基于概率的医学CT图像分割框架

Alaa El-Din Mohamed, Mohammed Abdel-Megeed Salem, Doaa Hegazy, Howida A. Shedeed
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引用次数: 5

摘要

肝脏分割是一个困难的过程,因为患者之间肝脏形状和大小的差异很大,肝脏和其他器官之间的强度相似。从腹部计算机断层扫描(CT)图像中分割肝脏在许多诊断和手术过程中非常有用。这是许多临床应用中必不可少的一步。在不使用CT、磁共振成像(MRI)或超声成像(US)等成像技术的情况下,很少会做出医疗决定。本文提出了一种基于概率的腹部CT图像肝脏分割的自动框架。该框架包括四个阶段;阈值分割阶段、超像素构建阶段、贝叶斯网络构建阶段和区域合并阶段。我们使用20个临床卷来训练和验证我们的模型。我们使用MICCAI数据集(医学图像计算和计算机辅助干预肝脏分割)。MICCAI数据集被用于90多项研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Probablistic-based framework for medical CT images segmentation
Liver segmentation is a difficult process due to wide variability of livers shapes and sizes between patients and the intensity similarity between the liver and other organs. Liver segmentation from abdominal Computed Tomography (CT) images is very useful in many diagnostic and surgical processes. It is the essential step in many clinical applications. Medical decisions are rarely taken without the use of imaging technology such as CT, Magnetic Resonance Imaging (MRI), or Ultrasound Imaging (US). In this paper, an automated probabilistic-based framework for liver segmentation from abdominal CT images is presented. The framework consists of four stages; thresholding stage, superpixels construction stage, Bayesian network construction stage and region merging stage. We train and validate our model using 20 clinical volumes. We use the MICCAI dataset (Medical Image Computing and Computer Assisted Intervention for Liver Segmentation). MICCAI dataset is used in more than 90 researches.
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