Segmentation of the brain using direction-averaged signal of DWI images

Cheng, Hu; Newman, Sharlene D.; Afzali, Maryam; Fadnavis, Shreyas; Garyfallidis, Eleftherios

doi:10.1016/j.mri.2020.02.010

Cited by 19 publications

(19 citation statements)

References 18 publications

(25 reference statements)

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“…Our proposed method performed tissue segmentation prediction directly from the dMRI data and thus could avoid obvious segmentation errors when transferring the anatomical T2w-based "ground truth" segmentation to the dMRI space. In the literature, anatomical-MRI-based segmentation, e.g., the one obtained by SPM, is usually used as the "ground truth" data (Ciritsis et al, 2018;Schnell et al, 2009;Cheng et al, 2020), since the segmentation appears in good agreement with the known anatomy. However, transferring T1w-or T2wbased segmentation into the dMRI space is challenging due to the image distortions in dMRI data, which affected inter-modality registration significantly (Albi et al, 2018;Wu et al, 2008;Jones and Cercignani, 2010).…”

Section: Discussionmentioning

confidence: 99%

“…We showed that our proposed method was able to correctly segment in areas where the T2w coregistered segmentation were apparently wrong, thus generating a visually more correct segmentation corresponding to the anatomy as appearing on the T2w image. Similar to previous studies (Beejesh et al, 2019;Hui et al, 2015;Steven et al, 2014;Cheng et al, 2020;Yap et al, 2015), we chose to calculate the accuracy against the "ground truth" segmentation from co-registered anatomical MRI data. Therefore, regions where our method correctly labeled the tissue type but the "ground truth" did not would cause a lower accuracy score.…”

Section: Discussionmentioning

confidence: 99%

“…The labels were projected into the co-registered dMRI data using nearest neighbor interpolation. We note that SPM is often the method of choice in generating high quality reference brain segmentation to compare with dMRI-based tissue segmentation (Schnell et al, 2009;Ciritsis et al, 2018;Cheng et al, 2020), although other segmentation tools, e.g., FMRIB's Automated Segmentation Tool (FSL FAST) (Jenkinson et al, 2012) could also be used instead.…”

Section: Datasetsmentioning

confidence: 99%

“…We evaluated the proposed method with comparison to three other state-ofthe-art dMRI-based tissue segmentation methods: 1) segmentation based on the estimation of a dMRI-based tissue response function (Dhollander et al, 2016(Dhollander et al, , 2018 in the constrained spherical deconvolution (CSD) framework (Tournier et al, 2007), 2) application of the SPM tissue segmentation algorithm (Ashburner and Friston, 2005) directly to the diffusion baseline (b0) image, 3) a method that uses the direction-averaged diffusion weighted imaging (daDWI) signal as input to SPM tissue segmentation (Cheng et al, 2020). In the rest of the paper, we refer to these methods as the CSD method, the SPM+b0 method, and the daDWI method, respectively.…”

Section: Comparison With Other State-of-the-art Dmri Tissue Segmentatmentioning

confidence: 99%

“…The daDWI method fits a power-law based model to the directionaveraged signal of the DWI images to obtain two parametric maps named alpha and beta (McKinnon et al, 2017). The alpha and beta images are used for creating a pseudo T1w image (Cheng et al, 2020), which is subsequently inputted to SPM to obtain the GM, WM and CSF segmentation. For each of the compared methods, the output of the prediction includes the overall tissue segmentation map where each voxel has a predicted label (GM, WM, CSF or background) and three probabilistic segmentation maps (for GM, WM and CSF, respectively) where each voxel has a probability belonging to a certain tissue type.…”

Section: Comparison With Other State-of-the-art Dmri Tissue Segmentatmentioning

confidence: 99%

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Deep Learning Based Segmentation of Brain Tissue from Diffusion MRI

