Predicting premature birth from MRI using deep learning
使用深度学习通过 MRI 预测早产
基本信息
- 批准号:2606532
- 负责人:
- 金额:--
- 依托单位:
- 依托单位国家:英国
- 项目类别:Studentship
- 财政年份:2021
- 资助国家:英国
- 起止时间:2021 至 无数据
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
Aim of the PhD Project:Deep Generative Modelling for prediction of gestation and weight at birth from fetal MRI and birth metricsEmphasis on the use of model interpretation to improve understanding of elements involved in preterm birth and late fetal growth restrictionLink prediction algorithm and acquisition to inform future protocol developments and thus facilitate clinical transition.Project Description:Human pregnancy and birth are among the most fascinating processes in life but also carry significant risks for both mother and unborn child. Consequences from preterm birth and fetal growth restriction are severe and lifelong [1].Human development occurs largely hidden: clinically available Ultrasound screening only catch glimpses into the womb. Advanced knowledge of the appropriate gestation when the birth should start, the size of the fetus, as well as the type of delivery most likely to occur, would allow for a step change in adequate preparation and monitoring of all but especially high-risk pregnancies.Recent advances in fetal MRI techniques [2,3] have successfully been able to assess the fetal organs and placenta both structurally as well as functionally. These show altered T2* values in pregnancies with fetal growth restriction and reduced lung volume in pregnancies threatened by preterm labour. Dynamic information such as the frequency of fetal motion and lung breathing exercises is an indicator for preterm birth. Fetal MRI scans contain a vast amount of information and typically cover the uterus in multiple planes. However, only subsets of the data acquired are currently analysed, e.g. only the brain structure or placental attachment. This is in parts due to the lack of appropriate analysis tools able to benefit from the full extent of obtained information.However, regardless of scan indication the questions of when the baby will be born and how much they will weigh is of high relevance for clinical planning of all births. A prediction for these questions would allow to optimize antenatal care and time point of delivery.Recent deep learning (DL) [4] techniques provide two important opportunities: They allow to seek patterns from complex image data sets without making simplifying assumptions, and at the same time can be tuned to return features from the images which are salient to answering these questions. Thus, the goal of this project is to use generative Deep Learning to develop PLATYPUS (Prediction of Length of gestation, Approximate weight and TYpe of delivery from Pregnancy whole-Uterus MRI Scanning: a tool for asking fundamental questions about each pregnancy, mode and time of delivery. A significant emphasis will be to leverage recent developments in model interpretability to inform improved acquisition strategies which will continuously map development in the last weeks before birth.The extensive database available from large scale fetal projects at King's provides the well characterized training data which is critical for deep learning.
博士项目目标:通过胎儿MRI和出生指标预测妊娠期和出生体重的深度生成模型重点是使用模型解释来提高对早产和晚期胎儿生长限制相关因素的理解链接预测算法和获取,为未来的方案开发提供信息,从而促进临床过渡。项目描述:人类怀孕和分娩是生命中最迷人的过程之一,但对母亲和未出生的孩子都有重大风险。早产和胎儿生长受限的后果是严重的和终生的。人类的发育在很大程度上是隐藏的:临床上可用的超声波检查只能瞥见子宫内的情况。对何时开始分娩的适当妊娠、胎儿的大小以及最有可能发生的分娩类型的先进知识,将允许在充分准备和监测所有妊娠(特别是高危妊娠)方面进行逐步改变。胎儿MRI技术的最新进展[2,3]已经成功地评估了胎儿器官和胎盘的结构和功能。这些结果表明,在胎儿生长受限的妊娠中T2*值发生改变,在有早产威胁的妊娠中肺容量减少。动态信息,如胎动频率和肺部呼吸练习是早产的一个指标。胎儿核磁共振扫描包含大量信息,通常在多个平面上覆盖子宫。然而,目前仅分析了所获得数据的子集,例如仅分析了大脑结构或胎盘附着。这部分是由于缺乏适当的分析工具,无法充分利用所获得的信息。然而,无论扫描显示婴儿何时出生以及他们的体重是与所有分娩的临床计划高度相关的问题。对这些问题的预测将允许优化产前护理和分娩时间点。最近的深度学习(DL)[4]技术提供了两个重要的机会:它们允许在不简化假设的情况下从复杂的图像数据集中寻找模式,同时可以调整为从图像中返回对回答这些问题重要的特征。因此,这个项目的目标是使用生成式深度学习来开发PLATYPUS(预测妊娠长度,怀孕全子宫MRI扫描的近似体重和分娩类型):一个关于每次怀孕,分娩方式和时间的基本问题的工具。一个重要的重点将是利用模型可解释性的最新发展,为改进的获取策略提供信息,这些策略将在出生前最后几周持续绘制发育图。来自King's大型胎儿项目的广泛数据库提供了表征良好的训练数据,这对深度学习至关重要。
项目成果
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其他文献
吉治仁志 他: "トランスジェニックマウスによるTIMP-1の線維化促進機序"最新医学. 55. 1781-1787 (2000)
Hitoshi Yoshiji 等:“转基因小鼠中 TIMP-1 的促纤维化机制”现代医学 55. 1781-1787 (2000)。
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LiDAR Implementations for Autonomous Vehicle Applications
- DOI:
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2021 - 期刊:
- 影响因子:0
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吉治仁志 他: "イラスト医学&サイエンスシリーズ血管の分子医学"羊土社(渋谷正史編). 125 (2000)
Hitoshi Yoshiji 等人:“血管医学与科学系列分子医学图解”Yodosha(涉谷正志编辑)125(2000)。
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Effect of manidipine hydrochloride,a calcium antagonist,on isoproterenol-induced left ventricular hypertrophy: "Yoshiyama,M.,Takeuchi,K.,Kim,S.,Hanatani,A.,Omura,T.,Toda,I.,Akioka,K.,Teragaki,M.,Iwao,H.and Yoshikawa,J." Jpn Circ J. 62(1). 47-52 (1998)
钙拮抗剂盐酸马尼地平对异丙肾上腺素引起的左心室肥厚的影响:“Yoshiyama,M.,Takeuchi,K.,Kim,S.,Hanatani,A.,Omura,T.,Toda,I.,Akioka,
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