Machine Learning Inspired Physical Models in Organs

项目总结 血管系统在诊断、治疗和外科手术计划中起着至关重要的作用。 疾病的威胁。在历史上，医生在CT扫描的每一张图像上手动定位血管。这是一个单调乏味的 根据个人的经验和能力，这一过程可能会有很大的不同。最近，有一种 自动化这一过程以节省时间和提高准确性的动机。这个过程，血管分割， 由于血管结构的小尺寸以及医学中不同的对比度和噪声，这是具有挑战性的 图像。目前的图像处理技术还不能成功地分辨出全血管系统。 人类因为这些挑战。然而，一种新的神经网络算法显示出降低成本的潜力 在医学图像分割中，训练次数和每自由度的准确率都有所提高。应用这一点 算法中对肝脏血管进行分割，并最终对其他器官的血管系统进行分割 很好的承诺。除了实现完整血管系统的成功血管分割外，还有 创建一个模型来模拟血液fl的流动和血管系统中的大量运输的动机。这是 通过使用fl和血液中传输的耦合多维计算模型来实现 船只。这两个目标的结合将给出一个完整的位置和功能概述 病人的循环系统。这项研究将由计算所的共同努力完成 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。它的协作性质 该项目允许数学家与在诊断和治疗方面有经验的医生合作 很多疾病。利用每个人的优势和背景将使一个成功的发展和 这一项目的实施。

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.