SBIR Phase I: Semi-Autonomous Adaptive Neural and Genetic Segmentation of Medical Images

SBIR 第一阶段：医学图像的半自主自适应神经和遗传分割

• 批准号: 0839335
• 负责人: Kovalan Muniandy
• 金额: --
• 依托单位: KJAYA, LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-01-01 至 2009-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0839335&HistoricalAwards=false
• 关键词: SBIR; Phase; Semi; Autonomous; Adaptive

This Small Business Innovative Research (SBIR) Phase I project will implement a physician-assisted, real-time adaptive system for the segmentation of anatomical structures in 3D medical image data. Medical image segmentation seeks to change the representation of an anatomical structure, making it more easily analyzed. Because of the extreme variability of these structures in biological systems, current idiosyncratic manual methods currently in use are tedious, time consuming, and error prone. Image segmentation cannot in general be programmatically solved. The proposed system is a Neural Network (NN) based adaptation of the individual data using parallel Graphics Processing Units (GPUs) and coupled with a Genetic Algorithm (GA) based adaptation across GPU cores. The system will build a diagnostically useful segmentation of the anatomical feature within seconds from an area of interest outlined by a physician using a Computed Tomography (CT) or Magnetic Resonance Imaging (MRI) scan.Fast growth in medical imaging overwhelms available diagnosticians. An intuitive and inexpensive system to quickly and accurately deliver diagnostic relevant segmentation of medical images offers tremendous commercial value. Currently, each scan requires approximately 50 minutes of manual preparation. The diagnosis and treatment of an estimated 20 percent of diseases benefit from medical imaging. Newer scanning technologies have increased in resolution, but such techniques have not made segmenting easier or faster. The proposed method will enable more diagnostics to be done with the quality controlled directly by physicians.

这个小型企业创新研究（SBIR）第一阶段项目将实施一个医生辅助的实时自适应系统，用于分割3D医学图像数据中的解剖结构。医学图像分割试图改变解剖结构的表示，使其更容易分析。由于这些结构在生物系统中的极端可变性，目前使用的当前特异质手动方法繁琐、耗时且容易出错。图像分割通常不能通过编程来解决。所提出的系统是一个神经网络（NN）为基础的适应的个人数据使用并行图形处理单元（GPU），再加上遗传算法（GA）为基础的适应跨GPU核心。该系统将在几秒钟内从医生使用计算机断层扫描（CT）或磁共振成像（MRI）扫描勾画出的感兴趣区域建立对诊断有用的解剖特征分割。一个直观和廉价的系统，快速和准确地提供诊断相关的医学图像分割提供了巨大的商业价值。目前，每次扫描需要大约50分钟的手动准备。据估计，20%的疾病的诊断和治疗受益于医学成像。较新的扫描技术提高了分辨率，但此类技术并没有使分割变得更容易或更快。所提出的方法将使更多的诊断与质量直接由医生控制。