Simultaneous segmentation and grading of anatomical structures for patient's classification: Application to Alzheimer's disease

Coupé, Pierrick; Eskildsen, Simon Fristed; Manjón, José V.; Fonov, Vladimir S.; Collins, D. Louis

doi:10.1016/j.neuroimage.2011.10.080

Cited by 134 publications

(141 citation statements)

References 55 publications

Supporting

Mentioning

134

Contrasting

Order By: Relevance

“…Patch-based segmentation methods [5] perform well on conventional T1w images, such as Magnetization Prepared Rapid Acquisition Gradient Echo (MPRAGE) [12]. To the best of our knowledge, the accuracy of different automated segmentation methods has not yet been compared using T1w images from the recently proposed MP2RAGE sequence, which significantly reduces the intensity bias and provides superior grey matter (GM) to white matter (WM) contrast [13].…”

Section: Introductionmentioning

confidence: 99%

Automatic thalamus and hippocampus segmentation from MP2RAGE: comparison of publicly available methods and implications for DTI quantification

et al. 2016

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

Automatic thalamus and hippocampus segmentation from MP2RAGE: comparison of publicly available methods and implications for DTI quantification

et al. 2016

Self Cite

View full text Add to dashboard Cite

show abstract

“…The 51.5% without PCA and 53.7% percentage indicates that classification was undertaken at random, as one half was being properly classified and the other half poorly classified. However, in the other cases, the worst percentage obtained was 82.8% for the Neural Network (supervised) classifier with a hidden internal layer without applying PCA, while the best was the ADABoost (semi-supervised) classifier, which obtained a 90% percentage [19] both with and without PCA. It makes sense that this classifier obtained the best percentage, as it learned the error that had been committed in each iteration from the classifier itself and adjusted this so as to improve the classification.…”

Section: Discussionmentioning

confidence: 93%

HClass: Automatic classification tool for health pathologies using artificial intelligence techniques

Garcia-Chimeno

Garcia‐Zapirain

2015

BME

View full text Add to dashboard Cite

Abstract. The classification of subjects' pathologies enables a rigorousness to be applied to the treatment of certain pathologies, as doctors on occasions play with so many variables that they can end up confusing some illnesses with others. Thanks to Machine Learning techniques applied to a health-record database, it is possible to make using our algorithm. hClass contains a non-linear classification of either a supervised, non-supervised or semi-supervised type. The machine is configured using other techniques such as validation of the set to be classified (cross-validation), reduction in features (PCA) and committees for assessing the various classifiers. The tool is easy to use, and the sample matrix and features that one wishes to classify, the number of iterations and the subjects who are going to be used to train the machine all need to be introduced as inputs. As a result, the success rate is shown either via a classifier or via a committee if one has been formed. A 90% success rate is obtained in the ADABoost classifier and 89.7% in the case of a committee (comprising three classifiers) when PCA is applied. This tool can be expanded to allow the user to totally characterise the classifiers by adjusting them to each classification use.

show abstract

“…The goal of grading features [9,47,46] is the scoring of a test subject by estimating its similarity to different training subjects. There are two main steps in their calculation: ROI learning using sparse regression as described in [18], and disease label propagation from training to test subjects.…”

Section: Datamentioning

confidence: 99%

Instantiated mixed effects modeling of Alzheimer's disease markers

et al. 2016

View full text Add to dashboard Cite

The assessment and prediction of a subject's current and future risk of developing neurodegenerative diseases like Alzheimer's disease is of great interest in both the design of clinical trials as well as in clinical decision making. Exploring the longitudinal trajectory of markers related to neurodegeneration is an important task when selecting subjects for treatment in trials and the clinic, in the evaluation of early disease indicators and the monitoring of disease progression. Given that there is substantial intersubject variability, models that attempt to describe marker trajectories for a whole population will likely lack specificity for the representation of individual patients. Therefore, we argue here that individualized models provide a more accurate alternative that can be used for tasks such as population stratification and a subject-specific prognosis. In the work presented here, mixed effects modeling is used to derive a global and individual marker trajectories for a training population. Test subject (new patient) specific models are then instantiated using a stratified "marker signature" that defines a subpopulation of similar * Corresponding author: Ricardo Guerrero Email address: reg09@imperial.ac.uk (R. Guerrero) 1 This project was partially funded by the Innovate UK (formerly Technology Strategy Board -TSB).2 Data used in the preparation of this article was obtained from the ADNI database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how to apply/ ADNI Acknowledgement List.pdf Preprint submitted to NeuroImage June 27, 2016 cases within the training database. From this subpopulation, personalized models of the expected trajectory of several markers are subsequently estimated for unseen patients. These patient specific models of markers are shown to provide better predictions of time-to-conversion to Alzheimer's disease than population based models.

show abstract

Simultaneous segmentation and grading of anatomical structures for patient's classification: Application to Alzheimer's disease

Cited by 134 publications

References 55 publications

Automatic thalamus and hippocampus segmentation from MP2RAGE: comparison of publicly available methods and implications for DTI quantification

Automatic thalamus and hippocampus segmentation from MP2RAGE: comparison of publicly available methods and implications for DTI quantification

HClass: Automatic classification tool for health pathologies using artificial intelligence techniques

Instantiated mixed effects modeling of Alzheimer's disease markers

Contact Info

Product

Resources

About