Accurate and fast liver segmentation remains a challenging and important task for clinicians. Segmentation algorithms are slow and inaccurate due to noise and low quality images in computed tomography (CT) abdominal scans. Chan–Vese is an active contour based powerful and flexible method for image segmentation due to superior noise robustness. However, it is quite slow due to time-consuming partial differential equations, especially for large medical datasets. This can pose a problem for a real-time implementation of liver segmentation and hence, an efficient parallel implementation is highly desirable. Another important aspect is the contrast of CT liver images. Liver slices are sometimes very low in contrast which reduces the overall quality of liver segmentation. Hence, we implement cross-modality guided liver contrast enhancement as a pre-processing step to liver segmentation. GPU implementation of Chan–Vese improves average speedup by 99.811 ($\pm$ 7.65) times and 14.647 ($\pm$ 1.155) times with and without enhancement respectively in comparison with the CPU. Average dice, sensitivity and accuracy of liver segmentation are 0.656, 0.816 and 0.822 respectively on the original liver images and 0.877, 0.964 and 0.956 respectively on the enhanced liver images improving the overall quality of liver segmentation.

对于临床医生而言，准确快速地进行肝脏分割仍然是一项艰巨而重要的任务。由于计算机断层扫描（CT）腹部扫描中的噪声和低质量图像，因此分割算法缓慢且不准确。由于出色的噪声鲁棒性，Chan–Vese是一种基于主动轮廓的强大而灵活的图像分割方法。但是，由于耗时的偏微分方程，速度相当慢，尤其是对于大型医疗数据集而言。这可能对肝脏分割的实时实施造成问题，因此，非常需要有效的并行实施。另一个重要方面是CT肝脏图像的对比度。肝脏切片有时对比度很低，这会降低肝脏分割的整体质量。因此，我们将跨模态引导的肝脏造影剂增强作为肝脏分割的预处理步骤。Chan-Vese的GPU实施将平均速度提高了99.811（$\pm$ 7.65）次和14.647（$\pm$与CPU相比，分别有1.155）次有和没有增强的时间。原始肝图像的平均骰子，肝分割的敏感性和准确性分别为0.656、0.816和0.822，增强肝脏图像的平均骰子，敏感性和准确性分别为0.877、0.964和0.956，从而改善了肝分割的整体质量。