Improved Heatmap-Based Landmark Detection

Yao, Huifeng; Zhang, Yatao; Li, Yongming

doi:10.1007/978-3-030-88210-5_11

Cited by 1 publication

(3 citation statements)

References 11 publications

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“…In this study, we collected over 10,000 compound figures (each figure might contain multiple subplots) through the NIH Open-I ® search engine with the keywords glomerular OR glomeruli OR glomerulus. Then, our compound figure separation method 30 was employed to separate compound images into individual images, which were further categorized to different modalities (e.g., light microscopy, florescent microscopy, and electron microscopy) as well as different stain types within the light microscopy. To curate all images, an automatic deep learning-based curator (detector) was trained using only a smaller scale annotated images dataset 74 .…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…However, the images from online resources are typically in compound figures (with multiple subplots), which cannot be directly used for self-supervised learning. Thus, we employ our previously developed compound image separation approach 30 to detect, separate, and curate subplots to individual images for downstream learning tasks. Using the compound figure separation approach, we acquired over 30,000 unannotated glomerular images via large web image mining.…”

Section: Methodsmentioning

confidence: 99%

“…We employ a SimSiam 29 network as the baseline method of self-supervised contrastive learning. By deploying our previously developed compound figure separation approach, 30 we provide 30,000 unannotated glomerular images via web image mining to train the SimSiam network. From the results, the GGS fine-grained classification model achieved superior performance as compared with the baseline methods.…”

Section: Introductionmentioning

confidence: 99%

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Glo-In-One: holistic glomerular detection, segmentation, and lesion characterization with large-scale web image mining

Yao

Long

et al. 2022

J. Med. Imag.

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Purpose: The quantitative detection, segmentation, and characterization of glomeruli from highresolution 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.Approach: The inputs of the Glo-In-One toolkit are WSIs, while the outputs are (1) WSIlevel 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. Results: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. Conclusion: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.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%