	84.87	82.73	85.62
Base_{4F_ResNeSt}^[35]	-	-	-	-	CRSU_4F	ResNeSt	39.40	101.38	85.23	84.17	83.41	86.11
CR U²Net	-	-	-	-	CRSU_4F	CEU	44.70	101.72	84.15	86.03	83.83	86.40

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