Engineering Faculty, Department of Electrical and Electronics Engineering, Selcuk University, 42250, Konya, Turkey.
Ankara Child Health and Disease Hematology Oncology Training and Research Hospital, Radiology Clinic, University of Health Sciences, Ankara, Turkey.
J Digit Imaging. 2018 Apr;31(2):262-274. doi: 10.1007/s10278-017-0032-0.
Computed tomography (CT) scans usually include some disadvantages due to the nature of the imaging procedure, and these handicaps prevent accurate abdomen segmentation. Discontinuous abdomen edges, bed section of CT, patient information, closeness between the edges of the abdomen and CT, poor contrast, and a narrow histogram can be regarded as the most important handicaps that occur in abdominal CT scans. Currently, one or more handicaps can arise and prevent technicians obtaining abdomen images through simple segmentation techniques. In other words, CT scans can include the bed section of CT, a patient's diagnostic information, low-quality abdomen edges, low-level contrast, and narrow histogram, all in one scan. These phenomena constitute a challenge, and an efficient pipeline that is unaffected by handicaps is required. In addition, analysis such as segmentation, feature selection, and classification has meaning for a real-time diagnosis system in cases where the abdomen section is directly used with a specific size. A statistical pipeline is designed in this study that is unaffected by the handicaps mentioned above. Intensity-based approaches, morphological processes, and histogram-based procedures are utilized to design an efficient structure. Performance evaluation is realized in experiments on 58 CT images (16 training, 16 test, and 26 validation) that include the abdomen and one or more disadvantage(s). The first part of the data (16 training images) is used to detect the pipeline's optimum parameters, while the second and third parts are utilized to evaluate and to confirm the segmentation performance. The segmentation results are presented as the means of six performance metrics. Thus, the proposed method achieves remarkable average rates for training/test/validation of 98.95/99.36/99.57% (jaccard), 99.47/99.67/99.79% (dice), 100/99.91/99.91% (sensitivity), 98.47/99.23/99.85% (specificity), 99.38/99.63/99.87% (classification accuracy), and 98.98/99.45/99.66% (precision). In summary, a statistical pipeline performing the task of abdomen segmentation is achieved that is not affected by the disadvantages, and the most detailed abdomen segmentation study is performed for the use before organ and tumor segmentation, feature extraction, and classification.
计算机断层扫描（CT）扫描通常由于成像过程的性质而存在一些缺点，这些缺点妨碍了腹部的准确分割。不连续的腹部边缘、CT 床层、患者信息、腹部边缘与 CT 的接近度、对比度差以及直方图狭窄，都可以被视为腹部 CT 扫描中最重要的障碍。目前，一种或多种障碍可能会出现，并阻止技术人员通过简单的分割技术获得腹部图像。换句话说，CT 扫描可能会在一次扫描中包含 CT 床层、患者的诊断信息、质量差的腹部边缘、低水平的对比度和狭窄的直方图。这些现象构成了一个挑战，需要一个不受障碍影响的高效流水线。此外，在直接使用特定大小的腹部部分的情况下，分割、特征选择和分类等分析对于实时诊断系统具有意义。本研究设计了一个不受上述障碍影响的统计流水线。利用基于强度的方法、形态学处理和基于直方图的方法来设计一个有效的结构。在包含腹部和一个或多个障碍的 58 张 CT 图像（16 张训练图像、16 张测试图像和 26 张验证图像）的实验中进行了性能评估。数据的第一部分（16 张训练图像）用于检测流水线的最佳参数，而第二和第三部分用于评估和确认分割性能。分割结果以六种性能指标的平均值表示。因此，所提出的方法在训练/测试/验证方面的平均率分别达到了 98.95%/99.36%/99.57%（Jaccard）、99.47%/99.67%/99.79%（Dice）、100%/99.91%/99.91%（敏感性）、98.47%/99.23%/99.85%（特异性）、99.38%/99.63%/99.87%（分类准确性）和 98.98%/99.45%/99.66%（精度）。总之，实现了一种不受障碍影响的执行腹部分割任务的统计流水线，并对器官和肿瘤分割、特征提取和分类之前的腹部分割进行了最详细的研究。
