Automatic Organ Segmentation Tool for Radiation Treatment Planning of Cancers
用于癌症放射治疗计划的自动器官分割工具
基本信息
- 批准号:10518374
- 负责人:
- 金额:$ 4.95万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2022
- 资助国家:美国
- 起止时间:2022-02-01 至 2023-07-31
- 项目状态:已结题
- 来源:
- 关键词:AddressAdoptedAdoptionAlgorithmsAmericanAreaAtlasesAttentionCancer PatientChestClinicalComputer Vision SystemsComputer softwareConsumptionDevelopmentDoseEarly DiagnosisEnvironmentHealthcareHeterogeneityHourHumanIntraobserver VariabilityLabelMalignant NeoplasmsManualsMedicalMedicineMethodsOrganOutcomePatient-Focused OutcomesPerformanceProcessRadiation Dose UnitRadiation therapyRiskScanningSiteSliceSurvival RateTimeToxic effectTreatment CostX-Ray Computed Tomographyautomated segmentationbasecancer radiation therapycancer therapycloud basedcostdeep learningdosimetryimprovedlearning strategymillimetersegmentation algorithmsimulationsuccesstooltreatment planningusabilityvalidation studies
项目摘要
ABSTRACT
As early detection and better treatment have increased cancer patient survival rates, the importance of
protecting normal organs during radiation treatment is drawing more attention, which is critical in reducing long
term toxicity of cancers. To avoid excessively high radiation doses to organs-at-risk (OARs), OARs need to be
correctly segmented from simulation computed tomography (CT) scans during radiation treatment planning to
get an accurate dose distribution. Despite tremendous effort in developing semi- or fully-automatic
segmentation solutions, current automated segmentation software, mostly using the atlas-based methods, has
not yet reached the level of accuracy and robustness required for clinical usage. Therefore, in current practice,
significant manual efforts are still required in the OAR segmentation process. Manual contouring suffers from
inter- and intra-observer variability, as well as institutional variability where different sites adopt distinct
contouring atlases and labeling criteria, thus leading to inaccuracy and variability in OAR segmentation. When
OARs are very close to the treatment target, segmentation errors as small as a few millimeters can have a
statistically significant impact on dosimetry distribution and outcome. In addition, it is also costly and time
consuming as it can take 1-2 hours of a clinicians’ time to segment major thoracic organs due to the large
number of axial slices required. In summary, an accurate and fast process for segmenting OARs in treatment
planning using CT scans is needed for improving patient outcomes and reducing the cost of radiation therapy
of cancers. In recent years, the rapid development of deep learning methods has revolutionized many
computer-vision areas and the adoption of deep learning in medical applications has shown great success.
Based on a deep-learning-based algorithm we developed that achieved better-than-human performance and
ranked 1st in 2017 American Association of Physicist in Medicine Thoracic Auto-segmentation Challenge, an
automatic OAR segmentation product will be developed in this project with the three aims: 1) further improve
the performance and robustness of OAR segmentation algorithms, focusing on addressing the heterogeneity
issue of different clinical environments; 2) further enrich the functionalities and enhance usability of the cloud-
based software product; and 3) perform clinical validation study on the algorithm performance and software
usability at collaborating sites. With this product, the segmentation accuracy can be improved, leading to more
robust treatment plans in protecting normal organs and improved long term patient outcome. The time and cost
of radiation treatment planning can be greatly reduced, contributing to a more affordable cancer treatment and
reduced healthcare burden.
摘要
项目成果
期刊论文数量(8)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Improving accuracy and robustness of deep convolutional neural network based thoracic OAR segmentation.
- DOI:10.1088/1361-6560/ab7877
- 发表时间:2020-03-31
- 期刊:
- 影响因子:3.5
- 作者:Feng X;Bernard ME;Hunter T;Chen Q
- 通讯作者:Chen Q
Head and neck tumor segmentation convolutional neural network robust to missing PET/CT modalities using channel dropout.
- DOI:10.1088/1361-6560/accac9
- 发表时间:2023-04-25
- 期刊:
- 影响因子:3.5
- 作者:
- 通讯作者:
Convolutional neural network enhancement of fast-scan low-dose cone-beam CT images for head and neck radiotherapy.
- DOI:10.1088/1361-6560/ab6240
- 发表时间:2020-01-27
- 期刊:
- 影响因子:3.5
- 作者:Yuan N;Dyer B;Rao S;Chen Q;Benedict S;Shang L;Kang Y;Qi J;Rong Y
- 通讯作者:Rong Y
Contouring quality assurance methodology based on multiple geometric features against deep learning auto-segmentation.
