Many renal cancers are incidentally found on non-contrast CT (NCCT) images. On contrast-enhanced CT (CECT) images, most kidney tumors, especially renal cancers, have different intensity values compared to normal tissues. However, on NCCT images, some tumors called isodensity tumors, have similar intensity values to the surrounding normal tissues, and can only be detected through a change in organ shape. Several deep learning methods which segment kidney tumors from CECT images have been proposed and showed promising results. However, these methods fail to capture such changes in organ shape on NCCT images. In this paper, we present a novel framework, which can explicitly capture protruded regions in kidneys to enable a better segmentation of kidney tumors. We created a synthetic mask dataset that simulates a protuberance, and trained a segmentation network to separate the protruded regions from the normal kidney regions. To achieve the segmentation of whole tumors, our framework consists of three networks. The first network is a conventional semantic segmentation network which extracts a kidney region mask and an initial tumor region mask. The second network, which we name protuberance detection network, identifies the protruded regions from the kidney region mask. Given the initial tumor region mask and the protruded region mask, the last network fuses them and predicts the final kidney tumor mask accurately. The proposed method was evaluated on a publicly available KiTS19 dataset, which contains 108 NCCT images, and showed that our method achieved a higher dice score of 0.615 (+0.097) and sensitivity of 0.721 (+0.103) compared to 3D-UNet. To the best of our knowledge, this is the first deep learning method that is specifically designed for kidney tumor segmentation on NCCT images.

翻译：许多肾癌是在非增强CT（NCCT）图像上偶然发现的。在增强CT（CECT）图像上，大多数肾脏肿瘤（尤其是肾癌）与正常组织相比具有不同的强度值。然而，在NCCT图像上，一些被称为等密度肿瘤的肿瘤与周围正常组织的强度值相似，只能通过器官形状的变化来检测。已有多种从CECT图像分割肾脏肿瘤的深度学习方法被提出并显示出有前景的结果。然而，这些方法无法捕捉NCCT图像上器官形状的变化。在本文中，我们提出了一种新颖的框架，能够显式捕捉肾脏中的隆起区域，从而实现更好的肾脏肿瘤分割。我们创建了一个模拟隆起的合成掩膜数据集，并训练了一个分割网络将隆起区域与正常肾脏区域分离。为了实现肿瘤的整体分割，我们的框架包含三个网络。第一个网络是传统的语义分割网络，用于提取肾脏区域掩膜和初始肿瘤区域掩膜。第二个网络（我们称之为隆起检测网络）从肾脏区域掩膜中识别隆起区域。结合初始肿瘤区域掩膜和隆起区域掩膜，最后一个网络对它们进行融合并准确预测最终的肾脏肿瘤掩膜。所提出的方法在公开可用的KiTS19数据集（包含108张NCCT图像）上进行了评估，结果显示与3D-UNet相比，我们的方法达到了更高的Dice分数（0.615，+0.097）和灵敏度（0.721，+0.103）。据我们所知，这是首个专门为NCCT图像上肾脏肿瘤分割设计的深度学习方法。