Why Does Synthesized Data Improve Multi-sequence Classification?

Tulder, Gijs van; Bruijne, Marleen de

doi:10.1007/978-3-319-24553-9_65

Cited by 61 publications

(43 citation statements)

References 16 publications

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“…Recently, non-local patch (Buades et al, 2005) based methods have been successful in many neuroimaging applications, such as tissue segmentations (Coupé et al, 2012; Hu et al, 2014; Roy et al, 2015b; Rousseau et al, 2011; Wang et al, 2014), classification (van Tulder and de Bruijne, 2015), lesion segmentation (Roy et al, 2014b, 2010b; Guizard et al, 2015), registration (Roy et al, 2014a; Iglesias et al, 2013), super resolution (Roy et al, 2010a; Manjon et al, 2010), intensity normalization (Jog et al, 2013, 2015; Roy et al, 2013b) and image synthesis (Roy et al, 2013a; Rousseau, 2008; Burgos et al, 2014; Roy et al, 2014c). A recent skull-stripping method, BEaST (Eskildsen et al, 2012) is based on non-local patch matching using multiple atlases.…”

Section: Introductionmentioning

confidence: 99%

Robust skull stripping using multiple MR image contrasts insensitive to pathology

2017

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Automatic skull-stripping or brain extraction of magnetic resonance (MR) images is often a fundamental step in many neuroimage processing pipelines. The accuracy of subsequent image processing relies on the accuracy of the skull-stripping. Although many automated stripping methods have been proposed in the past, it is still an active area of research particularly in the context of brain pathology. Most stripping methods are validated on T1-w MR images of normal brains, especially because high resolution T1-w sequences are widely acquired and ground truth manual brain mask segmentations are publicly available for normal brains. However, different MR acquisition protocols can provide complementary information about the brain tissues, which can be exploited for better distinction between brain, cerebrospinal fluid, and unwanted tissues such as skull, dura, marrow, or fat. This is especially true in the presence of pathology, where hemorrhages or other types of lesions can have similar intensities as skull in a T1-w image. In this paper, we propose a sparse patch based Multi-cONtrast brain STRipping method (MONSTR)1, where non-local patch information from one or more atlases, which contain multiple MR sequences and reference delineations of brain masks, are combined to generate a target brain mask. We compared MONSTR with four state-of-the-art, publicly available methods: BEaST, SPECTRE, ROBEX, and OptiBET. We evaluated the performance of these methods on 6 datasets consisting of both healthy subjects and patients with various pathologies. Three datasets (ADNI, MRBrainS, NAMIC) are publicly available, consisting of 44 healthy volunteers and 10 patients with schizophrenia. Other three in-house datasets, comprising 87 subjects in total, consisted of patients with mild to severe traumatic brain injury, brain tumors, and various movement disorders. A combination of T1-w, T2-w were used to skull-strip these datasets. We show significant improvement in stripping over the competing methods on both healthy and pathological brains. We also show that our multi-contrast framework is robust and maintains accurate performance across different types of acquisitions and scanners, even when using normal brains as atlases to strip pathological brains, demonstrating that our algorithm is applicable even when reference segmentations of pathological brains are not available to be used as atlases.

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Section: Introductionmentioning

confidence: 99%

Robust skull stripping using multiple MR image contrasts insensitive to pathology

2017

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“…The practice of image synthesis has been gaining attention recently (Rousseau, 2008; Roy et al, 2010a,b; Rousseau, 2010; Roy et al, 2011; Roohi et al, 2012; Jog et al, 2013a; Rousseau and Studholme, 2013; Konukoglu et al, 2013; Ye et al, 2013; Iglesias et al, 2013; Roy et al, 2013a,b; Jog et al, 2014a; Roy et al, 2014; Burgos et al, 2014; van Tulder and de Bruijne, 2015; Jog et al, 2015a; Cardoso et al, 2015; Van Nguyen et al, 2015; Bahrami, Khosro and Shi, Feng and Zong, Xiaopeng and Shin, Hae Won and An, Hongyu and Shen, Dinggang, 2015; Zikic et al, 2014) and the number of applications where image synthesis methods are being used is also growing. Synthesis of modalities differs from the data imputation literature (Hor and Moradi, 2015) in that the main goal is synthesis of the missing modality as opposed to classification in the absence of it.…”

