Machine Learning Inspired Physical Models in Organs

项目摘要 血管系统在诊断、治疗和手术计划中起着至关重要的作用 疾病。历史上，从业者在CT扫描的每个图像上手动定位血管。这是一个繁琐 这一过程可能因个人的经验和能力而有很大差异。最近有 自动化这一过程的动机，以节省时间和提高准确性。这个过程，血管分割， 由于血管结构的小尺寸以及医学中变化的对比度和噪声， 图像.目前的图像处理技术在解决全血管系统中还没有成功。 人类面临这些挑战。然而，一种新的神经网络算法已经显示出减少 训练时间，并提高医学成像分割中每自由度的准确度。应用该 算法在肝脏血管分割中的应用，最终将其他器官的血管系统分割显示出来 伟大的承诺。除了实现完整血管系统的成功血管分割外， 这是创建模拟血管系统中血液流动和质量输送的模型的动机。这是 通过使用血液内的流动和运输的耦合多维计算模型来实现 船舶.这两个目标的结合将给出一个完整的概述的位置和功能 病人的循环系统。这项研究将由计算科学家的共同努力完成。 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。这种合作的性质 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， 许多疾病。利用每个人的优势和背景将允许一个成功的发展， 这个项目的实施。

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.