Deep Segmentation Networks for Segmenting Kidneys and Detecting Kidney Stones in Unenhanced Abdominal CT Images.
Deep Segmentation Networks for Segmenting Kidneys and Detecting Kidney Stones in Unenhanced Abdominal CT Images.
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深度分割网络用于分割肾脏和检测非增强腹部CT图像中的肾结石。
DOI：
10.3390/diagnostics12081788
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发表时间：
2022-07-23
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深度学习算法在医学成像、自动检测和腹部计算机断层扫描（CT）图像中肾脏（肾脏）分割技术方面的最新突破有限。肾脏疾病的放射组学和机器学习分析依赖于CT图像中肾脏的自动分割。受此启发，我们的主要目标是利用深度语义分割学习模型和建议的训练方案来实现精确和准确的分割结果。此外，这项工作旨在为社区提供一个开源的、未经增强的腹部CT数据集，用于训练和测试深度学习分割网络，以分割肾脏和检测肾结石。深度分割网络的五种变体都是独立（基于所提出的训练方案）和独立训练和测试的。通过比较，使用所提出的训练方案训练的模型能够实现肾脏和肾结石的高度准确的2D和3D分割。我们相信这项工作是迈向人工智能驱动的诊断策略的基本一步，这可以成为个性化患者护理和改善肾脏疾病治疗决策的重要组成部分。
Recent breakthroughs of deep learning algorithms in medical imaging, automated detection, and segmentation techniques for renal (kidney) in abdominal computed tomography (CT) images have been limited. Radiomics and machine learning analyses of renal diseases rely on the automatic segmentation of kidneys in CT images. Inspired by this, our primary aim is to utilize deep semantic segmentation learning models with a proposed training scheme to achieve precise and accurate segmentation outcomes. Moreover, this work aims to provide the community with an open-source, unenhanced abdominal CT dataset for training and testing the deep learning segmentation networks to segment kidneys and detect kidney stones. Five variations of deep segmentation networks are trained and tested both dependently (based on the proposed training scheme) and independently. Upon comparison, the models trained with the proposed training scheme enable the highly accurate 2D and 3D segmentation of kidneys and kidney stones. We believe this work is a fundamental step toward AI-driven diagnostic strategies, which can be an essential component of personalized patient care and improved decision-making in treating kidney diseases.
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影响因子：
3.2
作者：
Dwivedi DK;Xi Y;Kapur P;Madhuranthakam AJ;Lewis MA;Udayakumar D;Rasmussen R;Yuan Q;Bagrodia A;Margulis V;Fulkerson M;Brugarolas J;Cadeddu JA;Pedrosa I
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Pedrosa I
影响因子：
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作者：
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通讯作者：
Gong X
DOI：
10.1038/nrdp.2016.8
发表时间：
2016-02-25
期刊：
Nature reviews. Disease primers
影响因子：
--
作者：
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通讯作者：
Tiselius HG
影响因子：
3.3
作者：
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通讯作者：
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影响因子：
48
作者：
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通讯作者：
Maier-Hein, Klaus H.