Section: Introductionmentioning

confidence: 99%

Random forest regression for magnetic resonance image synthesis

Jog

Carass

Roy

et al. 2017

Medical Image Analysis

173

130

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By choosing different pulse sequences and their parameters, magnetic resonance imaging (MRI) can generate a large variety of tissue contrasts. This very flexibility, however, can yield inconsistencies with MRI acquisitions across datasets or scanning sessions that can in turn cause inconsistent automated image analysis. Although image synthesis of MR images has been shown to be helpful in addressing this problem, an inability to synthesize both T2-weighted brain images that include the skull and FLuid Attenuated Inversion Recovery (FLAIR) images has been reported. The method described herein, called REPLICA, addresses these limitations. REPLICA is a supervised random forest image synthesis approach that learns a nonlinear regression to predict intensities of alternate tissue contrasts given specific input tissue contrasts. Experimental results include direct image comparisons between synthetic and real images, results from image analysis tasks on both synthetic and real images, and comparison against other state-of-the-art image synthesis methods. REPLICA is computationally fast, and is shown to be comparable to other methods on tasks they are able to perform. Additionally REPLICA has the capability to synthesize both T2-weighted images of the full head and FLAIR images, and perform intensity standardization between different imaging datasets.

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“…Examples include building a model based on MR imaging equations (Rousseau, 2008), solving a sparse system based on local intensity information (Roy et al, 2011b, 2013), and estimating directly the non-linear intensity transformation between the two modalities (Jog et al, 2013). Image synthesis has been successfully used for a number of medical image analysis purposes, such as anatomical labeling (Rousseau et al, 2011a,b), tissue segmentation (Roy et al, 2010b; Coupé et al, 2011; Roy et al, 2014b), super-resolution (Rousseau, 2008, 2010; Roy et al, 2010a; Rousseau and Studholme, 2013; Jog et al, 2014b; Konukoglu et al, 2013), direct contrast synthesis (Jog et al, 2014a), inhomogeneity correction (Roy et al, 2011a), and improving classification accuracy (van Tulder and de Bruijne, 2015). …”

Section: Introductionmentioning

confidence: 99%

Cross contrast multi-channel image registration using image synthesis for MR brain images

Chen

Carass

Jog

et al. 2017

Medical Image Analysis

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Multi-modal deformable registration is important for many medical image analysis tasks such as atlas alignment, image fusion, and distortion correction. Whereas a conventional method would register images with different modalities using modality independent features or information theoretic metrics such as mutual information, this paper presents a new framework that addresses the problem using a two-channel registration algorithm capable of using mono-modal similarity measures such as sum of squared differences or cross-correlation. To make it possible to use these same-modality measures, image synthesis is used to create proxy images for the opposite modality as well as intensity-normalized images from each of the two available images. The new deformable registration framework was evaluated by performing intra-subject deformation recovery, intra-subject boundary alignment, and inter-subject label transfer experiments using multi-contrast magnetic resonance brain imaging data. Three different multi-channel registration algorithms were evaluated, revealing that the framework is robust to the multi-channel deformable registration algorithm that is used. With a single exception, all results demonstrated improvements when compared against single channel registrations using the same algorithm with mutual information.

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Why Does Synthesized Data Improve Multi-sequence Classification?

Cited by 61 publications

References 16 publications

Robust skull stripping using multiple MR image contrasts insensitive to pathology

Robust skull stripping using multiple MR image contrasts insensitive to pathology

Random forest regression for magnetic resonance image synthesis

Cross contrast multi-channel image registration using image synthesis for MR brain images

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