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Babickova J, Yang HC, Fogo AB. Adverse effects of acute tubular injury on the glomerulus: contributing factors and mechanisms. Pediatr Nephrol 2024; 39:2301-2308. [PMID: 38191938 DOI: 10.1007/s00467-023-06264-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 12/04/2023] [Accepted: 12/14/2023] [Indexed: 01/10/2024]
Abstract
The intricate relationship between tubular injury and glomerular dysfunction in kidney diseases has been a subject of extensive research. While the impact of glomerular injury on downstream tubules has been well-studied, the reverse influence of tubular injury on the glomerulus remains less explored. This paper provides a comprehensive review of recent advances in the field, focusing on key pathways and players implicated in the pathogenesis of tubular injury on glomerular dysfunction. Anatomical and physiological evidence supports the possibility of crosstalk from the tubule to the glomerulus, whereby various mechanisms contribute to glomerular injury following tubular injury. These mechanisms include tubular backleak, dysfunctional tubuloglomerular feedback, capillary rarefaction, atubular glomeruli, and the secretion of factors from damaged tubular epithelial cells. Clinical evidence further supports the association between even mild or recovered acute kidney injury and an increased risk of chronic kidney disease, including glomerular diseases. We also discuss potential therapeutic interventions aimed at mitigating acute tubular injury, thereby reducing the detrimental effects on glomerular function. By unraveling the complex interplay from tubular injury to glomerular dysfunction, we aim to provide insights that can enhance clinical management strategies and improve outcomes for patients with kidney disease.
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Affiliation(s)
- Janka Babickova
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, MCN C3318, Nashville, TN, 37232, USA
- Department of Clinical Medicine, University of Bergen, Bergen, Norway
- Institute of Molecular Biomedicine, Faculty of Medicine, Comenius University, Bratislava, Slovakia
| | - Hai-Chun Yang
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, MCN C3318, Nashville, TN, 37232, USA
| | - Agnes B Fogo
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, MCN C3318, Nashville, TN, 37232, USA.
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2
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Lindemann MC, Glänzer L, Roeth AA, Schmitz-Rode T, Slabu I. Towards Realistic 3D Models of Tumor Vascular Networks. Cancers (Basel) 2023; 15:5352. [PMID: 38001612 PMCID: PMC10670125 DOI: 10.3390/cancers15225352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 11/03/2023] [Accepted: 11/03/2023] [Indexed: 11/26/2023] Open
Abstract
For reliable in silico or in vitro investigations in, for example, biosensing and drug delivery applications, accurate models of tumor vascular networks down to the capillary size are essential. Compared to images acquired with conventional medical imaging techniques, digitalized histological tumor slices have a higher resolution, enabling the delineation of capillaries. Volume rendering procedures can then be used to generate a 3D model. However, the preparation of such slices leads to misalignments in relative slice orientation between consecutive slices. Thus, image registration algorithms are necessary to re-align the slices. Here, we present an algorithm for the registration and reconstruction of a vascular network from histologic slices applied to 169 tumor slices. The registration includes two steps. First, consecutive images are incrementally pre-aligned using feature- and area-based transformations. Second, using the previous transformations, parallel registration for all images is enabled. Combining intensity- and color-based thresholds along with heuristic analysis, vascular structures are segmented. A 3D interpolation technique is used for volume rendering. This results in a 3D vascular network with approximately 400-450 vessels with diameters down to 25-30 µm. A delineation of vessel structures with close distance was limited in areas of high structural density. Improvement can be achieved by using images with higher resolution and or machine learning techniques.
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Affiliation(s)
- Max C. Lindemann
- Institute of Applied Medical Engineering, Helmholtz Institute, Medical Faculty, RWTH Aachen University, Pauwelsstraße 20, 52074 Aachen, Germany (L.G.); (T.S.-R.)
| | - Lukas Glänzer
- Institute of Applied Medical Engineering, Helmholtz Institute, Medical Faculty, RWTH Aachen University, Pauwelsstraße 20, 52074 Aachen, Germany (L.G.); (T.S.-R.)
| | - Anjali A. Roeth
- Department of General, Visceral and Transplant Surgery, RWTH Aachen University Hospital, Pauwelsstrasse 30, 52074 Aachen, Germany
- Department of Surgery, Maastricht University, P. Debyelaan 25, 6229 HX Maastricht, The Netherlands
| | - Thomas Schmitz-Rode
- Institute of Applied Medical Engineering, Helmholtz Institute, Medical Faculty, RWTH Aachen University, Pauwelsstraße 20, 52074 Aachen, Germany (L.G.); (T.S.-R.)
| | - Ioana Slabu
- Institute of Applied Medical Engineering, Helmholtz Institute, Medical Faculty, RWTH Aachen University, Pauwelsstraße 20, 52074 Aachen, Germany (L.G.); (T.S.-R.)
