Neural Radiance Field (NeRF) Models for Ultrasound Images

超声图像的神经辐射场 (NeRF) 模型

• 批准号: 2714693
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2714693
• 关键词: Neural; Radiance; Field; NeRF; Models

Brief description of the context of the research including potential impact:Neural Radiance Fields (NeRFs) are a new class of deep networks that provide photorealistic 3D reconstruction and rendering of natural scenes, given only a few input photos. The resulting renders include realistic reflections, lighting and other material properties with an accuracy that was previously not possible. Given its impressive performance, it is natural to ask whether it may apply to different input modalities. Medical ultrasound is a relatively low-cost and accessible medical imaging hardware that can be used to flag a number of pathologies and abnormal changes, including cancer, without any radiation risk. However, the produced images are hard to interpret and so require highly trained professionals. Allowing technicians (as opposed to only highly-trained professionals) to automatically flag abnormalities would have a high impact in early disease screening across many areas. This project will investigate the use of NeRFs to reconstruct organs in 3D from weakly-localized ultrasound images, whether in foetuses or adults. It will also explore deformable matching (e.g. with contrastive learning) to register scans and thus automatically flag abnormal tissue evolution. Aims and Objectives:To enhance the capabilities of the very simple and ubiquitous 2D Ultrasound scanner through software.The software should not only aid highly trained Doctors in interpreting ultrasound images, but permit lesser trained healthcare workers to also undertake and interpret effective ultrasound scans. The applications of this work should ideally be multiple, for example increasing detection rates of abnormalities in foetuses, or to aid detection of cancer in adults.Novelty of the research methodology - Applying and adapting novel and cutting-edge techniques from Computer Vision to the Medical field, where images are often of much lower quality, the priorities are different and the challenges are different such as anatomical complexity and practical requirements (scanning times and acquisition rates).Alignment to EPSRC's strategies and research areas (which EPSRC research area the project relates to):Image and vision computingMedical imagingAny companies or collaborators involved - None

对研究背景的简要描述，包括潜在的影响：神经辐射场(NERF)是一种新的深度网络，它在给定少量输入照片的情况下，提供照片级逼真的3D重建和自然场景的渲染。生成的渲染包括逼真的反射、照明和其他材质属性，其精确度在以前是不可能的。鉴于其令人印象深刻的表现，人们自然会问，它是否可能适用于不同的输入模式。医用超声是一种相对低成本和可获得的医学成像硬件，可用于标记包括癌症在内的许多病理和异常变化，而不存在任何辐射风险。然而，制作的图像很难解读，因此需要训练有素的专业人员。允许技术人员(而不仅仅是训练有素的专业人员)自动标记异常将对许多地区的早期疾病筛查产生很大影响。该项目将研究使用NERFS从弱局部超声图像中重建3D器官，无论是在胎儿还是成人中。它还将探索可变形匹配(例如，使用对比学习)来记录扫描，从而自动标记异常组织进化。目的和目的：通过软件增强非常简单和普遍存在的二维超声扫描仪的能力。软件不仅应该帮助训练有素的医生解释超声图像，而且允许训练有素的医护人员也进行和解释有效的超声扫描。这项工作的应用最好是多方面的，例如提高胎儿异常的检测率，或者帮助成年人检测癌症。研究方法的创新-将计算机视觉的新的和尖端的技术应用于医学领域，在医学领域，图像的质量往往要低得多，优先级不同，挑战也不同，例如解剖的复杂性和实际要求(扫描时间和采集率)。与EPSRC的战略和研究领域(该项目涉及的EPSRC研究领域)保持一致：图像和视觉计算医学图像任何公司或合作者都参与-没有