A novel LVPA-UNet network for target volume automatic delineation: an MRI case study of nasopharyngeal carcinoma

鼻咽癌 体积热力学 医学 肿瘤科 核医学 放射科 放射治疗 物理 量子力学
作者
Yu Zhang,Haoran Xu,Junhao Wen,Yujun Hu,Yinliang Diao,Junliang Chen,Yunfei Xia
出处
期刊:Heliyon [Elsevier BV]
卷期号:: e30763-e30763
标识
DOI:10.1016/j.heliyon.2024.e30763
摘要

Accurate delineation of Gross Tumor Volume (GTV) is crucial for radiotherapy. Deep learning-driven GTV segmentation technologies excel in rapidly and accurately delineating GTV, providing a basis for radiologists in formulating radiation plans. The existing 2D and 3D segmentation models of GTV based on deep learning are limited by the loss of spatial features and anisotropy respectively, and are both affected by the variability of tumor characteristics, blurred boundaries, and background interference. All these factors seriously affect the segmentation performance. To address the above issues, a Layer-Volume Parallel Attention (LVPA)-UNet model based on 2D-3D architecture has been proposed in this study, in which three strategies are introduced. Firstly, 2D and 3D workflows are introduced in the LVPA-UNet. They work in parallel and can guide each other. Both the fine features of each slice of 2D MRI and the 3D anatomical structure and spatial features of the tumor can be extracted by them. Secondly, parallel multi-branch depth-wise strip convolutions adapt the model to tumors of varying shapes and sizes within slices and volumetric spaces, and achieve refined processing of blurred boundaries. Lastly, a Layer-Channel Attention mechanism is proposed to adaptively adjust the weights of slices and channels according to their different tumor information, and then to highlight slices and channels with tumor. The experiments by LVPA-UNet on 1010 nasopharyngeal carcinoma (NPC) MRI datasets from three centers show a DSC of 0.7907, precision of 0.7929, recall of 0.8025, and HD95 of 1.8702 mm, outperforming eight typical models. Compared to the baseline model, it improves DSC by 2.14 %, precision by 2.96 %, and recall by 1.01 %, while reducing HD95 by 0.5434 mm. Consequently, while ensuring the efficiency of segmentation through deep learning, LVPA-UNet is able to provide superior GTV delineation results for radiotherapy and offer technical support for precision medicine.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
好吃鱼完成签到,获得积分10
刚刚
等一个晴天完成签到,获得积分10
3秒前
胡涂涂发布了新的文献求助10
5秒前
研友_VZG7GZ应助小赞采纳,获得10
6秒前
量子星尘发布了新的文献求助10
7秒前
快乐一江完成签到,获得积分10
9秒前
10秒前
15秒前
啊标完成签到,获得积分10
15秒前
15秒前
水溶c100完成签到,获得积分10
16秒前
梨江鱼完成签到,获得积分10
16秒前
LcnTCM完成签到,获得积分10
16秒前
19秒前
maizhenpeng发布了新的文献求助10
20秒前
执着乐双发布了新的文献求助30
22秒前
小赞发布了新的文献求助10
22秒前
26秒前
30秒前
沈静完成签到,获得积分10
30秒前
32秒前
loski发布了新的文献求助10
32秒前
marina完成签到 ,获得积分20
33秒前
36秒前
时尚俊驰发布了新的文献求助10
36秒前
37秒前
文献发布了新的文献求助30
38秒前
41秒前
我嘞个豆完成签到,获得积分10
41秒前
爱笑晓曼发布了新的文献求助10
42秒前
wdy111应助sc采纳,获得20
42秒前
敏感初露发布了新的文献求助10
42秒前
隐形曼青应助机智思真采纳,获得10
45秒前
思源应助时尚俊驰采纳,获得10
45秒前
可爱的函函应助敏感初露采纳,获得10
45秒前
46秒前
爆米花应助橙子采纳,获得10
49秒前
量子星尘发布了新的文献求助10
51秒前
阿满完成签到 ,获得积分10
52秒前
王馨雨完成签到,获得积分10
53秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3989378
求助须知:如何正确求助?哪些是违规求助? 3531442
关于积分的说明 11254002
捐赠科研通 3270126
什么是DOI,文献DOI怎么找? 1804887
邀请新用户注册赠送积分活动 882087
科研通“疑难数据库(出版商)”最低求助积分说明 809173