Objective: Automated segmentation tools are useful for calculating kidney
volumes rapidly and accurately. Furthermore, these tools have the power to
facilitate large-scale image-based artificial intelligence projects by
generating input labels, such as for image registration algorithms. Prior
automated segmentation models have largely ignored non-contrast computed
tomography (CT) imaging. This work aims to implement and train a deep learning
(DL) model to segment the kidneys and cystic renal lesions (CRLs) from
non-contrast CT scans.
Methods: Manual segmentation of the kidneys and CRLs was performed on 150
non-contrast abdominal CT scans. The data were divided into an 80/20 train/test
split and a deep learning (DL) model was trained to segment the kidneys and
CRLs. Various scoring metrics were used to assess model performance, including
the Dice Similarity Coefficient (DSC), Jaccard Index (JI), and absolute and
percent error kidney volume and lesion volume. Bland-Altman (B-A) analysis was
performed to compare manual versus DL-based kidney volumes.
Results: The DL model achieved a median kidney DSC of 0.934, median CRL DSC
of 0.711, and total median study DSC of 0.823. Average volume errors were 0.9%
for renal parenchyma, 37.0% for CRLs, and 2.2% overall. B-A analysis
demonstrated that DL-based volumes tended to be greater than manual volumes,
with a mean bias of +3.0 ml (+/- 2 SD of +/- 50.2 ml).
Conclusion: A deep learning model trained to segment kidneys and cystic renal
lesions on non-contrast CT examinations was able to provide highly accurate
segmentations, with a median kidney Dice Similarity Coefficient of 0.934.
Keywords: deep learning; kidney segmentation; artificial intelligence;
convolutional neural networks.

实施和训练深度学习模型以从非对比度 CT 扫描中分割肾脏和囊性肾病变，该模型能够提供高度准确的分割，肾脏的中值 Dice 相似系数为 0.934。

使用卷积神经网络自动分割非增强 CT 上的肾脏和囊性肾病变

Automatic Segmentation of the Kidneys and Cystic Renal Lesions on  Non-Contrast CT Using a Convolutional Neural Network

The automatic segmentation of kidney, kidney tumor and kidney cyst on
Computed Tomography (CT) scans is a challenging task due to the indistinct
lesion boundaries and fuzzy texture. Considering the large range and unbalanced
distribution of CT scans' thickness, 2.5D ResUnet are adopted to build an
efficient coarse-to-fine semantic segmentation framework in this work. A set of
489 CT scans are used for training and validation, and an independent
never-before-used CT scans for testing. Finally, we demonstrate the
effectiveness of our proposed method. The dice values on test set are 0.954,
0.792, 0.691, the surface dice values are 0.897, 0.591, 0.541 for kidney, tumor
and cyst, respectively. The average inference time of each CT scan is 20.65s
and the max GPU memory is 3525MB. The results suggest that a better trade-off
between model performance and efficiency.

研究中采用 2.5D ResUnet 构建一个高效的粗到细语义分割框架，对 CT 扫描中的肾脏、肾脏肿瘤和肾囊肿进行自动分割。实验结果表明，肾脏、肿瘤和囊肿的 Dice 系数分别为 0.954、0.792、0.691，表面 Dice 系数分别为 0.897、0.591、0.541，平均每个 CT 扫描的推断时间为 20.65 秒，最大 GPU 内存为 3525MB，这表明在模型性能和效率之间取得了更好的平衡。