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Lin Y, Liang Z, He Y, Huang W, Guan T. End-to-end affine registration framework for histopathological images with weak annotations. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 241:107763. [PMID: 37634308 DOI: 10.1016/j.cmpb.2023.107763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 08/12/2023] [Accepted: 08/12/2023] [Indexed: 08/29/2023]
Abstract
BACKGROUND AND OBJECTIVE Histopathological image registration is an essential component in digital pathology and biomedical image analysis. Deep-learning-based algorithms have been proposed to achieve fast and accurate affine registration. Some previous studies assume that the pairs are free from sizeable initial position misalignment and large rotation angles before performing the affine transformation. However, large-rotation angles are often introduced into image pairs during the production process in real-world pathology images. Reliable initial alignment is important for registration performance. The existing deep-learning-based approaches often use a two-step affine registration pipeline because convolutional neural networks (CNNs) cannot correct large-angle rotations. METHODS In this manuscript, a general framework ARoNet is developed to achieve end-to-end affine registration for histopathological images. We use CNNs to extract global features of images and fuse them to construct correspondent information for affine transformation. In ARoNet, a rotation recognition network is implemented to eliminate great rotation misalignment. In addition, a self-supervised learning task is proposed to assist the learning of image representations in an unsupervised manner. RESULTS We applied our model to four datasets, and the results indicate that ARoNet surpasses existing affine registration algorithms in alignment accuracy when large angular misalignments (e.g., 180 rotation) are present, providing accurate affine initialization for subsequent non-rigid alignments. Besides, ARoNet shows advantages in execution time (0.05 per pair), registration accuracy, and robustness. CONCLUSION We believe that the proposed general framework promises to simplify and speed up the registration process and has the potential for clinical applications.
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Affiliation(s)
- Yuanhua Lin
- Shenzhen International Graduate School, Tsinghua University, 518055, Shenzhen, China
| | - Zhendong Liang
- Shenzhen International Graduate School, Tsinghua University, 518055, Shenzhen, China
| | - Yonghong He
- Shenzhen International Graduate School, Tsinghua University, 518055, Shenzhen, China
| | - Wenting Huang
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 518116, Shenzhen, China
| | - Tian Guan
- Shenzhen International Graduate School, Tsinghua University, 518055, Shenzhen, China.
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Deng R, Li Y, Li P, Wang J, Remedios LW, Agzamkhodjaev S, Asad Z, Liu Q, Cui C, Wang Y, Wang Y, Tang Y, Yang H, Huo Y. Democratizing Pathological Image Segmentation with Lay Annotators via Molecular-empowered Learning. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2023; 14225:497-507. [PMID: 38529367 PMCID: PMC10961594 DOI: 10.1007/978-3-031-43987-2_48] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/27/2024]
Abstract
Multi-class cell segmentation in high-resolution Giga-pixel whole slide images (WSI) is critical for various clinical applications. Training such an AI model typically requires labor-intensive pixel-wise manual annotation from experienced domain experts (e.g., pathologists). Moreover, such annotation is error-prone when differentiating fine-grained cell types (e.g., podocyte and mesangial cells) via the naked human eye. In this study, we assess the feasibility of democratizing pathological AI deployment by only using lay annotators (annotators without medical domain knowledge). The contribution of this paper is threefold: (1) We proposed a molecular-empowered learning scheme for multi-class cell segmentation using partial labels from lay annotators; (2) The proposed method integrated Giga-pixel level molecular-morphology cross-modality registration, molecular-informed annotation, and molecular-oriented segmentation model, so as to achieve significantly superior performance via 3 lay annotators as compared with 2 experienced pathologists; (3) A deep corrective learning (learning with imperfect label) method is proposed to further improve the segmentation performance using partially annotated noisy data. From the experimental results, our learning method achieved F1 = 0.8496 using molecular-informed annotations from lay annotators, which is better than conventional morphology-based annotations (F1 = 0.7015) from experienced pathologists. Our method democratizes the development of a pathological segmentation deep model to the lay annotator level, which consequently scales up the learning process similar to a non-medical computer vision task. The official implementation and cell annotations are publicly available at https://github.com/hrlblab/MolecularEL.
