Random forest regression for magnetic resonance image synthesis

Jog, Amod; Carass, Aaron; Roy, Snehashis; Pham, Dzung L.; Prince, Jerry L.

doi:10.1016/j.media.2016.08.009

Cited by 170 publications

(135 citation statements)

References 52 publications

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“…The multi-output decision trees implementation has similarities to output kernel trees (Geurts et al, 2007). Predicting entire neighborhoods gives further context information such as the presence of lesions predominantly inside WM, which has been shown to improve patch based methods (Jog et al, 2017). This approach was originally presented in Jog et al (2015).…”

Section: Methods Overviewmentioning

confidence: 99%

Longitudinal multiple sclerosis lesion segmentation: Resource and challenge

et al. 2017

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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 website 1 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.

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Section: Methods Overviewmentioning

confidence: 99%

Longitudinal multiple sclerosis lesion segmentation: Resource and challenge

et al. 2017

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“…The unimodal (singleinput, single-output, one-to-one) method we compare against is pGAN [28], while the multimodal (many-to-one) models being REPLICA [22] (in a multi-input setting), and that of Chartsias et al [5], called MM-Synthesis hereafter. Both pGAN and MM-Synthesis were recently published (2019 and 2018), and they outperform all other methods before them (MM-Synthesis outperforms LSDN [27], Modality Propagation [14], and REPLICA [22], while pGAN outperforms both REPLICA and MM-Synthesis in one-to-one synthesis). To the best of our knowledge, we did not find any other methods that claimed to outperform either pGAN or MM-Synthesis, and so decided to evaluate our method against them.…”

Section: Benchmark Methodsmentioning

confidence: 99%

“…The method utilized global context of the input sequence by taking a full slice as input. Recently, Jog et al [22] propose a random forest based method that learns intensity mapping between input patches centered around a voxel extracted from a single pulse sequence, and the intensity of corresponding voxel in target sequence. The method utilized multi-resolution patches by building a Gaussian pyramid of the input sequence.…”

Section: A Unimodal Synthesismentioning

confidence: 99%

Missing MRI Pulse Sequence Synthesis Using Multi-Modal Generative Adversarial Network

Sharma

Hamarneh

2020

IEEE Trans. Med. Imaging

142

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Magnetic resonance imaging (MRI) is being increasingly utilized to assess, diagnose, and plan treatment for a variety of diseases. The ability to visualize tissue in varied contrasts in the form of MR pulse sequences in a single scan provides valuable insights to physicians, as well as enabling automated systems performing downstream analysis. However many issues like prohibitive scan time, image corruption, different acquisition protocols, or allergies to certain contrast materials may hinder the process of acquiring multiple sequences for a patient. This poses challenges to both physicians and automated systems since complementary information provided by the missing sequences is lost. In this paper, we propose a variant of generative adversarial network (GAN) capable of leveraging redundant information contained within multiple available sequences in order to generate one or more missing sequences for a patient scan. The proposed network is designed as a multi-input, multi-output network which combines information from all the available pulse sequences and synthesizes the missing ones in a single forward pass. We demonstrate and validate our method on two brain MRI datasets each with four sequences, and show the applicability of the proposed method in simultaneously synthesizing all missing sequences in any possible scenario where either one, two, or three of the four sequences may be missing. We compare our approach with competing unimodal and multi-modal methods, and show that we outperform both quantitatively and qualitatively.

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“…These intensity pairs and their voxel-wise features, along with their labels provide the training data for regressors that predict the intensity of the target modality given the features of the input modality. Our features consist of 3 × 3 × 3 image patches together with average image values in patches forming a constellation around the given voxel (“context features” similar to those in [2, 10]). We need 2 × K regressors, one each per modality and cluster.…”

Section: Methodsmentioning

confidence: 99%

A Supervoxel Based Random Forest Synthesis Framework for Bidirectional MR/CT Synthesis

Zhao

Carass

Lee³

et al. 2017

Simulation and Synthesis in Medical Imaging

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Synthesizing magnetic resonance (MR) and computed tomography (CT) images (from each other) has important implications for clinical neuroimaging. The MR to CT direction is critical for MRI-based radiotherapy planning and dose computation, whereas the CT to MR direction can provide an economic alternative to real MRI for image processing tasks. Additionally, synthesis in both directions can enhance MR/CT multi-modal image registration. Existing approaches have focused on synthesizing CT from MR. In this paper, we propose a multi-atlas based hybrid method to synthesize T1-weighted MR images from CT and CT images from T1-weighted MR images using a common framework. The task is carried out by: (a) computing a label field based on supervoxels for the subject image using joint label fusion; (b) correcting this result using a random forest classifier (RF-C); (c) spatial smoothing using a Markov random field; (d) synthesizing intensities using a set of RF regressors, one trained for each label. The algorithm is evaluated using a set of six registered CT and MR image pairs of the whole head.

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Random forest regression for magnetic resonance image synthesis

Cited by 170 publications

References 52 publications

Longitudinal multiple sclerosis lesion segmentation: Resource and challenge

Longitudinal multiple sclerosis lesion segmentation: Resource and challenge

Missing MRI Pulse Sequence Synthesis Using Multi-Modal Generative Adversarial Network

A Supervoxel Based Random Forest Synthesis Framework for Bidirectional MR/CT Synthesis

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