已入深夜,您辛苦了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!祝你早点完成任务,早点休息,好梦!

Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study

概化理论 分割 深度学习 人工智能 公制(单位) 医学物理学 医学 临床实习 头颈部 计算机科学 放射治疗计划 数据集 放射治疗 机器学习 放射科 外科 物理疗法 统计 数学 运营管理 经济
作者
Stanislav Nikolov,Sam Blackwell,Alexei Zverovitch,R. Mendes,Michelle Livne,Jeffrey De Fauw,Yojan Patel,Clemens Meyer,Harry Askham,Bernardino Romera‐Paredes,Christopher Kelly,Alan Karthikesalingam,Carlton Chu,Dawn Carnell,C.S. Boon,D. D’Souza,Syed Moinuddin,Bethany Garie,Yasmin McQuinlan,Sarah Ireland,Kiarna Hampton,Krystle Fuller,Hugh Montgomery,Geraint Rees,Mustafa Suleyman,Trevor Back,Cían Hughes,Joseph R. Ledsam,Olaf Ronneberger
出处
期刊:Journal of Medical Internet Research [JMIR Publications]
卷期号:23 (7): e26151-e26151 被引量:241
标识
DOI:10.2196/26151
摘要

Over half a million individuals are diagnosed with head and neck cancer each year globally. Radiotherapy is an important curative treatment for this disease, but it requires manual time to delineate radiosensitive organs at risk. This planning process can delay treatment while also introducing interoperator variability, resulting in downstream radiation dose differences. Although auto-segmentation algorithms offer a potentially time-saving solution, the challenges in defining, quantifying, and achieving expert performance remain.Adopting a deep learning approach, we aim to demonstrate a 3D U-Net architecture that achieves expert-level performance in delineating 21 distinct head and neck organs at risk commonly segmented in clinical practice.The model was trained on a data set of 663 deidentified computed tomography scans acquired in routine clinical practice and with both segmentations taken from clinical practice and segmentations created by experienced radiographers as part of this research, all in accordance with consensus organ at risk definitions.We demonstrated the model's clinical applicability by assessing its performance on a test set of 21 computed tomography scans from clinical practice, each with 21 organs at risk segmented by 2 independent experts. We also introduced surface Dice similarity coefficient, a new metric for the comparison of organ delineation, to quantify the deviation between organ at risk surface contours rather than volumes, better reflecting the clinical task of correcting errors in automated organ segmentations. The model's generalizability was then demonstrated on 2 distinct open-source data sets, reflecting different centers and countries to model training.Deep learning is an effective and clinically applicable technique for the segmentation of the head and neck anatomy for radiotherapy. With appropriate validation studies and regulatory approvals, this system could improve the efficiency, consistency, and safety of radiotherapy pathways.
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
刚刚
开心的野狼完成签到 ,获得积分10
1秒前
整齐的忆彤完成签到,获得积分10
3秒前
4秒前
liyb完成签到,获得积分10
4秒前
LiXingchen完成签到,获得积分10
4秒前
詹卫卫发布了新的文献求助10
5秒前
洁白的故人完成签到 ,获得积分10
8秒前
笔墨今宵完成签到 ,获得积分10
9秒前
11秒前
笔墨今宵关注了科研通微信公众号
14秒前
雷马完成签到,获得积分10
14秒前
15秒前
luwenxuan发布了新的文献求助10
16秒前
Mono完成签到 ,获得积分10
17秒前
雷马发布了新的文献求助10
17秒前
18秒前
Yuki发布了新的文献求助10
19秒前
钉钉完成签到 ,获得积分10
19秒前
21秒前
2224536发布了新的文献求助30
22秒前
鸭梨发布了新的文献求助10
23秒前
香蕉觅云应助luwenxuan采纳,获得10
24秒前
More完成签到,获得积分10
29秒前
栗米ki完成签到,获得积分10
29秒前
别当真完成签到 ,获得积分10
32秒前
源缘完成签到 ,获得积分10
34秒前
野性的小松鼠完成签到 ,获得积分10
36秒前
朴实的小萱完成签到,获得积分10
37秒前
42秒前
深情安青应助多年以后采纳,获得10
43秒前
44秒前
gungun完成签到,获得积分10
47秒前
凤凰山完成签到,获得积分10
47秒前
直率的画笔完成签到,获得积分10
47秒前
48秒前
恋雅颖月应助1234采纳,获得10
48秒前
黄晃晃发布了新的文献求助10
49秒前
NexusExplorer应助科研通管家采纳,获得10
50秒前
51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
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
Aktuelle Entwicklungen in der linguistischen Forschung 300
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 3989949
求助须知:如何正确求助?哪些是违规求助? 3532017
关于积分的说明 11255865
捐赠科研通 3270829
什么是DOI,文献DOI怎么找? 1805053
邀请新用户注册赠送积分活动 882233
科研通“疑难数据库(出版商)”最低求助积分说明 809216