Zhang

Breger

Cho

et al. 2020

Preprint

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Segmentation of brain tissue types from diffusion MRI (dMRI) is an important task, required for quantification of brain microstructure and for improving tractography. Current dMRI segmentation is mostly based on anatomical MRI (e.g., T1- and T2-weighted) segmentation that is registered to the dMRI space. However, such inter-modality registration is challenging due to more image distortions and lower image resolution in the dMRI data as compared with the anatomical MRI data. In this study, we present a deep learning method that learns tissue segmentation from high-quality imaging datasets from the Human Connectome Project (HCP), where registration of anatomical data to dMRI is more precise. The method is then able to predict a tissue segmentation directly from new dMRI data, including data collected with a different acquisition protocol, without requiring anatomical data and inter-modality registration. We train a convolutional neural network (CNN) to learn a tissue segmentation model using a novel augmented target loss function designed to improve accuracy in regions of tissue boundary. To further improve accuracy, our method adds diffusion kurtosis imaging (DKI) parameters that characterize non-Gaussian water molecule diffusion to the conventional diffusion tensor imaging parameters. The DKI parameters are calculated from the recently proposed mean-kurtosis-curve method that corrects implausible DKI parameter values and provides additional features that discriminate between tissue types. We demonstrate high tissue segmentation accuracy on HCP data, and also when applying the HCP-trained model on dMRI data from a clinical acquisition with lower resolution and fewer gradient directions.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Datasetsmentioning

confidence: 99%

Section: Comparison With Other State-of-the-art Dmri Tissue Segmentatmentioning

confidence: 99%

Section: Comparison With Other State-of-the-art Dmri Tissue Segmentatmentioning

confidence: 99%

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Deep Learning Based Segmentation of Brain Tissue from Diffusion MRI

Zhang

Breger

Cho

et al. 2020

Preprint

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Tissue Probability Based Registration of Diffusion‐Weighted Magnetic Resonance Imaging

Malovani

Friedman

Ben‐Eliezer

et al. 2021

Magnetic Resonance Imaging

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Background Current registration methods for diffusion‐MRI (dMRI) data mostly focus on white matter (WM) areas. Recently, dMRI has been employed for the characterization of gray matter (GM) microstructure, emphasizing the need for registration methods that consider all tissue types. Purpose To develop a dMRI registration method based on GM, WM, and cerebrospinal fluid (CSF) tissue probability maps (TPMs). Study Type Retrospective longitudinal study. Population Thirty‐two healthy participants were scanned twice (legacy data), divided into a training‐set (n = 16) and a test‐set (n = 16), and 35 randomly‐selected participants from the Human Connectome Project. Field Strength/Sequence 3.0T, diffusion‐weighted spin‐echo echo‐planar sequence; T1‐weighted spoiled gradient‐recalled echo (SPGR) sequence. Assessment A joint segmentation‐registration approach was implemented: Diffusion tensor imaging (DTI) maps were classified into TPMs using machine‐learning approaches. The resulting GM, WM, and CSF probability maps were employed as features for image alignment. Validation was performed on the test dataset and the HCP dataset. Registration performance was compared with current mainstream registration tools. Statistical Tests Classifiers used for segmentation were evaluated using leave‐one‐out cross‐validation and scored using Dice‐index. Registration success was evaluated by voxel‐wise variance, normalized cross‐correlation of registered DTI maps, intra‐ and inter‐subject similarity of the registered TPMs, and region‐based intra‐subject similarity using an anatomical atlas. One‐way ANOVAs were performed to compare between our method and other registration tools. Results The proposed method outperformed mainstream registration tools as indicated by lower voxel‐wise variance of registered DTI maps (SD decrease of 10%) and higher similarity between registered TPMs within and across participants, for all tissue types (Dice increase of 0.1–0.2; P < 0.05). Data Conclusion A joint segmentation‐registration approach based on diffusion‐driven TPMs provides a more accurate registration of dMRI data, outperforming other registration tools. Our method offers a “translation” of diffusion data into structural information in the form of TPMs, allowing to directly align diffusion and structural images. Level of Evidence: 1 Technical Efficacy Stage: 1

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