PSACNN: Pulse Sequence Adaptive Fast Whole Brain Segmentation

With the advent of convolutional neural networks~(CNN), supervised learning methods are increasingly being used for whole brain segmentation. However, a large, manually annotated training dataset of labeled brain images required to train such supervised methods is frequently difficult to obtain or create. In addition, existing training datasets are generally acquired with a homogeneous magnetic resonance imaging~(MRI) acquisition protocol. CNNs trained on such datasets are unable to generalize on test data with different acquisition protocols. Modern neuroimaging studies and clinical trials are necessarily multi-center initiatives with a wide variety of acquisition protocols. Despite stringent protocol harmonization practices, it is very difficult to standardize the gamut of MRI imaging parameters across scanners, field strengths, receive coils etc., that affect image contrast. In this paper we propose a CNN-based segmentation algorithm that, in addition to being highly accurate and fast, is also resilient to variation in the input acquisition. Our approach relies on building approximate forward models of pulse sequences that produce a typical test image. For a given pulse sequence, we use its forward model to generate plausible, synthetic training examples that appear as if they were acquired in a scanner with that pulse sequence. Sampling over a wide variety of pulse sequences results in a wide variety of augmented training examples that help build an image contrast invariant model. Our method trains a single CNN that can segment input MRI images with acquisition parameters as disparate as $T_1$-weighted and $T_2$-weighted contrasts with only $T_1$-weighted training data. The segmentations generated are highly accurate with state-of-the-art results~(overall Dice overlap$=0.94$), with a fast run time~($\approx$ 45 seconds), and consistent across a wide range of acquisition protocols.Comment: Typo in author name corrected. Greves -> Grev

MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans

Many methods have been proposed for tissue segmentation in brain MRI scans. The multitude of methods proposed complicates the choice of one method above others. We have therefore established the MRBrainS online evaluation framework for evaluating (semi) automatic algorithms that segment gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) on 3T brain MRI scans of elderly subjects (65-80 y). Participants apply their algorithms to the provided data, after which their results are evaluated and ranked. Full manual segmentations of GM, WM, and CSF are available for all scans and used as the reference standard. Five datasets are provided for training and fifteen for testing. The evaluated methods are ranked based on their overall performance to segment GM, WM, and CSF and evaluated using three evaluation metrics (Dice, H95, and AVD) and the results are published on the MRBrainS13 website. We present the results of eleven segmentation algorithms that participated in the MRBrainS13 challenge workshop at MICCAI, where the framework was launched, and three commonly used freeware packages: FreeSurfer, FSL, and SPM. The MRBrainS evaluation framework provides an objective and direct comparison of all evaluated algorithms and can aid in selecting the best performing method for the segmentation goal at hand.This study was financially supported by IMDI Grant 104002002 (Brainbox) from ZonMw, the Netherlands Organisation for Health Research and Development, within kind sponsoring by Philips, the University Medical Center Utrecht, and Eindhoven University of Technology. The authors would like to acknowledge the following members of the Utrecht Vascular Cognitive Impairment Study Group who were not included as coauthors of this paper but were involved in the recruitment of study participants and MRI acquisition at the UMC Utrecht (in alphabetical order by department): E. van den Berg, M. Brundel, S. Heringa, and L. J. Kappelle of the Department of Neurology, P. R. Luijten and W. P. Th. M. Mali of the Department of Radiology, and A. Algra and G. E. H. M. Rutten of the Julius Center for Health Sciences and Primary Care. The research of Geert Jan Biessels and the VCI group was financially supported by VIDI Grant 91711384 from ZonMw and by Grant 2010T073 of the Netherlands Heart Foundation. The research of Jeroen de Bresser is financially supported by a research talent fellowship of the University Medical Center Utrecht (Netherlands). The research of Annegreet van Opbroek and Marleen de Bruijne is financially supported by a research grant from NWO (the Netherlands Organisation for Scientific Research). The authors would like to acknowledge MeVis Medical Solutions AG (Bremen, Germany) for providing MeVisLab. Duygu Sarikaya and Liang Zhao acknowledge their Advisor Professor Jason Corso for his guidance. Duygu Sarikaya is supported by NIH 1 R21CA160825-01 and Liang Zhao is partially supported by the China Scholarship Council (CSC).info:eu-repo/semantics/publishedVersio