基于多个几何特征和深度学习自动分割的轮廓质量保证方法。
- DOI:10.1002/mp.16299
- 发表时间:2023
- 期刊:
- 影响因子:3.8
- 作者:Duan,Jingwei;Bernard,MarkE;Castle,JamesR;Feng,Xue;Wang,Chi;Kenamond,MarkC;Chen,Quan
- 通讯作者:Chen,Quan
Development of a virtual source model for Monte Carlo-based independent dose calculation for varian linac.
- DOI:10.1002/acm2.13556
- 发表时间:2022-05
- 期刊:
- 影响因子:2.1
- 作者:
- 通讯作者:
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Xue Feng其他文献
PTPN22-1123G C polymorphism is associated with susceptibility to primary immune thrombocytopenia in Chinese population
PTPN22-1123G
- DOI:
- 发表时间:
2013 - 期刊:
- 影响因子:3.3
- 作者:
Ge Jing;Li Huiyuan;Gu Dongsheng;Du Weiting;Xue Feng;Sui Tao;Xu Jianhui;Yang Renchi - 通讯作者:
Yang Renchi
Xue Feng的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('Xue Feng', 18)}}的其他基金
Improved Diagnosis of Shunt Malfunction with Automatic Quantification of Ventricular Space
通过心室空间自动量化改进分流故障的诊断
- 批准号:
10384590 - 财政年份:2022
- 资助金额:
$ 4.95万 - 项目类别:
Automatic Organ Segmentation Tool for Radiation Treatment Planning of Cancers
用于癌症放射治疗计划的自动器官分割工具
- 批准号:
10221655 - 财政年份:2019
- 资助金额:
$ 4.95万 - 项目类别:
Automatic Organ Segmentation Tool for Radiation Treatment Planning of Cancers
用于癌症放射治疗计划的自动器官分割工具
- 批准号:
10081752 - 财政年份:2019
- 资助金额:
$ 4.95万 - 项目类别:
相似海外基金
How novices write code: discovering best practices and how they can be adopted
新手如何编写代码:发现最佳实践以及如何采用它们
- 批准号:
2315783 - 财政年份:2023
- 资助金额:
$ 4.95万 - 项目类别:
Standard Grant
One or Several Mothers: The Adopted Child as Critical and Clinical Subject
一位或多位母亲:收养的孩子作为关键和临床对象
- 批准号:
2719534 - 财政年份:2022
- 资助金额:
$ 4.95万 - 项目类别:
Studentship
A comparative study of disabled children and their adopted maternal figures in French and English Romantic Literature
英法浪漫主义文学中残疾儿童及其收养母亲形象的比较研究
- 批准号:
2633211 - 财政年份:2020
- 资助金额:
$ 4.95万 - 项目类别:
Studentship
A material investigation of the ceramic shards excavated from the Omuro Ninsei kiln site: Production techniques adopted by Nonomura Ninsei.
对大室仁清窑遗址出土的陶瓷碎片进行材质调查:野野村仁清采用的生产技术。
- 批准号:
20K01113 - 财政年份:2020
- 资助金额:
$ 4.95万 - 项目类别:
Grant-in-Aid for Scientific Research (C)
A comparative study of disabled children and their adopted maternal figures in French and English Romantic Literature
英法浪漫主义文学中残疾儿童及其收养母亲形象的比较研究
- 批准号:
2436895 - 财政年份:2020
- 资助金额:
$ 4.95万 - 项目类别:
Studentship
A comparative study of disabled children and their adopted maternal figures in French and English Romantic Literature
英法浪漫主义文学中残疾儿童及其收养母亲形象的比较研究
- 批准号:
2633207 - 财政年份:2020
- 资助金额:
$ 4.95万 - 项目类别:
Studentship
The limits of development: State structural policy, comparing systems adopted in two European mountain regions (1945-1989)
发展的限制:国家结构政策,比较欧洲两个山区采用的制度(1945-1989)
- 批准号:
426559561 - 财政年份:2019
- 资助金额:
$ 4.95万 - 项目类别:
Research Grants
Securing a Sense of Safety for Adopted Children in Middle Childhood
确保被收养儿童的中期安全感
- 批准号:
2236701 - 财政年份:2019
- 资助金额:
$ 4.95万 - 项目类别:
Studentship
A Study on Mutual Funds Adopted for Individual Defined Contribution Pension Plans
个人设定缴存养老金计划采用共同基金的研究
- 批准号:
19K01745 - 财政年份:2019
- 资助金额:
$ 4.95万 - 项目类别:
Grant-in-Aid for Scientific Research (C)
Structural and functional analyses of a bacterial protein translocation domain that has adopted diverse pathogenic effector functions within host cells
对宿主细胞内采用多种致病效应功能的细菌蛋白易位结构域进行结构和功能分析
- 批准号:
415543446 - 财政年份:2019
- 资助金额:
$ 4.95万 - 项目类别:
Research Fellowships