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

Coronary artery calcification (CAC) is a strong and independent predictor of
cardiovascular disease (CVD). However, manual assessment of CAC often requires
radiological expertise, time, and invasive imaging techniques. The purpose of
this multicenter study is to validate an automated cardiac plaque detection
model using a 3D multiclass nnU-Net for gated and non-gated non-contrast chest
CT volumes. CT scans were performed at three tertiary care hospitals and
collected as three datasets, respectively. Heart, aorta, and lung segmentations
were determined using TotalSegmentator, while plaques in the coronary arteries
and heart valves were manually labeled for 801 volumes. In this work we
demonstrate how the nnU-Net semantic segmentation pipeline may be adapted to
detect plaques in the coronary arteries and valves. With a linear correction,
nnU-Net deep learning methods may also accurately estimate Agatston scores on
chest non-contrast CT scans. Compared to manual Agatson scoring, automated
Agatston scoring indicated a slope of the linear regression of 0.841 with an
intercept of +16 HU (R2 = 0.97). These results are an improvement over previous
work assessing automated Agatston score computation in non-gated CT scans.

本多中心研究旨在使用 3D 多类别 nnU-Net 验证一种自动化心脏斑块检测模型，以用于门控和非门控的非对比度胸部 CT 体积，证明了 nnU-Net 语义分割管道如何适应于检测冠状动脉和瓣膜斑块，并展示了使用线性修正时，nnU-Net 深度学习方法在胸部非对比度 CT 扫描中准确估计 Agatston 分数的能力，相较于手动的 Agatson 评分，在非门控 CT 扫描中，自动化 Agatston 评分显示了线性回归斜率为 0.841 和截距为 + 16 HU（R2 = 0.97），这些结果比先前检测自动化 Agatston 评分的工作有所改进。

非对比 CT 扫描上的自动斑块检测和 Agatston 评分估计：多中心研究

Automated Plaque Detection and Agatston Score Estimation on Non-Contrast  CT Scans: A Multicenter Study

Gastric cancer is the third leading cause of cancer-related mortality
worldwide, but no guideline-recommended screening test exists. Existing methods
can be invasive, expensive, and lack sensitivity to identify early-stage
gastric cancer. In this study, we explore the feasibility of using a deep
learning approach on non-contrast CT scans for gastric cancer detection. We
propose a novel cluster-induced Mask Transformer that jointly segments the
tumor and classifies abnormality in a multi-task manner. Our model incorporates
learnable clusters that encode the texture and shape prototypes of gastric
cancer, utilizing self- and cross-attention to interact with convolutional
features. In our experiments, the proposed method achieves a sensitivity of
85.0% and specificity of 92.6% for detecting gastric tumors on a hold-out test
set consisting of 100 patients with cancer and 148 normal. In comparison, two
radiologists have an average sensitivity of 73.5% and specificity of 84.3%. We
also obtain a specificity of 97.7% on an external test set with 903 normal
cases. Our approach performs comparably to established state-of-the-art gastric
cancer screening tools like blood testing and endoscopy, while also being more
sensitive in detecting early-stage cancer. This demonstrates the potential of
our approach as a novel, non-invasive, low-cost, and accurate method for
opportunistic gastric cancer screening.

使用非对比度 CT 扫描结合深度学习方法进行胃癌筛查，通过独特的集群感应掩膜变换技术实现肿瘤分割和异常分类，与传统方法相比具有更高的敏感性和准确性，是一种新型的、非侵入性、低成本且准确的机会性胃癌筛查方法。