Yao Tianyuan, Lu Yuzhe, Long Jun, Jha Aadarsh, Zhu Zheyu, Asad Zuhayr, Yang Haichun, Fogo Agnes B, Huo Yuankai
Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States.
Central South University, Big Data Institute, Changsha, China.
J Med Imaging (Bellingham). 2022 Sep;9(5):052408. doi: 10.1117/1.JMI.9.5.052408. Epub 2022 Jun 20.
The quantitative detection, segmentation, and characterization of glomeruli from high-resolution whole slide imaging (WSI) play essential roles in the computer-assisted diagnosis and scientific research in digital renal pathology. Historically, such comprehensive quantification requires extensive programming skills to be able to handle heterogeneous and customized computational tools. To bridge the gap of performing glomerular quantification for non-technical users, we develop the Glo-In-One toolkit to achieve holistic glomerular detection, segmentation, and characterization via a single line of command. Additionally, we release a large-scale collection of 30,000 unlabeled glomerular images to further facilitate the algorithmic development of self-supervised deep learning. The inputs of the Glo-In-One toolkit are WSIs, while the outputs are (1) WSI-level multi-class circle glomerular detection results (which can be directly manipulated with ImageScope), (2) glomerular image patches with segmentation masks, and (3) different lesion types. In the current version, the fine-grained global glomerulosclerosis (GGS) characterization is provided, including assessed-solidified-GSS (associated with hypertension-related injury), disappearing-GSS (a further end result of the SGGS becoming contiguous with fibrotic interstitium), and obsolescent-GSS (nonspecific GGS increasing with aging) glomeruli. To leverage the performance of the Glo-In-One toolkit, we introduce self-supervised deep learning to glomerular quantification via large-scale web image mining. The GGS fine-grained classification model achieved a decent performance compared with baseline supervised methods while only using 10% of the annotated data. The glomerular detection achieved an average precision of 0.627 with circle representations, while the glomerular segmentation achieved a 0.955 patch-wise Dice dimilarity coefficient. We develop and release an open-source Glo-In-One toolkit, a software with holistic glomerular detection, segmentation, and lesion characterization. This toolkit is user-friendly to non-technical users via a single line of command. The toolbox and the 30,000 web mined glomerular images have been made publicly available at https://github.com/hrlblab/Glo-In-One.
从高分辨率全切片成像（WSI）中对肾小球进行定量检测、分割和特征描述，在数字肾脏病理学的计算机辅助诊断和科研中发挥着重要作用。从历史上看，这种全面的量化需要具备广泛的编程技能，才能使用异构和定制的计算工具。为了弥合非技术用户在进行肾小球量化方面的差距，我们开发了Glo-In-One工具包，通过一行命令实现整体肾小球检测、分割和特征描述。此外，我们发布了一个包含30000张未标记肾小球图像的大规模数据集，以进一步促进自监督深度学习的算法开发。Glo-In-One工具包的输入是WSI，而输出是：（1）WSI级别的多类圆形肾小球检测结果（可直接用ImageScope操作），（2）带有分割掩码的肾小球图像块，以及（3）不同的病变类型。在当前版本中，提供了细粒度的全球肾小球硬化（GGS）特征描述，包括评估凝固性GSS（与高血压相关损伤有关）、消失性GSS（SGGS与纤维化间质相邻的进一步最终结果）和过时性GSS（随年龄增长的非特异性GGS）肾小球。为了利用Glo-In-One工具包的性能，我们通过大规模网络图像挖掘将自监督深度学习引入肾小球量化。与基线监督方法相比，GGS细粒度分类模型仅使用10%的标注数据就取得了不错的性能。肾小球检测在圆形表示下的平均精度为0.627，而肾小球分割的逐块Dice相似系数为0.955。我们开发并发布了一个开源的Glo-In-One工具包，这是一个具有整体肾小球检测、分割和病变特征描述的软件。该工具包通过一行命令对非技术用户友好。该工具箱和30000张网络挖掘的肾小球图像已在https://github.com/hrlblab/Glo-In-One上公开提供。
