Abstract: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… Show more
“…The GIF method has previously been successfully used for the parcellation of anisotropic brain MR images. 7,19,20 Fig. 6Extracting a high-dimensional imaging “fingerprint”.…”
Changes on brain imaging may precede clinical manifestations or disclose disease progression opaque to conventional clinical measures. Where, as in multiple sclerosis, the pathological process has a complex anatomical distribution, such changes are not easily detected by low-dimensional models in common use. This hinders our ability to detect treatment effects, both in the management of individual patients and in interventional trials. Here we compared the ability of conventional models to detect an imaging response to treatment against high-dimensional models incorporating a wide multiplicity of imaging factors. We used fully-automated image analysis to extract 144 regional, longitudinal trajectories of pre- and post- treatment changes in brain volume and disconnection in a cohort of 124 natalizumab-treated patients. Low- and high-dimensional models of the relationship between treatment and the trajectories of change were built and evaluated with machine learning, quantifying performance with receiver operating characteristic curves. Simulations of randomised controlled trials enrolling varying numbers of patients were used to quantify the impact of dimensionality on statistical efficiency. Compared to existing methods, high-dimensional models were superior in treatment response detection (area under the receiver operating characteristic curve = 0.890 [95% CI = 0.885–0.895] vs. 0.686 [95% CI = 0.679–0.693],
P
< 0.01]) and in statistical efficiency (achieved statistical power = 0.806 [95% CI = 0.698–0.872] vs. 0.508 [95% CI = 0.403–0.593] with number of patients enrolled = 50, at
α
= 0.01). High-dimensional models based on routine, clinical imaging can substantially enhance the detection of the imaging response to treatment in multiple sclerosis, potentially enabling more accurate individual prediction and greater statistical efficiency of randomised controlled trials.
“…The GIF method has previously been successfully used for the parcellation of anisotropic brain MR images. 7,19,20 Fig. 6Extracting a high-dimensional imaging “fingerprint”.…”
Changes on brain imaging may precede clinical manifestations or disclose disease progression opaque to conventional clinical measures. Where, as in multiple sclerosis, the pathological process has a complex anatomical distribution, such changes are not easily detected by low-dimensional models in common use. This hinders our ability to detect treatment effects, both in the management of individual patients and in interventional trials. Here we compared the ability of conventional models to detect an imaging response to treatment against high-dimensional models incorporating a wide multiplicity of imaging factors. We used fully-automated image analysis to extract 144 regional, longitudinal trajectories of pre- and post- treatment changes in brain volume and disconnection in a cohort of 124 natalizumab-treated patients. Low- and high-dimensional models of the relationship between treatment and the trajectories of change were built and evaluated with machine learning, quantifying performance with receiver operating characteristic curves. Simulations of randomised controlled trials enrolling varying numbers of patients were used to quantify the impact of dimensionality on statistical efficiency. Compared to existing methods, high-dimensional models were superior in treatment response detection (area under the receiver operating characteristic curve = 0.890 [95% CI = 0.885–0.895] vs. 0.686 [95% CI = 0.679–0.693],
P
< 0.01]) and in statistical efficiency (achieved statistical power = 0.806 [95% CI = 0.698–0.872] vs. 0.508 [95% CI = 0.403–0.593] with number of patients enrolled = 50, at
α
= 0.01). High-dimensional models based on routine, clinical imaging can substantially enhance the detection of the imaging response to treatment in multiple sclerosis, potentially enabling more accurate individual prediction and greater statistical efficiency of randomised controlled trials.
“…Based on the FLAIR and T1 weighted images, two sets of lesion segmentation masks were generated. One set was acquired by computer assisted manual segmentation using BrainSeg3D (Lesjak et al, 2018). For this, a neuroradiologist (PE, 6 years of experience) identified and marked lesions on axial reformations of the FLAIR images.…”
Magnetic resonance imaging (MRI) scans play a pivotal role in the evaluation of patients presenting with a clinically isolated syndrome (CIS), as these may depict brain lesions suggestive of an inflammatory cause. We hypothesized that it is possible to predict the conversion from CIS to multiple sclerosis (MS) based on the baseline MRI scan by studying image features of these lesions.
We analyzed 84 patients diagnosed with CIS from a prospective observational single center cohort. The patients were followed up for at least three years. Conversion to MS was defined according to the 2010 McDonald criteria. Brain lesions were segmented based on 3D FLAIR and 3D T1 images. We generated brain lesion masks by a computer assisted manual segmentation. We also generated a set of automated segmentations using the Lesion Segmentation Toolbox for SPM to assess the influence of different segmentation methods. Shape and brightness features were automatically calculated from the segmented masks and used as input data to train an oblique random forest classifier. Prediction accuracies of the resulting model were validated through a three-fold cross-validation.
Conversion from CIS to MS occurred in 66 of 84 patients (79%). The conversion or non-conversion was predicted correctly in 71 patients based on shape features derived from the computer assisted manual segmentation masks (84.5% accuracy). This predictor was more accurate than predicting conversion using dissemination in space at baseline according to the 2010 McDonald criteria (75% accuracy). While shape features strongly contributed to the accuracy of the predictor, including intensity features did not further improve performance.
As patients who convert to definite MS benefit from early treatment, an early classification model is highly desirable. Our study shows that shape parameters of lesions can contribute to predicting the future course of CIS patients more accurately.
“…The most recent segmentation study presents a new protocol to develop benchmark segmentations of white matter tumors based on multi-rater consensus using a new MR database of 30 patients with multiple sclerosis. On these databases, three specialist reviewers individually segmented white matter lesions; using semi-automated tumor contouring tools developed in-house [24]. They have developed specialized BrainSeg3D software which allows their correct and effective delineation in 3D MR images.…”
“…In each case, seven liberated specialists segmented the outliers manually and consensual segmentation was created by merging the segmentations based on automatic LOP STAPLE method [28]. The major recent challenge consists on white-matter outliers segmentation [24] applied on MR databases of 30 patients with multiple sclerosis, which were learned on a 3T MR scanner with conventional sequences. A cohort of 30 MS patients was envisaged by a 3T Siemens Magnetom Trio MR system at the University of Medical Center Ljubljana (UMCL).…”
Nowadays, Brain tumors segmentation is a very important task in diagnosis of brain lesions.There are diversapproaches of segmentation, but segmentation of brain tumors is always difficult due to the complex features of magnetic resonance imaging MRI such as the appearance of vague and unclear tumors and boundaries. In this paper, an automatic MRI segmentation method is used to solve these problems. Here, tumor segmentation is treated as a problem of classification using the Ant Colony ACO optimization algorithm combined with a proposed protocol based on BrainSeg3D tools. Many studies and many existing approaches tend the multiple sclerosis (MS) which is a chronic inflammatory anomaly of the central nervous system. To pick up the outliers of multiple sclerosis, we should do a diagnosis by magnetic resonance imaging with various sequences. For this aim, some late technics of segmentation and classification of MRI images have been suggested for the automatic detection of MS outliers. In this work, we submit a novel protocol based on current and novel semi-automated tumors segmentation technics of BrainSeg3D. Evaluation of our results was performed on novel MR database containing 30 MS patients, which were learned with a 3T MR scanner with conventional sequences so that to estimate our assessment with the acquired consensus segmentation with ground truth data. The submitted algorithm is evaluated by using MATLAB GUI program and BrainSeg3D tools.
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