Machine Learning Inspired Physical Models in Organs

机器学习启发了器官的物理模型

• 批准号: 10544288
• 负责人: Bilyana Tzolova
• 金额: $ 4.68万
• 依托单位: RICE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10544288
• 关键词: 3-Dimensional; Adenomyosis; Algorithms; Arteriovenous malformation; Automation; Benign Prostatic Hypertrophy; Blood Vessels; Blood flow; Breast; Cardiovascular system; Catheters; Cerebral Aneurysm; Chemicals; Clinical; Colorectal; Computer Models; Computing Methodologies; Coupled; Data; Deep Vein Thrombosis; Development; Diagnosis; Diagnostic; Diagnostic Imaging; Disease; Drainage procedure; Fistula; Freedom; Goals; Hemodialysis; Human; Hyperplasia; Image; Individual; Islet Cell Tumor; Joints; Laws; Liver; Location; Machine Learning; Malignant Neoplasms; Malignant neoplasm of liver; Manuals; Mathematics; Medical; Medical Imaging; Metastatic to; Methods; Modeling; Motivation; Nature; Noise; Operative Surgical Procedures; Organ; Patient Care; Patient-Focused Outcomes; Patients; Physician' s Role; Physicians; Physics; Play; Primary carcinoma of the liver cells; Procedures; Process; Pulmonary Embolism; Research; Rest; Rice; Role; Scanning; Stenosis; Structure; Techniques; Therapeutic; Therapeutic Embolization; Time; Training; Translating; Transportation; Universities; University of Texas M D Anderson Cancer Center; Uterine Fibroids; Vascular System; Vascular blood supply; Venous; Work; X-Ray Computed Tomography; accurate diagnosis; base; cost; deep learning; deep learning algorithm; deep neural network; disease diagnosis; experience; image processing; imaging Segmentation; imaging modality; improved; insight; method development; models and simulation; neural network; neural network algorithm; neural network architecture; novel; physical model; radiologist; solute; treatment planning; tumor

PROJECT SUMMARY The vascular system plays a crucial role in diagnostics, treatment, and surgical planning in a wide array of diseases. Historically, practitioners locate vessel manually on each image of a CT scan. This is a tedious process that can vary highly depending on the individual's experience and ability. Recently, there has been motivation to automate this process to save time and increase accuracy. This process, vessel segmentation, is challenging because of the small size of the vessel structure and the varying contrast and noise in medical images. Current image processing techniques have not been successful in resolving the full vascular systems in humans because of these challenges. However, a novel neural network algorithm has shown potential to reduce training times and increase accuracy per degree of freedom in medical imaging segmentation. Applying this algorithm in the liver vessel segmentation, and eventually other organs' vascular system segmentation shows great promise. In addition to achieving successful vessel segmentation of the full vascular system, there is motivation to create a model that simulates blood flow and mass transportation in the vascular system. This is accomplished by using coupled multidimensional computational models for the flow and transport within the blood vessels. The combination of these two aims will give a complete overview of the location and function of a patient's circulatory system. This research will be completed by the joint effort of the Computational and Applied Mathematics Department at Rice University and the Department of Imaging Physics, Division of Diagnostic Imaging at The University of Texas MD Anderson Cancer Center. The collaborative nature of this project allows mathematicians to work with physicians who are experienced in the diagnosis and treatment of many diseases. Leveraging everyone's strengths and background will allow for a successful development and implementation of this project.

项目概要 血管系统在广泛的诊断、治疗和手术计划中发挥着至关重要的作用 从历史上看，医生在 CT 扫描的每张图像上手动定位血管，这是一项繁琐的工作。 最近，出现了这种情况，具体取决于个人的经验和能力。 自动化该过程以节省时间并提高准确性的动机，血管分割， 由于血管结构尺寸小以及医学中不同的对比度和噪声，因此具有挑战性 当前的图像处理技术尚未成功地解析完整的血管系统。 然而，一种新颖的神经网络算法已显示出减少这些挑战的潜力。 训练时间并提高医学成像分割中每个自由度的准确性。 肝脏血管分割中的算法，最终其他器官的血管系统分割显示 除了成功实现整个血管系统的血管分割之外，还有巨大的前景。 创建模拟血管系统中的血流和物质运输的模型的动机。 通过使用血液内流动和运输的耦合多维计算模型来完成 这两个目标的结合将提供对位置和功能的完整概述。 这项研究将由计算部门的共同努力完成。 莱斯大学应用数学系和成像物理系 德克萨斯大学 MD 安德森癌症中心的诊断成像 协作性质。 该项目允许数学家与在诊断和治疗方面经验丰富的医生合作 利用每个人的优势和背景将能够成功地发展和治疗许多疾病。 该项目的实施。