Goryawala Mohammed, Guillen Magno R, Cabrerizo Mercedes, Barreto Armando, Gulec Seza, Barot Tushar C, Suthar Rekha R, Bhatt Ruchir N, Mcgoron Anthony, Adjouadi Malek
Department of Biomedical Engineering, Florida International University, Miami, FL 33199, USA.
IEEE Trans Inf Technol Biomed. 2012 Jan;16(1):62-9. doi: 10.1109/TITB.2011.2171191. Epub 2011 Oct 10.
This study describes a new 3-D liver segmentation method in support of the selective internal radiation treatment as a treatment for liver tumors. This 3-D segmentation is based on coupling a modified k-means segmentation method with a special localized contouring algorithm. In the segmentation process, five separate regions are identified on the computerized tomography image frames. The merit of the proposed method lays in its potential to provide fast and accurate liver segmentation and 3-D rendering as well as in delineating tumor region(s), all with minimal user interaction. Leveraging of multicore platforms is shown to speed up the processing of medical images considerably, making this method more suitable in clinical settings. Experiments were performed to assess the effect of parallelization using up to 442 slices. Empirical results, using a single workstation, show a reduction in processing time from 4.5 h to almost 1 h for a 78% gain. Most important is the accuracy achieved in estimating the volumes of the liver and tumor region(s), yielding an average error of less than 2% in volume estimation over volumes generated on the basis of the current manually guided segmentation processes. Results were assessed using the analysis of variance statistical analysis.
本研究描述了一种新的三维肝脏分割方法，以支持选择性内部放射治疗作为肝脏肿瘤的一种治疗手段。这种三维分割基于将改进的k均值分割方法与一种特殊的局部轮廓算法相结合。在分割过程中，在计算机断层扫描图像帧上识别出五个不同的区域。所提出方法的优点在于其有潜力提供快速准确的肝脏分割和三维渲染，以及描绘肿瘤区域，所有这些都只需最少的用户交互。利用多核平台可显著加快医学图像的处理速度，使该方法更适合临床应用。进行了实验以评估使用多达442个切片进行并行化的效果。使用单个工作站的实证结果表明，处理时间从4.5小时减少到近1小时，增益达78%。最重要的是在估计肝脏和肿瘤区域体积时所达到的准确性，与基于当前手动引导分割过程生成的体积相比，体积估计的平均误差小于2%。使用方差分析统计分析对结果进行了评估。