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Affiliation(s)
| | - Yanwei Li
- Vanderbilt University, Nashville TN 37215, USA
| | - Peize Li
- Vanderbilt University, Nashville TN 37215, USA
| | | | | | | | - Zuhayr Asad
- Vanderbilt University, Nashville TN 37215, USA
| | - Quan Liu
- Vanderbilt University, Nashville TN 37215, USA
| | - Can Cui
- Vanderbilt University, Nashville TN 37215, USA
| | - Yaohong Wang
- Vanderbilt University Medical Center, Nashville TN 37232, USA
| | - Yihan Wang
- Vanderbilt University Medical Center, Nashville TN 37232, USA
| | - Yucheng Tang
- NVIDIA Corporation, Santa Clara and Bethesda, USA
| | - Haichun Yang
- Vanderbilt University Medical Center, Nashville TN 37232, USA
| | - Yuankai Huo
- Vanderbilt University, Nashville TN 37215, USA
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Al-Thelaya K, Gilal NU, Alzubaidi M, Majeed F, Agus M, Schneider J, Househ M. Applications of discriminative and deep learning feature extraction methods for whole slide image analysis: A survey. J Pathol Inform 2023; 14:100335. [PMID: 37928897 PMCID: PMC10622844 DOI: 10.1016/j.jpi.2023.100335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 07/17/2023] [Accepted: 07/19/2023] [Indexed: 11/07/2023] Open
Abstract
Digital pathology technologies, including whole slide imaging (WSI), have significantly improved modern clinical practices by facilitating storing, viewing, processing, and sharing digital scans of tissue glass slides. Researchers have proposed various artificial intelligence (AI) solutions for digital pathology applications, such as automated image analysis, to extract diagnostic information from WSI for improving pathology productivity, accuracy, and reproducibility. Feature extraction methods play a crucial role in transforming raw image data into meaningful representations for analysis, facilitating the characterization of tissue structures, cellular properties, and pathological patterns. These features have diverse applications in several digital pathology applications, such as cancer prognosis and diagnosis. Deep learning-based feature extraction methods have emerged as a promising approach to accurately represent WSI contents and have demonstrated superior performance in histology-related tasks. In this survey, we provide a comprehensive overview of feature extraction methods, including both manual and deep learning-based techniques, for the analysis of WSIs. We review relevant literature, analyze the discriminative and geometric features of WSIs (i.e., features suited to support the diagnostic process and extracted by "engineered" methods as opposed to AI), and explore predictive modeling techniques using AI and deep learning. This survey examines the advances, challenges, and opportunities in this rapidly evolving field, emphasizing the potential for accurate diagnosis, prognosis, and decision-making in digital pathology.
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Affiliation(s)
- Khaled Al-Thelaya
- Department of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Nauman Ullah Gilal
- Department of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Mahmood Alzubaidi
- Department of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Fahad Majeed
- Department of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Marco Agus
- Department of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Jens Schneider
- Department of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Mowafa Househ
- Department of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
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Glänzer L, Masalkhi HE, Roeth AA, Schmitz-Rode T, Slabu I. Vessel Delineation Using U-Net: A Sparse Labeled Deep Learning Approach for Semantic Segmentation of Histological Images. Cancers (Basel) 2023; 15:3773. [PMID: 37568589 PMCID: PMC10417575 DOI: 10.3390/cancers15153773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 07/20/2023] [Accepted: 07/21/2023] [Indexed: 08/13/2023] Open
Abstract
Semantic segmentation is an important imaging analysis method enabling the identification of tissue structures. Histological image segmentation is particularly challenging, having large structural information while providing only limited training data. Additionally, labeling these structures to generate training data is time consuming. Here, we demonstrate the feasibility of a semantic segmentation using U-Net with a novel sparse labeling technique. The basic U-Net architecture was extended by attention gates, residual and recurrent links, and dropout regularization. To overcome the high class imbalance, which is intrinsic to histological data, under- and oversampling and data augmentation were used. In an ablation study, various architectures were evaluated, and the best performing model was identified. This model contains attention gates, residual links, and a dropout regularization of 0.125. The segmented images show accurate delineations of the vascular structures (with a precision of 0.9088 and an AUC-ROC score of 0.9717), and the segmentation algorithm is robust to images containing staining variations and damaged tissue. These results demonstrate the feasibility of sparse labeling in combination with the modified U-Net architecture.
