Automated detection of multiple sclerosis lesions in serial brain MRI

Lladó, Xavier; Ganiler, Onur; Oliver, Arnau; Martí, Robert; Freixenet, Jordi; Valls, Laia; Vilanova, Joan C.; Ramió-Torrentà, Lluı́s; Rovira, Àlex

doi:10.1007/s00234-011-0992-6

Cited by 75 publications

(71 citation statements)

References 66 publications

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“…In contrast ALSAs are usually but not always tested on single time point images [19]. ALSAs, in essence, attempt to solve a harder problem: detect and outline lesions, without knowledge of where previous lesions were marked for that patient.…”

Section: Discussionmentioning

confidence: 99%

Improved operator agreement and efficiency using the minimum area contour change method for delineation of hyperintense multiple sclerosis lesions on FLAIR MRI

et al. 2013

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BackgroundActivity of disease in patients with multiple sclerosis (MS) is monitored by detecting and delineating hyper-intense lesions on MRI scans. The Minimum Area Contour Change (MACC) algorithm has been created with two main goals: a) to improve inter-operator agreement on outlining regions of interest (ROIs) and b) to automatically propagate longitudinal ROIs from the baseline scan to a follow-up scan.MethodsThe MACC algorithm first identifies an outer bound for the solution path, forms a high number of iso-contour curves based on equally spaced contour values, and then selects the best contour value to outline the lesion. The MACC software was tested on a set of 17 FLAIR MRI images evaluated by a pair of human experts and a longitudinal dataset of 12 pairs of T2-weighted Fluid Attenuated Inversion Recovery (FLAIR) images that had lesion analysis ROIs drawn by a single expert operator.ResultsIn the tests where two human experts evaluated the same MRI images, the MACC program demonstrated that it could markedly reduce inter-operator outline error. In the longitudinal part of the study, the MACC program created ROIs on follow-up scans that were in close agreement to the original expert’s ROIs. Finally, in a post-hoc analysis of 424 follow-up scans 91% of propagated MACC were accepted by an expert and only 9% of the final accepted ROIS had to be created or edited by the expert.ConclusionWhen used with an expert operator's verification of automatically created ROIs, MACC can be used to improve inter- operator agreement and decrease analysis time, which should improve data collected and analyzed in multicenter clinical trials.

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Section: Discussionmentioning

confidence: 99%

Improved operator agreement and efficiency using the minimum area contour change method for delineation of hyperintense multiple sclerosis lesions on FLAIR MRI

et al. 2013

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“…To alleviate this problem, several automatic methods have been proposed in the literature to segment MS lesions. Interestingly, the vast majority of automatic methods are based on a single time point (cross-sectional) and relatively few methods take into account multiple time points (longitudinal) (Llado et al, 2012; Garcia-Lorenzo et al, 2013). Executing a cross-sectional method for each time point would indeed produce the longitudinal measures of interest, but such measures are less reliable as each time point is processed independently.…”

Section: Introductionmentioning

confidence: 99%

Two Time Point MS Lesion Segmentation in Brain MRI: An Expectation-Maximization Framework

et al. 2016

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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.

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“…For example, a common task is to detect the presence of focal lesions and estimate their respective locations [1], [2]. Other types of combined detection-estimation tasks are found in cancer imaging, where lesion detection may be followed by estimation of tumor size or functional biomarkers such as standardized uptake value [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Optimal Joint Detection and Estimation That Maximizes ROC-Type Curves

Wunderlich

Goossens

Abbey

2016

IEEE Trans. Med. Imaging

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Combined detection-estimation tasks are frequently encountered in medical imaging. Optimal methods for joint detection and estimation are of interest because they provide upper bounds on observer performance, and can potentially be utilized for imaging system optimization, evaluation of observer efficiency, and development of image formation algorithms. We present a unified Bayesian framework for decision rules that maximize receiver operating characteristic (ROC)-type summary curves, including ROC, localization ROC (LROC), estimation ROC (EROC), free-response ROC (FROC), alternative free-response ROC (AFROC), and exponentially-transformed FROC (EFROC) curves, succinctly summarizing previous results. The approach relies on an interpretation of ROC-type summary curves as plots of an expected utility versus an expected disutility (or penalty) for signal-present decisions. We propose a general utility structure that is flexible enough to encompass many ROC variants and yet sufficiently constrained to allow derivation of a linear expected utility equation that is similar to that for simple binary detection. We illustrate our theory with an example comparing decision strategies for joint detection-estimation of a known signal with unknown amplitude. In addition, building on insights from our utility framework, we propose new ROC-type summary curves and associated optimal decision rules for joint detection-estimation tasks with an unknown, potentially-multiple, number of signals in each observation.

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Automated detection of multiple sclerosis lesions in serial brain MRI

Cited by 75 publications

References 66 publications

Improved operator agreement and efficiency using the minimum area contour change method for delineation of hyperintense multiple sclerosis lesions on FLAIR MRI

Improved operator agreement and efficiency using the minimum area contour change method for delineation of hyperintense multiple sclerosis lesions on FLAIR MRI

Two Time Point MS Lesion Segmentation in Brain MRI: An Expectation-Maximization Framework

Optimal Joint Detection and Estimation That Maximizes ROC-Type Curves

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