Image Synthesis in Magnetic Resonance Neuroimaging

Automatic processing of magnetic resonance images~(MRI) is a vital part of neuroscience research. Yet even the best and most widely used medical image processing methods will not produce consistent results when their input images are acquired with different pulse sequences. The lack of consistency is a result of multiple sources of variation in the acquired MRI data. MRI, unlike computed tomography~(CT), does not produce images where the magnitude of the intensity is standardized across scanners. In a typical scanning session, different MRI pulse sequences are acquired at different resolutions for various reasons. Certain pulse sequences are prone to artifacts that cause corrupted data. Medical image analysts have developed preprocessing algorithms such as intensity standardization and image synthesis methods to address this problem. However, their performance remains dependent on knowledge and consistency of the pulse sequences used to acquire the images. In this thesis three different approaches---REPLICA, $\Psi$-CLONE, and SynthCRAFT---to perform image synthesis in MRI are presented. REPLICA is a multi-resolution framework that performs random forest regression with carefully designed features that capture anatomical variability in MRI. $\Psi$-CLONE takes into account the physics of MRI acquisition process and estimates the pulse sequence parameters used to acquire the given subject image. These are then used to create new training images that are, by design, standardized to the subject image. This step allows for improved training of the random forest regression, which generates the final synthetic image. SynthCRAFT, in contrast to REPLICA and $\Psi$-CLONE, is a probabilistic framework for image synthesis. The conditional probability of the unknown, desired, synthetic image given the subject input images is modeled as a Gaussian conditional random field~(CRF). Inference on this CRF, which models inter-voxel dependencies, results in the output synthetic image. All approaches were validated using simulated and real brain MRI data by direct image comparison and were shown to outperform state-of-the-art image synthesis algorithms. The ability to synthesize $T_2$-weighted~($T_2$w) and FLuid Attenuated Inversion Recovery~(FLAIR) images has been showcased for all three algorithms. Subsequent lesion segmentations of synthetic FLAIR images were shown to be similar to those obtained from real FLAIR images, thus demonstrating the utility of synthesis. In addition, REPLICA was shown to be capable of synthesizing full-head images~(not skull-stripped), which is a challenging synthesis task. Intensity standardization using synthesis between two different $T_1$-weighted pulse sequences was demonstrated using REPLICA and SynthCRAFT. $\Psi$-CLONE was used to standardize the intensities of a large dataset, leading to more consistent segmentation results within that dataset. All three algorithms were used to perform super-resolution of low resolution $T_2$w and FLAIR images. The resulting super-resolution FLAIR images showed improved lesion segmentation. All three methods were demonstrated to be effective preprocessing algorithms that mitigated the variation in MRI data and improved the consistency of subsequent image processing

Image Synthesis in Magnetic Resonance Neuroimaging

Automatic processing of magnetic resonance images~(MRI) is a vital part of neuroscience research. Yet even the best and most widely used medical image processing methods will not produce consistent results when their input images are acquired with different pulse sequences. The lack of consistency is a result of multiple sources of variation in the acquired MRI data. MRI, unlike computed tomography~(CT), does not produce images where the magnitude of the intensity is standardized across scanners. In a typical scanning session, different MRI pulse sequences are acquired at different resolutions for various reasons. Certain pulse sequences are prone to artifacts that cause corrupted data. Medical image analysts have developed preprocessing algorithms such as intensity standardization and image synthesis methods to address this problem. However, their performance remains dependent on knowledge and consistency of the pulse sequences used to acquire the images. In this thesis three different approaches---REPLICA, $\Psi$-CLONE, and SynthCRAFT---to perform image synthesis in MRI are presented. REPLICA is a multi-resolution framework that performs random forest regression with carefully designed features that capture anatomical variability in MRI. $\Psi$-CLONE takes into account the physics of MRI acquisition process and estimates the pulse sequence parameters used to acquire the given subject image. These are then used to create new training images that are, by design, standardized to the subject image. This step allows for improved training of the random forest regression, which generates the final synthetic image. SynthCRAFT, in contrast to REPLICA and $\Psi$-CLONE, is a probabilistic framework for image synthesis. The conditional probability of the unknown, desired, synthetic image given the subject input images is modeled as a Gaussian conditional random field~(CRF). Inference on this CRF, which models inter-voxel dependencies, results in the output synthetic image. All approaches were validated using simulated and real brain MRI data by direct image comparison and were shown to outperform state-of-the-art image synthesis algorithms. The ability to synthesize $T_2$-weighted~($T_2$w) and FLuid Attenuated Inversion Recovery~(FLAIR) images has been showcased for all three algorithms. Subsequent lesion segmentations of synthetic FLAIR images were shown to be similar to those obtained from real FLAIR images, thus demonstrating the utility of synthesis. In addition, REPLICA was shown to be capable of synthesizing full-head images~(not skull-stripped), which is a challenging synthesis task. Intensity standardization using synthesis between two different $T_1$-weighted pulse sequences was demonstrated using REPLICA and SynthCRAFT. $\Psi$-CLONE was used to standardize the intensities of a large dataset, leading to more consistent segmentation results within that dataset. All three algorithms were used to perform super-resolution of low resolution $T_2$w and FLAIR images. The resulting super-resolution FLAIR images showed improved lesion segmentation. All three methods were demonstrated to be effective preprocessing algorithms that mitigated the variation in MRI data and improved the consistency of subsequent image processing

Longitudinal multiple sclerosis lesion segmentation data resource

The data presented in this article is related to the research article entitled “Longitudinal multiple sclerosis lesion segmentation: Resource and challenge” (Carass et al., 2017) [1]. In conjunction with the 2015 International Symposium on Biomedical Imaging, we organized a longitudinal multiple sclerosis (MS) lesion segmentation challenge providing training and test data to registered participants. The training data consists of five subjects with a mean of 4.4 (±0.55) time-points, and test data of fourteen subjects with a mean of 4.4 (±0.67) time-points. All 82 data sets had the white matter lesions associated with multiple sclerosis delineated by two human expert raters. The training data including multi-modal scans and manually delineated lesion masks is available for download. In addition, the testing data is also being made available in conjunction with a website for evaluating the automated analysis of the testing data

Longitudinal multiple sclerosis lesion segmentation: Resource and challenge

International audienceIn 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