| 1. |
- Adl, Sina M., et al.
(författare)
-
Revisions to the Classification, Nomenclature, and Diversity of Eukaryotes
- 2019
-
Ingår i: Journal of Eukaryotic Microbiology. - : WILEY. - 1066-5234 .- 1550-7408. ; 66:1, s. 4-119
-
Tidskriftsartikel (refereegranskat)abstract
- This revision of the classification of eukaryotes follows that of Adl et al., 2012 [J. Euk. Microbiol. 59(5)] and retains an emphasis on protists. Changes since have improved the resolution of many nodes in phylogenetic analyses. For some clades even families are being clearly resolved. As we had predicted, environmental sampling in the intervening years has massively increased the genetic information at hand. Consequently, we have discovered novel clades, exciting new genera and uncovered a massive species level diversity beyond the morphological species descriptions. Several clades known from environmental samples only have now found their home. Sampling soils, deeper marine waters and the deep sea will continue to fill us with surprises. The main changes in this revision are the confirmation that eukaryotes form at least two domains, the loss of monophyly in the Excavata, robust support for the Haptista and Cryptista. We provide suggested primer sets for DNA sequences from environmental samples that are effective for each clade. We have provided a guide to trophic functional guilds in an appendix, to facilitate the interpretation of environmental samples, and a standardized taxonomic guide for East Asian users.
|
|
| 2. |
- Kamnitsas, Konstantinos, et al.
(författare)
-
Transductive Image Segmentation : Self-training and Effect of Uncertainty Estimation
- 2021
-
Ingår i: Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health - 3rd MICCAI Workshop, DART 2021, and 1st MICCAI Workshop, FAIR 2021, Held in Conjunction with MICCAI 2021, Proceedings. - Cham : Springer International Publishing. - 1611-3349 .- 0302-9743. - 9783030877217 ; 12968 LNCS, s. 79-89
-
Konferensbidrag (refereegranskat)abstract
- Semi-supervised learning (SSL) uses unlabeled data during training to learn better models. Previous studies on SSL for medical image segmentation focused mostly on improving model generalization to unseen data. In some applications, however, our primary interest is not generalization but to obtain optimal predictions on a specific unlabeled database that is fully available during model development. Examples include population studies for extracting imaging phenotypes. This work investigates an often overlooked aspect of SSL, transduction. It focuses on the quality of predictions made on the unlabeled data of interest when they are included for optimization during training, rather than improving generalization. We focus on the self-training framework and explore its potential for transduction. We analyze it through the lens of Information Gain and reveal that learning benefits from the use of calibrated or under-confident models. Our extensive experiments on a large MRI database for multi-class segmentation of traumatic brain lesions shows promising results when comparing transductive with inductive predictions. We believe this study will inspire further research on transductive learning, a well-suited paradigm for medical image analysis.
|
|
| 3. |
- Antila, Kari, et al.
(författare)
-
The PredictAD project : development of novel biomarkers and analysis software for early diagnosis of the Alzheimer's disease
- 2013
-
Ingår i: Interface Focus. - : The Royal Society Publishing. - 2042-8898 .- 2042-8901. ; 3:2
-
Tidskriftsartikel (refereegranskat)abstract
- Alzheimer's disease (AD) is the most common cause of dementia affecting 36 million people worldwide. As the demographic transition in the developed countries progresses towards older population, the worsening ratio of workers per retirees and the growing number of patients with age-related illnesses such as AD will challenge the current healthcare systems and national economies. For these reasons AD has been identified as a health priority, and various methods for diagnosis and many candidates for therapies are under intense research. Even though there is currently no cure for AD, its effects can be managed. Today the significance of early and precise diagnosis of AD is emphasized in order to minimize its irreversible effects on the nervous system. When new drugs and therapies enter the market it is also vital to effectively identify the right candidates to benefit from these. The main objective of the PredictAD project was to find and integrate efficient biomarkers from heterogeneous patient data to make early diagnosis and to monitor the progress of AD in a more efficient, reliable and objective manner. The project focused on discovering biomarkers from biomolecular data, electrophysiological measurements of the brain and structural, functional and molecular brain images. We also designed and built a statistical model and a framework for exploiting these biomarkers with other available patient history and background data. We were able to discover several potential novel biomarker candidates and implement the framework in software. The results are currently used in several research projects, licensed to commercial use and being tested for clinical use in several trials.
|
|
| 4. |
- Bernard, Olivier, et al.
(författare)
-
Standardized evaluation system for left ventricular segmentation algorithms in 3D echocardiography.
- 2016
-
Ingår i: IEEE Transactions on Medical Imaging. - : Institute of Electrical and Electronics Engineers (IEEE). - 0278-0062 .- 1558-254X. ; 35:4, s. 967-977
-
Tidskriftsartikel (refereegranskat)abstract
- Real-time 3D Echocardiography (RT3DE) has been proven to be an accurate tool for left ventricular (LV) volume assessment. However, identification of the LV endocardium remains a challenging task, mainly because of the low tissue/blood contrast of the images combined with typical artifacts. Several semi and fully automatic algorithms have been proposed for segmenting the endocardium in RT3DE data in order to extract relevant clinical indices, but a systematic and fair comparison between such methods has so far been impossible due to the lack of a publicly available common database. Here, we introduce a standardized evaluation framework to reliably evaluate and compare the performance of the algorithms developed to segment the LV border in RT3DE. A database consisting of 45 multivendor cardiac ultrasound recordings acquired at different centers with corresponding reference measurements from 3 experts are made available. The algorithms from nine research groups were quantitatively evaluated and compared using the proposed online platform. The results showed that the best methods produce promising results with respect to the experts' measurements for the extraction of clinical indices, and that they offer good segmentation precision in terms of mean distance error in the context of the experts' variability range. The platform remains open for new submissions.
|
|
| 5. |
- Clerx, Lies, et al.
