School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin, Guangxi, 541004, China.
Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi, 530021, China.
Comput Biol Med. 2022 Nov;150:106223. doi: 10.1016/j.compbiomed.2022.106223. Epub 2022 Oct 18.
The Kidney and Kidney Tumor Segmentation Challenge 2021 (KiTS21) released a kidney CT dataset with 300 patients. Unlike KiTS19, KiTS21 provided a cyst category. Therefore, the segmentation of kidneys, tumors, and cysts will be able to assess the complexity and aggressiveness of kidney mass. Deep learning models can save medical resources, but 3D models still have some disadvantages, such as the high cost of computing resources. This paper proposes a scheme that saves computing resources and achieves the segmentation of kidney mass in two steps. First, we preprocess the kidney volume data using the automatic down-sampling method of 3D images, reducing the volume while preserving the feature information. Second, we finely segment kidneys, tumors, and cysts using the AgDenseU-Net (Attention gate DenseU-Net) 2.5D model. KiTS21 proposed using Hierarchical Evaluation Classes (HECs) to compute a metric for the superset: the HEC of kidney considers kidneys, tumors, and cysts as the foreground to compute segmentation performance; the HEC of kidney mass considers both tumor and cyst as the foreground classes; the HEC of tumor considers tumor as the foreground only. For KiTS21, our model achieved a dice score of 0.971 for the kidney, 0.883 for the mass, and 0.815 for the tumor. In addition, we also tested segmentation results without HECs, and our model achieved a dice score of 0.950 for the kidney, 0.878 for the tumor, and 0.746 for the cyst. The results demonstrate that the method proposed in this paper can be used as a reference for kidney tumor segmentation.
肾脏和肾脏肿瘤分割挑战赛 2021（KiTS21）发布了一个包含 300 名患者的肾脏 CT 数据集。与 KiTS19 不同，KiTS21 提供了囊肿类别。因此，肾脏、肿瘤和囊肿的分割将能够评估肾脏肿块的复杂性和侵袭性。深度学习模型可以节省医疗资源，但 3D 模型仍然存在一些缺点，例如计算资源成本高。本文提出了一种方案，通过两步来节省计算资源并实现对肾脏肿块的分割。首先，我们使用 3D 图像的自动下采样方法预处理肾脏体积数据，在保留特征信息的同时减少体积。其次，我们使用 AgDenseU-Net（注意力门控密集 U-Net）2.5D 模型精细地分割肾脏、肿瘤和囊肿。KiTS21 提出使用层次评估类别（HEC）来计算超集的度量标准：肾脏的 HEC 将肾脏、肿瘤和囊肿视为前景来计算分割性能；肾脏肿块的 HEC 将肿瘤和囊肿视为前景类；肿瘤的 HEC 仅将肿瘤视为前景。对于 KiTS21，我们的模型在肾脏方面的 Dice 得分达到 0.971，在肿块方面达到 0.883，在肿瘤方面达到 0.815。此外，我们还测试了没有 HEC 的分割结果，我们的模型在肾脏方面的 Dice 得分为 0.950，在肿瘤方面的 Dice 得分为 0.878，在囊肿方面的 Dice 得分为 0.746。结果表明，本文提出的方法可以作为肾脏肿瘤分割的参考。
