Quantified volume and count of white-matter lesions based on magnetic resonance (MR) images are important biomarkers in several neurodegenerative diseases. For a routine extraction of these biomarkers an accurate and reliable automated lesion segmentation is required. To objectively and reliably determine a standard automated method, however, creation of standard validation datasets is of extremely high importance. Ideally, these datasets should be publicly available in conjunction with standardized evaluation methodology to enable objective validation of novel and existing methods. For validation purposes, we present a novel MR dataset of 30 multiple sclerosis patients and a novel protocol for creating reference white-matter lesion segmentations based on multi-rater consensus. On these datasets three expert raters individually segmented white-matter lesions, using in-house developed semi-automated lesion contouring tools. Later, the raters revised the segmentations in several joint sessions to reach a consensus on segmentation of lesions. To evaluate the variability, and as quality assurance, the protocol was executed twice on the same MR images, with a six months break. The obtained intra-consensus variability was substantially lower compared to the intra- and inter-rater variabilities, showing improved reliability of lesion segmentation by the proposed protocol. Hence, the obtained reference segmentations may represent a more precise target to evaluate, compare against and also train, the automatic segmentations. To encourage further use and research we will publicly disseminate on our website http://lit.fe.uni-lj.si/tools the tools used to create lesion segmentations, the original and preprocessed MR image datasets and the consensus lesion segmentations.
Changes of white-matter lesions (WMLs) are good predictors of the progression of neurodegenerative diseases like multiple sclerosis (MS). Based on longitudinal magnetic resonance (MR) imaging the changes can be monitored, while the need for their accurate and reliable quantification led to the development of several automated MR image analysis methods. However, an objective comparison of the methods is difficult, because publicly unavailable validation datasets with ground truth and different sets of performance metrics were used. In this study, we acquired longitudinal MR datasets of 20 MS patients, in which brain regions were extracted, spatially aligned and intensity normalized. Two expert raters then delineated and jointly revised the WML changes on subtracted baseline and follow-up MR images to obtain ground truth WML segmentations. The main contribution of this paper is an objective, quantitative and systematic evaluation of two unsupervised and one supervised intensity based change detection method on the publicly available datasets with ground truth segmentations, using common pre- and post-processing steps and common evaluation metrics. Besides, different combinations of the two main steps of the studied change detection methods, i.e. dissimilarity map construction and its segmentation, were tested to identify the best performing combination.
Multiple sclerosis (MS) is a neurological disease characterized by focal lesions and morphological changes in the brain captured on magnetic resonance (MR) images. However, extraction of the corresponding imaging markers requires accurate segmentation of normal-appearing brain structures (NABS) and the lesions in MR images. On MR images of healthy brains, the NABS can be accurately captured by MR intensity mixture models, which, in combination with regularization techniques, such as in Markov random field (MRF) models, are known to give reliable NABS segmentation. However, on MR images that also contain abnormalities such as MS lesions, obtaining an accurate and reliable estimate of NABS intensity models is a challenge. We propose a method for automated segmentation of normal-appearing and abnormal structures in brain MR images that is based on a locally adaptive NABS model, a robust model parameters estimation method, and an MRF-based segmentation framework. Experiments on multisequence brain MR images of 30 MS patients show that, compared to whole-brain MR intensity model and compared to four popular unsupervised lesion segmentation methods, the proposed method increases the accuracy of MS lesion segmentation.
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