Fully Automated and Adaptive Detection of Amyloid Plaques in Stained Brain Sections of Alzheimer Transgenic Mice

Feki, A.; Teboul, Olivier; Dubois, Albertine; Bozon, Bruno; Faure, Alexis; Hantraye, Philippe; Dhénain, Marc; Delatour, Benoı̂t; Delzescaux, Thierry

doi:10.1007/978-3-540-75759-7_116

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…This study describes an automatic segmentation algorithm that can be used for amyloid PL quantification in MR images. The high correlation between PLs quantified from MR imaging using the proposed algorithm and the gold standard histological measurements is comparable to that reported in literature for validating an automatic method of plaque segmentation from Congo Red‐stained brain sections (37, 38). Although Teboul et al (38) compared their method based on adaptive thresholds to expert segmentation of 2D histology sections, our validation scheme is more complex as our data include volumetric ROIs from multiple brain regions in images acquired by two different modalities.…”

Section: Discussionsupporting

confidence: 82%

Automatic segmentation of amyloid plaques in MR images using unsupervised support vector machines

Iordanescu

Venkatasubramanian

Wyrwicz

2011

Magnetic Resonance in Med

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Deposition of the β-amyloid peptide (Aβ) is an important pathological hallmark of Alzheimer’s disease (AD). However, reliable quantification of amyloid plaques in both human and animal brains remains a challenge. We present here a novel automatic plaque segmentation algorithm based on the intrinsic MR signal characteristics of plaques. This algorithm identifies plaque candidates in MR data by using watershed transform, which extracts regions with low intensities completely surrounded by higher intensity neighbors. These candidates are classified as plaque or non-plaque by an unsupervised learning method using features derived from the MR data intensity. The algorithm performance is validated by comparison with histology. We also demonstrate the algorithm’s ability to detect age-related changes in plaque load ex vivo in 5×FAD APP transgenic mice. To our knowledge, this work represents the first quantitative method for characterizing amyloid plaques in MRI data. The proposed method can be used to describe the spatio-temporal progression of amyloid deposition, which is necessary for understanding the evolution of plaque pathology in mouse models of AD and to evaluate the efficacy of emergent amyloid-targeting therapies in preclinical trials.

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Section: Discussionsupporting

confidence: 82%

Automatic segmentation of amyloid plaques in MR images using unsupervised support vector machines

Iordanescu

Venkatasubramanian

Wyrwicz

2011

Magnetic Resonance in Med

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“…In this context, global and adaptive thresholding are popular segmentation approaches for they are simple and fully automated [2,3]. However these methods are prone to errors.…”

Section: Introductionmentioning

confidence: 99%

Robust supervised segmentation of neuropathology whole-slide microscopy images

Vandenberghe

Balbastre

Souedet

et al. 2015

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Alzheimer's disease is characterized by brain pathological aggregates such as Aβ plaques and neurofibrillary tangles which trigger neuroinflammation and participate to neuronal loss. Quantification of these pathological markers on histological sections is widely performed to study the disease and to evaluate new therapies. However, segmentation of neuropathology images presents difficulties inherent to histology (presence of debris, tissue folding, non-specific staining) as well as specific challenges (sparse staining, irregular shape of the lesions). Here, we present a supervised classification approach for the robust pixel-level classification of large neuropathology whole slide images. We propose a weighted form of Random Forest in order to fit nonlinear decision boundaries that take into account class imbalance. Both color and texture descriptors were used as predictors and model selection was performed via a leave-one-image-out cross-validation scheme. Our method showed superior results compared to the current state of the art method when applied to the segmentation of Aβ plaques and neurofibrillary tangles in a human brain sample. Furthermore, using parallel computing, our approach easily scales-up to large gigabyte-sized images. To show this, we segmented a whole brain histology dataset of a mouse model of Alzheimer's disease. This demonstrates our method relevance as a routine tool for whole slide microscopy images analysis in clinical and preclinical research settings.

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“…Both sporadic and familial cases of ALS present accumulation of SOD1 and/or of TDP43 159 proteins; it is yet to be determined whether they are a source of toxicity for the cells or a defense mechanism adopted to segregate toxic material. HCA protocols for quantifying cellular aggregates have already been successfully validated for Huntington’s 160 and Alzheimer’s 161,162 disease. As demonstrated by some of the studies reviewed in this article, HCA systems can perform automated, unbiased quantification of aggregates in ALS settings at low throughput.…”

Section: Reviewmentioning

confidence: 99%