781 research outputs found
Novel structural-scale uncertainty measures and error retention curves: application to multiple sclerosis
This paper focuses on the uncertainty estimation for white matter lesions
(WML) segmentation in magnetic resonance imaging (MRI). On one side,
voxel-scale segmentation errors cause the erroneous delineation of the lesions;
on the other side, lesion-scale detection errors lead to wrong lesion counts.
Both of these factors are clinically relevant for the assessment of multiple
sclerosis patients. This work aims to compare the ability of different voxel-
and lesion-scale uncertainty measures to capture errors related to segmentation
and lesion detection, respectively. Our main contributions are (i) proposing
new measures of lesion-scale uncertainty that do not utilise voxel-scale
uncertainties; (ii) extending an error retention curves analysis framework for
evaluation of lesion-scale uncertainty measures. Our results obtained on the
multi-center testing set of 58 patients demonstrate that the proposed
lesion-scale measure achieves the best performance among the analysed measures.
All code implementations are provided at
https://github.com/NataliiaMolch/MS_WML_uncsComment: 4 pages, 2 figures, 3 tables, ISBI preprin
Structural-Based Uncertainty in Deep Learning Across Anatomical Scales: Analysis in White Matter Lesion Segmentation
This paper explores uncertainty quantification (UQ) as an indicator of the
trustworthiness of automated deep-learning (DL) tools in the context of white
matter lesion (WML) segmentation from magnetic resonance imaging (MRI) scans of
multiple sclerosis (MS) patients. Our study focuses on two principal aspects of
uncertainty in structured output segmentation tasks. Firstly, we postulate that
a good uncertainty measure should indicate predictions likely to be incorrect
with high uncertainty values. Second, we investigate the merit of quantifying
uncertainty at different anatomical scales (voxel, lesion, or patient). We
hypothesize that uncertainty at each scale is related to specific types of
errors. Our study aims to confirm this relationship by conducting separate
analyses for in-domain and out-of-domain settings. Our primary methodological
contributions are (i) the development of novel measures for quantifying
uncertainty at lesion and patient scales, derived from structural prediction
discrepancies, and (ii) the extension of an error retention curve analysis
framework to facilitate the evaluation of UQ performance at both lesion and
patient scales. The results from a multi-centric MRI dataset of 172 patients
demonstrate that our proposed measures more effectively capture model errors at
the lesion and patient scales compared to measures that average voxel-scale
uncertainty values. We provide the UQ protocols code at
https://github.com/Medical-Image-Analysis-Laboratory/MS_WML_uncs.Comment: Preprint submitted to the journa
Opportunities for Understanding MS Mechanisms and Progression With MRI Using Large-Scale Data Sharing and Artificial Intelligence
Multiple sclerosis (MS) patients have heterogeneous clinical presentations, symptoms and progression over time, making MS difficult to assess and comprehend in vivo. The combination of large-scale data-sharing and artificial intelligence creates new opportunities for monitoring and understanding MS using magnetic resonance imaging (MRI).First, development of validated MS-specific image analysis methods can be boosted by verified reference, test and benchmark imaging data. Using detailed expert annotations, artificial intelligence algorithms can be trained on such MS-specific data. Second, understanding disease processes could be greatly advanced through shared data of large MS cohorts with clinical, demographic and treatment information. Relevant patterns in such data that may be imperceptible to a human observer could be detected through artificial intelligence techniques. This applies from image analysis (lesions, atrophy or functional network changes) to large multi-domain datasets (imaging, cognition, clinical disability, genetics, etc.).After reviewing data-sharing and artificial intelligence, this paper highlights three areas that offer strong opportunities for making advances in the next few years: crowdsourcing, personal data protection, and organized analysis challenges. Difficulties as well as specific recommendations to overcome them are discussed, in order to best leverage data sharing and artificial intelligence to improve image analysis, imaging and the understanding of MS
Uncertainty-driven refinement of tumor-core segmentation using 3D-to-2D networks with label uncertainty
The BraTS dataset contains a mixture of high-grade and low-grade gliomas,
which have a rather different appearance: previous studies have shown that
performance can be improved by separated training on low-grade gliomas (LGGs)
and high-grade gliomas (HGGs), but in practice this information is not
available at test time to decide which model to use. By contrast with HGGs,
LGGs often present no sharp boundary between the tumor core and the surrounding
edema, but rather a gradual reduction of tumor-cell density.
