Neural Radiance Field (NeRF) Models for Ultrasound Images

简要描述研究背景，包括潜在影响：神经辐射场（nerf）是一种新型的深度网络，仅提供少量输入照片，即可提供逼真的3D重建和自然场景渲染。由此产生的渲染包括真实的反射、照明和其他材料属性，其准确性以前是不可能的。鉴于其令人印象深刻的表现，人们自然会问它是否适用于不同的输入方式。医学超声是一种成本相对较低且易于使用的医学成像硬件，可用于标记许多病理和异常变化，包括癌症，而没有任何辐射风险。然而，产生的图像很难解释，因此需要训练有素的专业人员。允许技术人员（而不是仅仅训练有素的专业人员）自动标记异常，将对许多领域的早期疾病筛查产生重大影响。该项目将研究利用nerf从弱定位超声图像中重建三维器官，无论是胎儿还是成人。它还将探索可变形匹配（例如，对比学习）来注册扫描，从而自动标记异常组织进化。目的和目标：通过软件提高非常简单和无处不在的二维超声扫描仪的能力。该软件不仅应该帮助训练有素的医生解释超声图像，而且允许训练较差的医护人员也承担和解释有效的超声扫描。这项工作的理想应用应该是多方面的，例如提高胎儿异常的检出率，或者帮助检测成人的癌症。研究方法的新颖性-将计算机视觉的新颖尖端技术应用和适应医学领域，其中图像质量通常要低得多，优先级不同，挑战也不同，例如解剖复杂性和实际要求（扫描时间和采集率）。符合EPSRC的战略和研究领域（该项目与EPSRC的研究领域相关）：图像和视觉计算医学成像任何涉及的公司或合作者-无

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