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Kapasi A, Poirier J, Hedayat A, Scherlek A, Mondal S, Wu T, Gibbons J, Barnes LL, Bennett DA, Leurgans SE, Schneider JA. High-throughput digital quantification of Alzheimer disease pathology and associated infrastructure in large autopsy studies. J Neuropathol Exp Neurol 2023; 82:976-986. [PMID: 37944065 PMCID: PMC11032710 DOI: 10.1093/jnen/nlad086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2023] Open
Abstract
High-throughput digital pathology offers considerable advantages over traditional semiquantitative and manual methods of counting pathology. We used brain tissue from 5 clinical-pathologic cohort studies of aging; the Religious Orders Study, the Rush Memory and Aging Project, the Minority Aging Research Study, the African American Clinical Core, and the Latino Core to (1) develop a workflow management system for digital pathology processes, (2) optimize digital algorithms to quantify Alzheimer disease (AD) pathology, and (3) harmonize data statistically. Data from digital algorithms for the quantification of β-amyloid (Aβ, n = 413) whole slide images and tau-tangles (n = 639) were highly correlated with manual pathology data (r = 0.83 to 0.94). Measures were robust and reproducible across different magnifications and repeated scans. Digital measures for Aβ and tau-tangles across multiple brain regions reproduced established patterns of correlations, even when samples were stratified by clinical diagnosis. Finally, we harmonized newly generated digital measures with historical measures across multiple large autopsy-based studies. We describe a multidisciplinary approach to develop a digital pathology pipeline that reproducibly identifies AD neuropathologies, Aβ load, and tau-tangles. Digital pathology is a powerful tool that can overcome critical challenges associated with traditional microscopy methods.
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Affiliation(s)
- Alifiya Kapasi
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, Illinois, USA
- Department of Pathology, Rush University Medical Center, Chicago, Illinois, USA
| | - Jennifer Poirier
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, Illinois, USA
| | - Ahmad Hedayat
- Department of Pathology, Washington University School of Medicine, St Louis, Missouri, USA
| | - Ashley Scherlek
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, Illinois, USA
| | - Srabani Mondal
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, Illinois, USA
| | - Tiffany Wu
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, Illinois, USA
| | - John Gibbons
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, Illinois, USA
| | - Lisa L Barnes
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, Illinois, USA
- Department of Neurological Sciences, Rush University Medical Center, Chicago, Illinois, USA
| | - David A Bennett
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, Illinois, USA
- Department of Neurological Sciences, Rush University Medical Center, Chicago, Illinois, USA
| | - Sue E Leurgans
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, Illinois, USA
- Department of Neurological Sciences, Rush University Medical Center, Chicago, Illinois, USA
| | - Julie A Schneider
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, Illinois, USA
- Department of Pathology, Rush University Medical Center, Chicago, Illinois, USA
- Department of Neurological Sciences, Rush University Medical Center, Chicago, Illinois, USA
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You C, Dai W, Min Y, Staib L, Duncan JS. Implicit Anatomical Rendering for Medical Image Segmentation with Stochastic Experts. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2023; 14222:561-571. [PMID: 38840671 PMCID: PMC11151725 DOI: 10.1007/978-3-031-43898-1_54] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2024]
Abstract
Integrating high-level semantically correlated contents and low-level anatomical features is of central importance in medical image segmentation. Towards this end, recent deep learning-based medical segmentation methods have shown great promise in better modeling such information. However, convolution operators for medical segmentation typically operate on regular grids, which inherently blur the high-frequency regions, i.e., boundary regions. In this work, we propose MORSE, a generic implicit neural rendering framework designed at an anatomical level to assist learning in medical image segmentation. Our method is motivated by the fact that implicit neural representation has been shown to be more effective in fitting complex signals and solving computer graphics problems than discrete grid-based representation. The core of our approach is to formulate medical image segmentation as a rendering problem in an end-to-end manner. Specifically, we continuously align the coarse segmentation prediction with the ambiguous coordinate-based point representations and aggregate these features to adaptively refine the boundary region. To parallelly optimize multi-scale pixel-level features, we leverage the idea from Mixture-of-Expert (MoE) to design and train our MORSE with a stochastic gating mechanism. Our experiments demonstrate that MORSE can work well with different medical segmentation backbones, consistently achieving competitive performance improvements in both 2D and 3D supervised medical segmentation methods. We also theoretically analyze the superiority of MORSE.
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Affiliation(s)
- Chenyu You
- Department of Electrical Engineering, Yale University
| | - Weicheng Dai
- Department of Radiology and Biomedical Imaging, Yale University
| | - Yifei Min
- Department of Statistics and Data Science, Yale University
| | - Lawrence Staib
- Department of Electrical Engineering, Yale University
- Department of Radiology and Biomedical Imaging, Yale University
- Department of Biomedical Engineering, Yale University
| | - James S Duncan
- Department of Electrical Engineering, Yale University
- Department of Radiology and Biomedical Imaging, Yale University
- Department of Biomedical Engineering, Yale University
- Department of Statistics and Data Science, Yale University
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Oliveira LC, Lai Z, Harvey D, Nzenkue K, Jin LW, Decarli C, Chuah CN, Dugger BN. Preanalytic variable effects on segmentation and quantification machine learning algorithms for amyloid-β analyses on digitized human brain slides. J Neuropathol Exp Neurol 2023; 82:212-220. [PMID: 36692190 PMCID: PMC9941825 DOI: 10.1093/jnen/nlac132] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
Abstract
Computational machine learning (ML)-based frameworks could be advantageous for scalable analyses in neuropathology. A recent deep learning (DL) framework has shown promise in automating the processes of visualizing and quantifying different types of amyloid-β deposits as well as segmenting white matter (WM) from gray matter (GM) on digitized immunohistochemically stained slides. However, this framework has only been trained and evaluated on amyloid-β-stained slides with minimal changes in preanalytic variables. In this study, we evaluated select preanalytical variables including magnification, compression rate, and storage format using three digital slides scanners (Zeiss Axioscan Z1, Leica Aperio AT2, and Leica Aperio GT 450), on over 60 whole slide images, in a cohort of 14 cases having a spectrum of amyloid-β deposits. We conducted statistical comparisons of preanalytic variables with repeated measures analysis of variance evaluating the outputs of two DL frameworks for segmentation and object classification tasks. For both WM/GM segmentation and amyloid-β plaque classification tasks, there were statistical differences with respect to scanner types (p < 0.05) and magnifications (p < 0.05). Although small numbers of cases were analyzed, this pilot study highlights the significance of preanalytic variables that may alter the performance of ML algorithms.
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Affiliation(s)
- Luca Cerny Oliveira
- Department of Electrical and Computer Engineering, University of California Davis, Davis, California, USA
| | - Zhengfeng Lai
- Department of Electrical and Computer Engineering, University of California Davis, Davis, California, USA
| | - Danielle Harvey
- Department of Public Health Sciences, University of California, Davis, Davis, California, USA
| | - Kevin Nzenkue
- Department of Pathology and Laboratory Medicine, University of California Davis, Sacramento, California, USA
| | - Lee-Way Jin
- Department of Pathology and Laboratory Medicine, University of California Davis, Sacramento, California, USA
| | - Charles Decarli
- Department of Neurology, University of California, Davis, Sacramento, California, USA
| | - Chen-Nee Chuah
- Department of Electrical and Computer Engineering, University of California Davis, Davis, California, USA
| | - Brittany N Dugger
- Department of Pathology and Laboratory Medicine, University of California Davis, Sacramento, California, USA
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