Overview of MR Image Segmentation Strategies in Neuromuscular Disorders

Ogier, Augustin C.; Hostin, Marc-Adrien; Bellemare, Marc‐Emmanuel; Bendahan, David

doi:10.3389/fneur.2021.625308

Cited by 32 publications

(49 citation statements)

References 80 publications

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“…Manual segmentation is a bottleneck, and therefore a major limitation in the application of qMRI in clinical studies. This has driven researchers towards developing automated solutions using algorithmic machine learning solutions [7]. Defining each muscle separately and segmenting an image into n labels can be framed as a categorization problem where the goal is to find the right category for each voxel in the image.…”

Section: Introductionmentioning

confidence: 99%

3D Automated Segmentation of Lower Leg Muscles Using Machine Learning on a Heterogeneous Dataset

et al. 2021

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Quantitative MRI combines non-invasive imaging techniques to reveal alterations in muscle pathophysiology. Creating muscle-specific labels manually is time consuming and requires an experienced examiner. Semi-automatic and fully automatic methods reduce segmentation time significantly. Current machine learning solutions are commonly trained on data from healthy subjects using homogeneous databases with the same image contrast. While yielding high Dice scores (DS), those solutions are not applicable to different image contrasts and acquisitions. Therefore, the aim of our study was to evaluate the feasibility of automatic segmentation of a heterogeneous database. To create a heterogeneous dataset, we pooled lower leg muscle images from different studies with different contrasts and fields-of-view, containing healthy controls and diagnosed patients with various neuromuscular diseases. A second homogenous database with uniform contrasts was created as a subset of the first database. We trained three 3D-convolutional neuronal networks (CNN) on those databases to test performance as compared to manual segmentation. All networks, training on heterogeneous data, were able to predict seven muscles with a minimum average DS of 0.75. U-Net performed best when trained on the heterogeneous dataset (DS: 0.80 ± 0.10, AHD: 0.39 ± 0.35). ResNet and DenseNet yielded higher DS, when trained on a heterogeneous dataset (both DS: 0.86), as compared to a homogeneous dataset (ResNet DS: 0.83, DenseNet DS: 0.76). In conclusion, a CNN trained on a heterogeneous dataset achieves more accurate labels for predicting a heterogeneous database of lower leg muscles than a CNN trained on a homogenous dataset. We propose that a large heterogeneous database is needed, to make automated segmentation feasible for different kinds of image acquisitions.

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Section: Introductionmentioning

confidence: 99%

3D Automated Segmentation of Lower Leg Muscles Using Machine Learning on a Heterogeneous Dataset

et al. 2021

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“…Since diffusion metrics vary between different muscle groups in healthy controls and NMD, muscle segmentation has an important role in the analysis of mDTI data [ 3 ]. In NMD, the segmentation process is even more challenging because fatty infiltration, increase in connective tissue, and inflammation complicate differentiation of different muscle groups [ 12 ]. In this study, two evaluated segmentation techniques—MSB and VBT—showed excellent inter-rater reliability in healthy and dystrophic muscles and, therefore, a low rater dependency despite a high degree of fatty infiltration.…”

Section: Discussionmentioning

confidence: 99%

“…A high degree of fat infiltration can complicate muscle segmentation due to deviating anatomy [ 10 ]. Since automatic segmentation algorithms are still in evaluation, muscle segmentation is usually done manually [ 11 , 12 ]. As manual segmentation is time- and cost-consuming and NMD are rare diseases, the pooling of data plays an important role in the application in clinical studies [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

High Inter-Rater Reliability of Manual Segmentation and Volume-Based Tractography in Healthy and Dystrophic Human Calf Muscle

et al. 2021

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Background: Muscle diffusion tensor imaging (mDTI) is a promising surrogate biomarker in the evaluation of muscular injuries and neuromuscular diseases. Since mDTI metrics are known to vary between different muscles, separation of different muscles is essential to achieve muscle-specific diffusion parameters. The commonly used technique to assess DTI metrics is parameter maps based on manual segmentation (MSB). Other techniques comprise tract-based approaches, which can be performed in a previously defined volume. This so-called volume-based tractography (VBT) may offer a more robust assessment of diffusion metrics and additional information about muscle architecture through tract properties. The purpose of this study was to assess DTI metrics of human calf muscles calculated with two segmentation techniques—MSB and VBT—regarding their inter-rater reliability in healthy and dystrophic calf muscles. Methods: 20 healthy controls and 18 individuals with different neuromuscular diseases underwent an MRI examination in a 3T scanner using a 16-channel Torso XL coil. DTI metrics were assessed in seven calf muscles using MSB and VBT. Coefficients of variation (CV) were calculated for both techniques. MSB and VBT were performed by two independent raters to assess inter-rater reliability by ICC analysis and Bland-Altman plots. Next to analysis of DTI metrics, the same assessments were also performed for tract properties extracted with VBT. Results: For both techniques, low CV were found for healthy controls (≤13%) and neuromuscular diseases (≤17%). Significant differences between methods were found for all diffusion metrics except for λ1. High inter-rater reliability was found for both MSB and VBT (ICC ≥ 0.972). Assessment of tract properties revealed high inter-rater reliability (ICC ≥ 0.974). Conclusions: Both segmentation techniques can be used in the evaluation of DTI metrics in healthy controls and different NMD with low rater dependency and high precision but differ significantly from each other. Our findings underline that the same segmentation protocol must be used to ensure comparability of mDTI data.

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“…The most prominent technique is volume‐based analysis, which can be performed using full manual segmentation or semiautomatic segmentation to separate individual muscles 10 . Semiautomatic segmentation techniques are increasingly popular in mDTI analysis because they provide similar accuracy as full manual segmentations and reduce the segmentation time 11,12 . By contrast, classic ROI‐based tractography has to be performed manually and requires a large amount of time and an experienced examiner 9 .…”

Section: Introductionmentioning

confidence: 99%

Robustness and stability of volume‐based tractography in a multicenter setting

et al. 2022

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Muscle diffusion tensor imaging (mDTI)-based tractography is a promising tool with which to detect subclinical changes in muscle injuries and to evaluate pathophysiology in neuromuscular diseases. Classic region of interest (ROI)-based tractography is very time-consuming and requires an examiner with extensive experience. (Semi)automatic approaches such as volume-based tractography (VBT) can diminish this problem but its robustness and stability are unknown. The aim of

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Overview of MR Image Segmentation Strategies in Neuromuscular Disorders

Cited by 32 publications

References 80 publications

3D Automated Segmentation of Lower Leg Muscles Using Machine Learning on a Heterogeneous Dataset

3D Automated Segmentation of Lower Leg Muscles Using Machine Learning on a Heterogeneous Dataset

High Inter-Rater Reliability of Manual Segmentation and Volume-Based Tractography in Healthy and Dystrophic Human Calf Muscle

Robustness and stability of volume‐based tractography in a multicenter setting

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