Utilizing our 3D-to-2D fully convolutional architecture, DeepSCAN, which
ranked highly in the 2019 BraTS challenge and was trained using an
uncertainty-aware loss, we separate cases into those with a confidently
segmented core, and those with a vaguely segmented or missing core. Since by
assumption every tumor has a core, we reduce the threshold for classification
of core tissue in those cases where the core, as segmented by the classifier,
is vaguely defined or missing.
We then predict survival of high-grade glioma patients using a fusion of
linear regression and random forest classification, based on age, number of
distinct tumor components, and number of distinct tumor cores.
We present results on the validation dataset of the Multimodal Brain Tumor
Segmentation Challenge 2020 (segmentation and uncertainty challenge), and on
the testing set, where the method achieved 4th place in Segmentation, 1st place
in uncertainty estimation, and 1st place in Survival prediction.Comment: Presented (virtually) in the MICCAI Brainles workshop 2020. Accepted
for publication in Brainles proceeding
Exploring variability in medical imaging
Although recent successes of deep learning and novel machine learning techniques improved the perfor-
mance of classification and (anomaly) detection in computer vision problems, the application of these
methods in medical imaging pipeline remains a very challenging task. One of the main reasons for this
is the amount of variability that is encountered and encapsulated in human anatomy and subsequently
reflected in medical images. This fundamental factor impacts most stages in modern medical imaging
processing pipelines.
Variability of human anatomy makes it virtually impossible to build large datasets for each disease
with labels and annotation for fully supervised machine learning. An efficient way to cope with this is
to try and learn only from normal samples. Such data is much easier to collect. A case study of such
an automatic anomaly detection system based on normative learning is presented in this work. We
present a framework for detecting fetal cardiac anomalies during ultrasound screening using generative
models, which are trained only utilising normal/healthy subjects.
However, despite the significant improvement in automatic abnormality detection systems, clinical
routine continues to rely exclusively on the contribution of overburdened medical experts to diagnosis
and localise abnormalities. Integrating human expert knowledge into the medical imaging processing
pipeline entails uncertainty which is mainly correlated with inter-observer variability. From the per-
spective of building an automated medical imaging system, it is still an open issue, to what extent
this kind of variability and the resulting uncertainty are introduced during the training of a model
and how it affects the final performance of the task. Consequently, it is very important to explore the
effect of inter-observer variability both, on the reliable estimation of model’s uncertainty, as well as
on the model’s performance in a specific machine learning task. A thorough investigation of this issue
is presented in this work by leveraging automated estimates for machine learning model uncertainty,
inter-observer variability and segmentation task performance in lung CT scan images.
Finally, a presentation of an overview of the existing anomaly detection methods in medical imaging
was attempted. This state-of-the-art survey includes both conventional pattern recognition methods
and deep learning based methods. It is one of the first literature surveys attempted in the specific
research area.Open Acces
Beyond Voxel Prediction Uncertainty: Identifying brain lesions you can trust
Deep neural networks have become the gold-standard approach for the automated
segmentation of 3D medical images. Their full acceptance by clinicians remains
however hampered by the lack of intelligible uncertainty assessment of the
provided results. Most approaches to quantify their uncertainty, such as the
popular Monte Carlo dropout, restrict to some measure of uncertainty in
prediction at the voxel level. In addition not to be clearly related to genuine
medical uncertainty, this is not clinically satisfying as most objects of
interest (e.g. brain lesions) are made of groups of voxels whose overall
relevance may not simply reduce to the sum or mean of their individual
uncertainties. In this work, we propose to go beyond voxel-wise assessment
using an innovative Graph Neural Network approach, trained from the outputs of
a Monte Carlo dropout model. This network allows the fusion of three estimators
of voxel uncertainty: entropy, variance, and model's confidence; and can be
applied to any lesion, regardless of its shape or size. We demonstrate the
superiority of our approach for uncertainty estimate on a task of Multiple
Sclerosis lesions segmentation.Comment: Accepted for presentation at the Workshop on Interpretability of
Machine Intelligence in Medical Image Computing (iMIMIC) at MICCAI 202
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