(författare)
-
Measurements of medial temporal lobe atrophy for prediction of Alzheimer's disease in subjects with mild cognitive impairment
- 2013
-
Ingår i: Neurobiology of Aging. - : Elsevier BV. - 1558-1497 .- 0197-4580. ; 34:8, s. 2003-2013
-
Tidskriftsartikel (refereegranskat)abstract
- Our aim was to compare the predictive accuracy of 4 different medial temporal lobe measurements for Alzheimer's disease (AD) in subjects with mild cognitive impairment (MCI). Manual hippocampal measurement, automated atlas-based hippocampal measurement, a visual rating scale (MTA-score), and lateral ventricle measurement were compared. Predictive accuracy for AD 2 years after baseline was assessed by receiver operating characteristics analyses with area under the curve as outcome. Annual cognitive decline was assessed by slope analyses up to 5 years after baseline. Correlations with biomarkers in cerebrospinal fluid (CSF) were investigated. Subjects with MCI were selected from the Development of Screening Guidelines and Clinical Criteria for Predementia AD (DESCRIPA) multicenter study (n = 156) and the single-center VU medical center (n = 172). At follow-up, area under the curve was highest for automated atlas-based hippocampal measurement (0.71) and manual hippocampal measurement (0.71), and lower for MTA-score (0.65) and lateral ventricle (0.60). Slope analysis yielded similar results. Hippocampal measurements correlated with CSF total tau and phosphorylated tau, not with beta-amyloid 1-42. MTA-score and lateral ventricle volume correlated with CSF beta-amyloid 1-42. We can conclude that volumetric hippocampal measurements are the best predictors of AD conversion in subjects with MCI. (c) 2013 Elsevier Inc. All rights reserved.
|
|
| 6. |
- Kofler, Florian, et al.
(författare)
-
Approaching Peak Ground Truth
- 2023
-
Ingår i: Proceedings - International Symposium on Biomedical Imaging. - 1945-7928 .- 1945-8452. - 9781665473583
-
Konferensbidrag (refereegranskat)abstract
- Machine learning models are typically evaluated by computing similarity with reference annotations and trained by maximizing similarity with such. Especially in the biomedical domain, annotations are subjective and suffer from low inter-and intra-rater reliability. Since annotations only reflect one interpretation of the real world, this can lead to sub-optimal predictions even though the model achieves high similarity scores. Here, the theoretical concept of Peak Ground Truth (PGT) is introduced. PGT marks the point beyond which an increase in similarity with the reference annotation stops translating to better Real World Model Performance (RWMP). Additionally, a quantitative technique to approximate PGT by computing inter- and intra-rater reliability is proposed. Finally, four categories of PGT-aware strategies to evaluate and improve model performance are reviewed.
|
|
| 7. |
- Munoz-Ruiz, Miguel Angel, et al.
(författare)
-
Structural MRI in Frontotemporal Dementia : Comparisons between Hippocampal Volumetry, Tensor-Based Morphometry and Voxel-Based Morphometry
- 2012
-
Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 7:12
-
Tidskriftsartikel (refereegranskat)abstract
- Background: MRI is an important clinical tool for diagnosing dementia-like diseases such as Frontemporal Dementia (FTD). However there is a need to develop more accurate and standardized MRI analysis methods. Objective: To compare FTD with Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) with three automatic MRI analysis methods - Hippocampal Volumetry (HV), Tensor-based Morphometry (TBM) and Voxel-based Morphometry (VBM), in specific regions of interest in order to determine the highest classification accuracy. Methods: Thirty-seven patients with FTD, 46 patients with AD, 26 control subjects, 16 patients with progressive MCI (PMCI) and 48 patients with stable MCI (SMCI) were examined with HV, TBM for shape change, and VBM for gray matter density. We calculated the Correct Classification Rate (CCR), sensitivity (SS) and specificity (SP) between the study groups. Results: We found unequivocal results differentiating controls from FTD with HV (hippocampus left side) (CCR = 0.83; SS = 0.84; SP = 0.80), with TBM (hippocampus and amygdala (CCR = 0.80/SS = 0.71/SP = 0.94), and with VBM (all the regions studied, especially in lateral ventricle frontal horn, central part and occipital horn) (CCR = 0.87/SS = 0.81/SP = 0.96). VBM achieved the highest accuracy in differentiating AD and FTD (CCR = 0.72/SS = 0.67/SP = 0.76), particularly in lateral ventricle (frontal horn, central part and occipital horn) (CCR = 0.73), whereas TBM in superior frontal gyrus also achieved a high accuracy (CCR = 0.71/SS = 0.68/SP = 0.73). TBM resulted in low accuracy (CCR = 0.62) in the differentiation of AD from FTD using all regions of interest, with similar results for HV (CCR = 0.55). Conclusion: Hippocampal atrophy is present not only in AD but also in FTD. Of the methods used, VBM achieved the highest accuracy in its ability to differentiate between FTD and AD.
|
|
| 8. |
- van Rossum, Ineke A, et al.
(författare)
-
Injury markers predict time to dementia in subjects with MCI and amyloid pathology.
- 2012
-
Ingår i: Neurology. - 1526-632X. ; 79:17, s. 1809-16
-
Tidskriftsartikel (refereegranskat)abstract
- Alzheimer disease (AD) can now be diagnosed in subjects with mild cognitive impairment (MCI) using biomarkers. However, little is known about the rate of decline in those subjects. In this cohort study, we aimed to assess the conversion rate to dementia and identify prognostic markers in subjects with MCI and evidence of amyloid pathology.
|
|
