2018
DOI: 10.1101/494005
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Diffeomorphic registration with intensity transformation and missing data: Application to 3D digital pathology of Alzheimer's disease

Abstract: This paper examines the problem of diffeomorphic image mapping in the presence of differing image intensity profiles and missing data. Our motivation comes from the problem of aligning 3D brain MRI with 100 micron isotropic resolution, to histology sections with 1 micron in plane resolution. Multiple stains, as well as damaged, folded, or missing tissue are common in this situation. We overcome these challenges by introducing two new concepts. Cross modality image matching is achieved by jointly estimating pol… Show more

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Cited by 9 publications
(24 citation statements)
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“…Recently, deep-learning-based systems have achieved state-of-the-art performance in analyzing microscopy images (Moen et al, 2019). Many different architectures have been used, including our work detecting tau tangles with sliding windows to annotate single pixels (Tward et al, 2020), UNET (Ronneberger et al, 2015) for annotating larger blocks which has been implemented in FIJI (Falk et al, 2019), and other elaborations such as VNET (Milletari et al, 2016). Typically, trained networks assign class probabilities to each pixel, which are collected into larger objects based on connected components or watershed approaches available in standard packages, such as FIJI (Schindelin et al, 2012).…”
Section: Discussionmentioning
confidence: 99%
“…Recently, deep-learning-based systems have achieved state-of-the-art performance in analyzing microscopy images (Moen et al, 2019). Many different architectures have been used, including our work detecting tau tangles with sliding windows to annotate single pixels (Tward et al, 2020), UNET (Ronneberger et al, 2015) for annotating larger blocks which has been implemented in FIJI (Falk et al, 2019), and other elaborations such as VNET (Milletari et al, 2016). Typically, trained networks assign class probabilities to each pixel, which are collected into larger objects based on connected components or watershed approaches available in standard packages, such as FIJI (Schindelin et al, 2012).…”
Section: Discussionmentioning
confidence: 99%
“…The present study focused on inferring maps of key cellular structures in the mouse brain from multi-contrast MRI data. Previous works on this problem include: new MRI contrasts that capture specific aspects of cellular structures of interest (19,20); carefully constructed tissue models for MR signals (21); statistical methods to extract relevant information from multi-contrast MRI (2); and techniques to register histology and MRI data (22,23) for validation (24,25). Here, we built on these efforts by demonstrating that deep learning networks trained by co-registered histological and MRI data can improve our ability to detect target cellular structures.…”
Section: Discussionmentioning
confidence: 99%
“…As described previously [62, 36], we used a convolutional neural network to model and predict probabilities of being part of a tau tangle for each pixel in a digital histology image. To capture larger contextual features as well as local information for producing per pixel probabilities at high resolutions, we trained UNETs [63] with the architecture described in Table F2 (see Appendix F).…”
Section: Algorithm For Solving Projective Lddmm With In Plane Transfo...mentioning
confidence: 99%