Colorectal cancer (CRC) is the third most common type of cancer with the liver being the most common site for cancer spread. A precise understanding of patient liver anatomy and pathology, as well as surgical planning based on that, plays a critical role in the treatment process. In some cases, surgeons request a 3D reconstruction, which requires a thorough analysis of the available images to be converted into 3D models of relevant objects through a segmentation process. Liver vessel segmentation is challenging due to the large variations in size and directions of the vessel structures as well as difficult contrasting conditions. In recent years, deep learning-based methods had been outperforming the conventional image analysis methods in the field of medical imaging. Though Convolutional Neural Networks (CNN) have been proved to be efficient for the task of medical image segmentation, the way of handling the image data and the preprocessing techniques play an important role in segmentation. Our work focuses on the combination of different vesselness enhancement filters and preprocessing methods to enhance the hepatic vessels prior to segmentation. In the first experiment, the effect of enhancement using individual vesselness filters was studied. In the second experiment, the effect of gamma correction on vesselness filters was studied. Lastly, the effect of fused vesselness filters over individual filters was studied. The methods were evaluated on clinical CT data. The quantitative analysis of the results in terms of different evaluation metrics from experiments can be summed up as (i) each of the filtered methods shows an improvement as compared to unenhanced with the best mean DICE score of 0.800 in comparison to 0.740 for unenhanced; (ii) applied gamma correction provides a statistically significant improvement in the performance of each filter with improvement in mean DICE of around 2%; (iii) both the fused filtered images and fused segmentation give the best results (mean DICE score of 0.818 and 0.830, respectively) with the statistically significant improvement compared to the individual filters with and without Gamma correction. The results have further been verified by qualitative analysis and hence show the importance of our proposed fused filter and segmentation approaches.

中文翻译：

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增强对基于深度学习的肝血管分割的影响

大肠癌（CRC）是第三大最常见的癌症类型，肝脏是最常见的癌症扩散部位。对患者肝脏解剖结构和病理的准确了解以及基于此的手术计划在治疗过程中起着至关重要的作用。在某些情况下，外科医生要求进行3D重建，这需要对可用图像进行全面分析，以通过分割过程将其转换为相关对象的3D模型。由于血管结构的尺寸和方向的巨大变化以及困难的对比条件，肝血管的分割具有挑战性。近年来，基于深度学习的方法在医学成像领域已经超越了传统的图像分析方法。尽管已经证明卷积神经网络（CNN）对于医学图像分割是有效的，但是图像数据的处理方式和预处理技术在分割中起着重要的作用。我们的工作集中于将不同的血管增强过滤器和预处理方法相结合，以在分割之前增强肝血管。在第一个实验中，研究了使用单个血管过滤器的增强效果。在第二个实验中，研究了伽玛校正对血管过滤器的影响。最后，研究了熔融容器式过滤器对单个过滤器的影响。该方法是根据临床CT数据进行评估的。根据来自实验的不同评估指标对结果进行的定量分析可以归纳为：（i）与未增强的相比，每种过滤方法均显示出改进，与未增强的0.740相比，最佳平均DICE得分为0.800；（ii）应用的伽玛校正在统计上显着改善了每个滤镜的性能，平均DICE改善了2％左右；（iii）与使用和不使用Gamma校正的单个滤镜相比，经过融合的滤波图像和经过融合的分割均给出了最好的结果（DICE平均值分别为0.818和0.830），具有统计学上的显着改善。定性分析进一步验证了结果，因此表明了我们提出的融合滤波器和分割方法的重要性。