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Affiliation(s)
- Lukas Glänzer
- Institute of Applied Medical Engineering, Helmholtz Institute, Medical Faculty, RWTH Aachen University, Pauwelsstraße 20, 52074 Aachen, Germany; (L.G.); (H.E.M.); (T.S.-R.)
| | - Husam E. Masalkhi
- Institute of Applied Medical Engineering, Helmholtz Institute, Medical Faculty, RWTH Aachen University, Pauwelsstraße 20, 52074 Aachen, Germany; (L.G.); (H.E.M.); (T.S.-R.)
| | - Anjali A. Roeth
- Department of Visceral and Transplantation Surgery, University Hospital RWTH Aachen, Pauwelsstrasse 30, 52074 Aachen, Germany;
- Department of Surgery, Maastricht University, P. Debyelaan 25, 6229 Maastricht, The Netherlands
| | - Thomas Schmitz-Rode
- Institute of Applied Medical Engineering, Helmholtz Institute, Medical Faculty, RWTH Aachen University, Pauwelsstraße 20, 52074 Aachen, Germany; (L.G.); (H.E.M.); (T.S.-R.)
| | - Ioana Slabu
- Institute of Applied Medical Engineering, Helmholtz Institute, Medical Faculty, RWTH Aachen University, Pauwelsstraße 20, 52074 Aachen, Germany; (L.G.); (H.E.M.); (T.S.-R.)
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Yao T, Lu Y, Long J, Jha A, Zhu Z, Asad Z, Yang H, Fogo AB, Huo Y. Glo-In-One: holistic glomerular detection, segmentation, and lesion characterization with large-scale web image mining. J Med Imaging (Bellingham) 2022; 9:052408. [PMID: 35747553 DOI: 10.1117/1.jmi.9.5.052408] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 05/31/2022] [Indexed: 11/14/2022] Open
Abstract
Purpose: 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. Approach: 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. 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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Affiliation(s)
- Tianyuan Yao
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Yuzhe Lu
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Jun Long
- Central South University, Big Data Institute, Changsha, China
| | - Aadarsh Jha
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Zheyu Zhu
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Zuhayr Asad
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Haichun Yang
- Vanderbilt University Medical Center, Department of Pathology, Microbiology and Immunology, Nashville, Tennessee, United States
| | - Agnes B Fogo
- Vanderbilt University Medical Center, Department of Pathology, Microbiology and Immunology, Nashville, Tennessee, United States
| | - Yuankai Huo
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
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Huo Y, Deng R, Liu Q, Fogo AB, Yang H. AI applications in renal pathology. Kidney Int 2021; 99:1309-1320. [PMID: 33581198 PMCID: PMC8154730 DOI: 10.1016/j.kint.2021.01.015] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 01/09/2021] [Accepted: 01/13/2021] [Indexed: 12/20/2022]
Abstract
The explosive growth of artificial intelligence (AI) technologies, especially deep learning methods, has been translated at revolutionary speed to efforts in AI-assisted healthcare. New applications of AI to renal pathology have recently become available, driven by the successful AI deployments in digital pathology. However, synergetic developments of renal pathology and AI require close interdisciplinary collaborations between computer scientists and renal pathologists. Computer scientists should understand that not every AI innovation is translatable to renal pathology, while renal pathologists should capture high-level principles of the relevant AI technologies. Herein, we provide an integrated review on current and possible future applications in AI-assisted renal pathology, by including perspectives from computer scientists and renal pathologists. First, the standard stages, from data collection to analysis, in full-stack AI-assisted renal pathology studies are reviewed. Second, representative renal pathology-optimized AI techniques are introduced. Last, we review current clinical AI applications, as well as promising future applications with the recent advances in AI.
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Affiliation(s)
- Yuankai Huo
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Ruining Deng
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Quan Liu
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Agnes B Fogo
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Haichun Yang
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
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