Saurabh Jain

Longitudinal multiple sclerosis lesion segmentation: Resource and challenge

Abstract In conjunction with the ISBI 2015 conference, we organized a longitudinal lesion segmentation challenge providing training and test data to registered participants. The training data consisted of five subjects with a mean of 4.4 time‐points, and test data of fourteen subjects with a mean of 4.4 time‐points. All 82 data sets had the white matter lesions associated with multiple sclerosis delineated by two human expert raters. Eleven teams submitted results using state‐of‐the‐art lesion segmentation algorithms to the challenge, with ten teams presenting their results at the conference. We present a quantitative evaluation comparing the consistency of the two raters as well as exploring the performance of the eleven submitted results in addition to three other lesion segmentation algorithms. The challenge presented three unique opportunities: (1) the sharing of a rich data set; (2) collaboration and comparison of the various avenues of research being pursued in the community; and (3) a review and refinement of the evaluation metrics currently in use. We report on the performance of the challenge participants, as well as the construction and evaluation of a consensus delineation. The image data and manual delineations will continue to be available for download, through an evaluation website2 as a resource for future researchers in the area. This data resource provides a platform to compare existing methods in a fair and consistent manner to each other and multiple manual raters. HighlightsPublic lesion data base of 21 training data sets and 61 testing data sets.Fully automated evaluation website.Comparison between 14 state‐of‐the‐art algorithms and 2 manual delineators.

Reliable measurements of brain atrophy in individual patients with multiple sclerosis

As neurodegeneration is recognized as a major contributor to disability in multiple sclerosis (MS), brain atrophy quantification could have a high added value in clinical practice to assess treatment efficacy and disease progression, provided that it has a sufficiently low measurement error to draw meaningful conclusions for an individual patient.

Patch-Based Super-Resolution of MR Spectroscopic Images: Application to Multiple Sclerosis

Purpose: Magnetic resonance spectroscopic imaging (MRSI) provides complementary information to conventional magnetic resonance imaging. Acquiring high resolution MRSI is time consuming and requires complex reconstruction techniques. Methods: In this paper, a patch-based super-resolution method is presented to increase the spatial resolution of metabolite maps computed from MRSI. The proposed method uses high resolution anatomical MR images (T1-weighted and Fluid-attenuated inversion recovery) to regularize the super-resolution process. The accuracy of the method is validated against conventional interpolation techniques using a phantom, as well as simulated and in vivo acquired human brain images of multiple sclerosis subjects. Results: The method preserves tissue contrast and structural information, and matches well with the trend of acquired high resolution MRSI. Conclusions: These results suggest that the method has potential for clinically relevant neuroimaging applications.

Reliability of measuring regional callosal atrophy in neurodegenerative diseases

The Corpus Callosum (CC) is an important structure connecting the two brain hemispheres. As several neurodegenerative diseases are known to alter its shape, it is an interesting structure to assess as biomarker. Yet, currently, the CC-segmentation is often performed manually and is consequently an error prone and time-demanding procedure. In this paper, we present an accurate and automated method for corpus callosum segmentation based on T1-weighted MRI images. After the initial construction of a CC atlas based on healthy controls, a new image is subjected to a mid-sagittal plane (MSP) detection algorithm and a 3D affine registration in order to initialise the CC within the extracted MSP. Next, an active shape model is run to extract the CC. We calculated the reliability of most popular CC features (area, circularity, corpus callosum index and thickness profile) in healthy controls, Alzheimers Disease patients and Multiple Sclerosis patients. Importantly, we also provide inter-scanner reliability estimates. We obtained an intra-class correlation coefficient (ICC) of over 0.95 for most features and most datasets. The inter-scanner reliability assessed on the MS patients was remarkably well and ranged from 0.77 to 0.97. In summary, we have constructed an algorithm that reliably detects the CC in 3D T1 images in a fully automated way in healthy controls and different neurodegenerative diseases. Although the CC area and the circularity are the most reliable features (ICC > 0.97); the reliability of the thickness profile (ICC > 0.90; excluding the tip) is sufficient to warrant its inclusion in future clinical studies.

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.

Use Case I: Imaging Biomarkers in Neurological Disease. Focus on Multiple Sclerosis

Imaging is widely used for diagnosis and monitoring of neurological diseases. CT scans are routinely acquired in emergency units in patients with traumatic injuries or stroke. PET imaging has gained a strong foothold in oncology. MRI has become the standard of practice for the diagnosis, follow-up and management of numerous neurological and psychiatric conditions. All of these imaging techniques have in common that, in clinical practice, the images need to be interpreted visually by trained specialists, who are responsible for initial diagnosis or for interpretation of follow-up examinations.

Patch based super-resolution of MR spectroscopic images

In this paper, a new single-image super-resolution method is presented to increase the spatial resolution of metabolite maps computed from magnetic resonance spectroscopic imaging. The proposed method is based on a non-local patch-based strategy that uses a high resolution T1-weighted image to regularise the super-resolution process. The method is implemented in a multi-scale fashion. The accuracy of the method is validated on both phantom and in vivo images. Both qualitative and quantitative validation suggest that the method has potential for clinically relevant neuroimaging applications.

Unsupervised Framework for Consistent Longitudinal MS Lesion Segmentation

Quantification of white matter lesion changes on brain magnetic resonance (MR) images is of major importance for the follow-up of patients with Multiple Sclerosis (MS). Many automated segmentation methods have been proposed. However, most of them focus on a single time point MR scan session and hence lack consistency when evaluating lesion changes over time. In this paper, we present MSmetrix-long, an unsupervised method that incorporates temporal consistency by jointly segmenting MS lesions of two subsequent scan sessions. The method is formulated as a Maximum A Posteriori model on the FLAIR image intensities of both time points and the difference image intensities, and optimised using an expectation maximisation algorithm. Validation is performed on two different data sets in terms of consistency and sensitivity to MS lesion changes. It is shown that MSmetrix-long outperforms MSmetrix-cross for the quantification of MS lesion evolution over time.