Selçuk University, Electrical & Electronics Engineering Department, Konya, Turkey.
University of Health Sciences, Konya Training and Research Hospital, Radiology Clinic, Konya, Turkey.
Comput Methods Programs Biomed. 2018 Jun;159:77-86. doi: 10.1016/j.cmpb.2018.01.032. Epub 2018 Mar 7.
Adrenal tumours occur on adrenal glands surrounded by organs and osteoid. These tumours can be categorized as either functional, non-functional, malign, or benign. Depending on their appearance in the abdomen, adrenal tumours can arise from one adrenal gland (unilateral) or from both adrenal glands (bilateral) and can connect with other organs, including the liver, spleen, pancreas, etc. This connection phenomenon constitutes the most important handicap against adrenal tumour segmentation. Size change, variety of shape, diverse location, and low contrast (similar grey values between the various tissues) are other disadvantages compounding segmentation difficulty. Few studies have considered adrenal tumour segmentation, and no significant improvement has been achieved for unilateral, bilateral, adherent, or noncohesive tumour segmentation. There is also no recognised segmentation pipeline or method for adrenal tumours including different shape, size, or location information.
This study proposes an adrenal tumour segmentation (ATUS) pipeline designed to eliminate the above disadvantages for adrenal tumour segmentation. ATUS incorporates a number of image methods, including contrast limited adaptive histogram equalization, split and merge based on quadtree decomposition, mean shift segmentation, large grey level eliminator, and region growing.
Performance assessment of ATUS was realised on 32 arterial and portal phase computed tomography images using six metrics: dice, jaccard, sensitivity, specificity, accuracy, and structural similarity index. ATUS achieved remarkable segmentation performance, and was not affected by the discussed handicaps, on particularly adherence to other organs, with success rates of 83.06%, 71.44%, 86.44%, 99.66%, 99.43%, and 98.51% for the metrics, respectively, for images including sufficient contrast uptake.
The proposed ATUS system realises detailed adrenal tumour segmentation, and avoids known disadvantages preventing accurate segmentation.
肾上腺肿瘤发生在被器官和骨样组织包围的肾上腺上。这些肿瘤可以分为功能性、非功能性、恶性或良性。根据其在腹部的表现，肾上腺肿瘤可以来自一个肾上腺（单侧）或两个肾上腺（双侧），并可以与其他器官相连，包括肝脏、脾脏、胰腺等。这种连接现象是阻碍肾上腺肿瘤分割的最重要因素。大小变化、形状多样、位置多样以及对比度低（各种组织之间的灰度值相似）是增加分割难度的其他缺点。很少有研究考虑过肾上腺肿瘤的分割，对于单侧、双侧、粘连或非粘连肿瘤的分割，也没有取得显著的改进。对于包括不同形状、大小或位置信息的肾上腺肿瘤，也没有公认的分割管道或方法。
本研究提出了一种肾上腺肿瘤分割（ATUS）管道，旨在消除肾上腺肿瘤分割中的上述缺点。ATUS 结合了多种图像方法，包括对比度受限自适应直方图均衡化、基于四叉树分解的分裂和合并、均值漂移分割、大灰度消除器和区域生长。
使用六种度量标准（Dice、Jaccard、敏感性、特异性、准确性和结构相似性指数）对 32 张动脉期和门静脉期 CT 图像的 ATUS 性能进行了评估。ATUS 实现了出色的分割性能，并且不受所讨论的障碍的影响，特别是对与其他器官的粘连，对于具有足够对比度摄取的图像，其度量标准的成功率分别为 83.06%、71.44%、86.44%、99.66%、99.43%和 98.51%。
所提出的 ATUS 系统实现了详细的肾上腺肿瘤分割，并避免了阻碍准确分割的已知缺点。
