Shallow vs deep learning architectures for white matter lesion segmentation in the early stages of multiple sclerosis

In this work, we present a comparison of a shallow and a deep learning architecture for the automated segmentation of white matter lesions in MR images of multiple sclerosis patients. In particular, we train and test both methods on early stage disease patients, to verify their performance in challenging conditions, more similar to a clinical setting than what is typically provided in multiple sclerosis segmentation challenges. Furthermore, we evaluate a prototype naive combination of the two methods, which refines the final segmentation. All methods were trained on 32 patients, and the evaluation was performed on a pure test set of 73 cases. Results show low lesion-wise false positives (30%) for the deep learning architecture, whereas the shallow architecture yields the best Dice coefficient (63%) and volume difference (19%). Combining both shallow and deep architectures further improves the lesion-wise metrics (69% and 26% lesion-wise true and false positive rate, respectively).Comment: Accepted to the MICCAI 2018 Brain Lesion (BrainLes) worksho

Automated Detection of Cortical Lesions in Multiple Sclerosis Patients with 7T MRI

The automated detection of cortical lesions (CLs) in patients with multiple sclerosis (MS) is a challenging task that, despite its clinical relevance, has received very little attention. Accurate detection of the small and scarce lesions requires specialized sequences and high or ultra- high field MRI. For supervised training based on multimodal structural MRI at 7T, two experts generated ground truth segmentation masks of 60 patients with 2014 CLs. We implemented a simplified 3D U-Net with three resolution levels (3D U-Net-). By increasing the complexity of the task (adding brain tissue segmentation), while randomly dropping input channels during training, we improved the performance compared to the baseline. Considering a minimum lesion size of 0.75 μL, we achieved a lesion-wise cortical lesion detection rate of 67% and a false positive rate of 42%. However, 393 (24%) of the lesions reported as false positives were post-hoc confirmed as potential or definite lesions by an expert. This indicates the potential of the proposed method to support experts in the tedious process of CL manual segmentation

PARTIAL VOLUME ESTIMATION IN MULTIPLE SCLEROSIS LESION SEGMENTATION

Partial volume (PV) is the effect of having a mixture of tissues present within a voxel. This effect occurs in tissue borders and affects small structures such as small multiple sclerosis (MS) lesions. Ignoring PV effects in volumetry may lead to significant estimation errors. Here, we propose a novel automated MS lesion segmentation technique that takes PV effects into account. The proposed method shows higher accuracy in terms of lesion volume estimation compared to a manually segmented ground truth as well as significant improvement in detection of small lesions, also in comparison to two software packages for MS lesion segmentation

The Evolution of Cortical and Sub-cortical Lesion Size and Number Correlates with Changes in Cognition in Early-Stage Relapsing-Remitting Multiple Sclerosis Patients

Lesion load and activity in multiple sclerosis (MS) patients, as identified by conventional magnetic resonance imaging (MRI), correlate only moderately with patients clinical status and evolution. Cortical lesion number and volume measured with advanced MRI may provide better correlates to cognitive dysfunction and disability. In this work, we studied the clinical impact of advanced MRI metrics of cortical and subcortical lesion evolution in a cohort of early relapsing-remitting MS patients. The number and volume of lesions that “shrunk”, disappeared or remained stable over time were strong determinants of changes in cognition in our patients cohort

The Evolution of Cortical and Sub-cortical Lesion Size and Number Correlates with Changes in Cognition in Early-Stage Relapsing-Remitting Multiple Sclerosis Patients

Lesion load and activity in multiple sclerosis (MS) patients, as identified by conventional magnetic resonance imaging (MRI), correlate only moderately with patients clinical status and evolution. Cortical lesion number and volume measured with advanced MRI may provide better correlates to cognitive dysfunction and disability. In this work, we studied the clinical impact of advanced MRI metrics of cortical and subcortical lesion evolution in a cohort of early relapsing-remitting MS patients. The number and volume of lesions that “shrunk”, disappeared or remained stable over time were strong determinants of changes in cognition in our patients cohort

Automated Detection of White-matter and Cortical Lesions in MP2RAGE at Ultra-High Field using a Single Scan

Ultra-high-field Magnetic Resonance Imaging (7T MRI) has been shown to be a valuable tool to assess focal and diffuse pathology in multiple sclerosis (MS) patients, both in grey- and in white-matter. In this work, we developed and evaluated a method to automatically assess MS lesion load using magnetization-prepared two inversion-contrast rapid gradient-echo (MP2RAGE) MRI at 7T. The validation was conducted in a cohort of twenty MS patients from two research centers through a ground truth based on manual segmentations performed by a radiologist. Our single-sequence segmentation accurately detects visible white-matter and cortical lesions

Segmentation of Cortical and Subcortical Multiple Sclerosis Lesions Based on Constrained Partial Volume Modeling

We propose a novel method to automatically detect and segment multiple sclerosis lesions, located both in white matter and in the cortex. The algorithm consists of two main steps: (i) a supervised approach that outputs an initial bitmap locating candidates of lesional tissue and (ii) a Bayesian partial volume estimation framework that estimates the lesion concentration in each voxel. By using a “mixel” approach, potential partial volume effects especially affecting small lesions can be modeled, thus yielding improved lesion segmentation. The proposed method is tested on multiple MR image sequences including 3D MP2RAGE, 3D FLAIR, and 3D DIR. Quantitative evaluation is done by comparison with manual segmentations on a cohort of 39 multiple sclerosis early-stage patients