1,075 research outputs found
Two Time Point MS Lesion Segmentation in Brain MRI:An Expectation-Maximization Framework
Purpose: Lesion volume is a meaningful measure in multiple sclerosis (MS) prognosis. Manual lesion segmentation for computing volume in a single or multiple time points is time consuming and suffers from intra and inter-observer variability. Methods: In this paper, we present MSmetrix-long: a joint expectation-maximization (EM) framework for two time point white matter (WM) lesion segmentation. MSmetrix-long takes as input a 3D T1-weighted and a 3D FLAIR MR image and segments lesions in three steps: (1) cross-sectional lesion segmentation of the two time points; (2) creation of difference image, which is used to model the lesion evolution; (3) a joint EM lesion segmentation framework that uses output of step (1) and step (2) to provide the final lesion segmentation. The accuracy (Dice score) and reproducibility (absolute lesion volume difference) of MSmetrix-long is evaluated using two datasets. Results: On the first dataset, the median Dice score between MSmetrix-long and expert lesion segmentation was 0.63 and the Pearson correlation coefficient (PCC) was equal to 0.96. On the second dataset, the median absolute volume difference was 0.11 ml. Conclusions: MSmetrix-long is accurate and consistent in segmenting MS lesions. Also, MSmetrix-long compares favorably with the publicly available longitudinal MS lesion segmentation algorithm of Lesion Segmentation Toolbox
HACA3: A Unified Approach for Multi-site MR Image Harmonization
The lack of standardization is a prominent issue in magnetic resonance (MR)
imaging. This often causes undesired contrast variations due to differences in
hardware and acquisition parameters. In recent years, MR harmonization using
image synthesis with disentanglement has been proposed to compensate for the
undesired contrast variations. Despite the success of existing methods, we
argue that three major improvements can be made. First, most existing methods
are built upon the assumption that multi-contrast MR images of the same subject
share the same anatomy. This assumption is questionable since different MR
contrasts are specialized to highlight different anatomical features. Second,
these methods often require a fixed set of MR contrasts for training (e.g.,
both Tw-weighted and T2-weighted images must be available), which limits their
applicability. Third, existing methods generally are sensitive to imaging
artifacts. In this paper, we present a novel approach, Harmonization with
Attention-based Contrast, Anatomy, and Artifact Awareness (HACA3), to address
these three issues. We first propose an anatomy fusion module that enables
HACA3 to respect the anatomical differences between MR contrasts. HACA3 is also
robust to imaging artifacts and can be trained and applied to any set of MR
contrasts. Experiments show that HACA3 achieves state-of-the-art performance
under multiple image quality metrics. We also demonstrate the applicability of
HACA3 on downstream tasks with diverse MR datasets acquired from 21 sites with
different field strengths, scanner platforms, and acquisition protocols
Improved inter-scanner MS lesion segmentation by adversarial training on longitudinal data
The evaluation of white matter lesion progression is an important biomarker
in the follow-up of MS patients and plays a crucial role when deciding the
course of treatment. Current automated lesion segmentation algorithms are
susceptible to variability in image characteristics related to MRI scanner or
protocol differences. We propose a model that improves the consistency of MS
lesion segmentations in inter-scanner studies. First, we train a CNN base model
to approximate the performance of icobrain, an FDA-approved clinically
available lesion segmentation software. A discriminator model is then trained
to predict if two lesion segmentations are based on scans acquired using the
same scanner type or not, achieving a 78% accuracy in this task. Finally, the
base model and the discriminator are trained adversarially on multi-scanner
longitudinal data to improve the inter-scanner consistency of the base model.
The performance of the models is evaluated on an unseen dataset containing
manual delineations. The inter-scanner variability is evaluated on test-retest
data, where the adversarial network produces improved results over the base
model and the FDA-approved solution.Comment: MICCAI BrainLes 2019 Worksho
A large margin algorithm for automated segmentation of white matter hyperintensity
Precise detection and quantification of white matter hyperintensity (WMH) is of great interest in studies of neurological and vascular disorders. In this work, we propose a novel method for automatic WMH segmentation with both supervised and semi-supervised large margin algorithms provided by the framework. The proposed algorithms optimize a kernel based max-margin objective function which aims to maximize the margin between inliers and outliers. We show that the semi-supervised learning problem can be formulated to learn a classifier and label assignment simultaneously, which can be solved efficiently by an iterative algorithm. The model is learned first via the supervised approach and then fine-tuned on a target image by using the semi-supervised algorithm. We evaluate our method on 88 brain fluid-attenuated inversion recovery (FLAIR) magnetic resonance (MR) images from subjects with vascular disease. Quantitative evaluation of the proposed approach shows that it outperforms other well known methods for WMH segmentation
Test-time Unsupervised Domain Adaptation
Convolutional neural networks trained on publicly available medical imaging
datasets (source domain) rarely generalise to different scanners or acquisition
protocols (target domain). This motivates the active field of domain
adaptation. While some approaches to the problem require labeled data from the
target domain, others adopt an unsupervised approach to domain adaptation
(UDA). Evaluating UDA methods consists of measuring the model's ability to
generalise to unseen data in the target domain. In this work, we argue that
this is not as useful as adapting to the test set directly. We therefore
propose an evaluation framework where we perform test-time UDA on each subject
separately. We show that models adapted to a specific target subject from the
target domain outperform a domain adaptation method which has seen more data of
the target domain but not this specific target subject. This result supports
the thesis that unsupervised domain adaptation should be used at test-time,
even if only using a single target-domain subjectComment: Accepted at MICCAI 202
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