Automatic Segmentation of Muscle Tissue and Inter-muscular Fat in Thigh and Calf MRI Images

Amer, Rula; Nassar, Jannette; Bendahan, David; Greenspan, Hayit; Ben‐Eliezer, Noam

doi:10.1007/978-3-030-32245-8_25

Cited by 21 publications

(24 citation statements)

References 10 publications

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“…Both binary masks and the FF data demonstrated good agreements between the automatic method and the manual segmentation. After all, results from the whole muscles in this study are either comparable to or outperform others in the literature 16,17,21 …”

Section: Discussionsupporting

confidence: 82%

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Automation of Quantifying Axonal Loss in Patients with Peripheral Neuropathies through Deep Learning Derived Muscle Fat Fraction

Chen

Moiseev

Kong

et al. 2021

Magnetic Resonance Imaging

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Background Axonal loss denervates muscle, leading to an increase of fat accumulation in the muscle. Therefore, fat fraction (FF) in whole limb muscle using MRI has emerged as a monitoring biomarker for axonal loss in patients with peripheral neuropathies. In this study, we are testing whether deep learning‐based model can automate quantification of the FF in individual muscles. While individual muscle is smaller with irregular shape, manually segmented muscle MRI images have been accumulated in this lab; and make the deep learning feasible. Purpose To automate segmentation on muscle MRI images through deep learning for quantifying individual muscle FF in patients with peripheral neuropathies. Study Type Retrospective. Subjects 24 patients and 19 healthy controls. Field Strength/Sequences 3T; Interleaved 3D GRE. Assessment A 3D U‐Net model was implemented in segmenting muscle MRI images. This was enabled by leveraging a large set of manually segmented muscle MRI images. B1+ and B1− maps were used to correct image inhomogeneity. Accuracy of the automation was evaluated using Pixel Accuracy (PA), Dice Coefficient (DC) in binary masks; and Bland‐Altman and Pearson correlation by comparing FF values between manual and automated methods. Statistical Tests PA and DC were reported with their median value and standard deviation. Two methods were compared using the ± 95% confidence intervals (CI) of Bland‐Altman analysis and the Pearson’s coefficient (r2). Results DC values were from 0.83 ± 0.17 to 0.98 ± 0.02 in thigh and from 0.63 ± 0.18 to 0.96 ± 0.02 in calf muscles. For FF values, the overall ± 95% CI and r2 were [0.49, –0.56] and 0.989 in thigh and [0.84, –0.71] and 0.971 in the calf. Data Conclusion Automated results well agreed with the manual results in quantifying FF for individual muscles. This method mitigates the formidable time consumption and intense labor in manual segmentations; and enables the use of individual muscle FF as outcome measures in upcoming longitudinal studies. Level of Evidence 3 Technical Efficacy Stage 1

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Section: Discussionsupporting

confidence: 82%

“…Several studies have proposed either semi‐automated or fully automated methods in segmenting muscles on MRI images 16–22 . These automations have been mainly applied to whole limb muscles.…”

Section: Introductionmentioning

confidence: 99%

Automation of Quantifying Axonal Loss in Patients with Peripheral Neuropathies through Deep Learning Derived Muscle Fat Fraction

Chen

Moiseev

Kong

et al. 2021

Magnetic Resonance Imaging

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“…The very first deep learning approach applied on lower limb MR images was used in order to detect the fascia lata . Two studies intended to address this issue using a 5-layer network combined with a dual active contour model ( 65 ) or a U-Net architecture ( 66 ). Yao et al used T 1 -weighted images while Amer et al showed the interest of combining T 2 -weighted and PD images for their study.…”

Section: Deep Learning-based Segmentation Methodsmentioning

confidence: 99%

“…Both of them provided high-quality results with DSC values larger than 0.97 ± 0.02. Distinction between adipose and healthy muscle tissue was performed using the same networks and the corresponding DSC values were also high, i.e., 0.91 ( 66 ) and 0.94 ± 0.07 ( 65 ) for muscle detection. Recently, impressive DSC scores of 0.97 were obtained with an improved U-Net structure using residual connections and dense blocks ( 67 ).…”

Section: Deep Learning-based Segmentation Methodsmentioning

confidence: 99%

“…An interesting solution for this issue relies on either the use of unlabeled data or the creation of artificial training examples. Amer et al ( 66 ) proposed a semi-supervised learning method, which uses both labeled and unlabeled datasets. In that case, each image does not have to be annotated before the network training phase and one can increase the database without a human intervention for the labeling process.…”

Section: Deep Learning-based Segmentation Methodsmentioning

confidence: 99%

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Overview of MR Image Segmentation Strategies in Neuromuscular Disorders

et al. 2021

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Neuromuscular disorders are rare diseases for which few therapeutic strategies currently exist. Assessment of therapeutic strategies efficiency is limited by the lack of biomarkers sensitive to the slow progression of neuromuscular diseases (NMD). Magnetic resonance imaging (MRI) has emerged as a tool of choice for the development of qualitative scores for the study of NMD. The recent emergence of quantitative MRI has enabled to provide quantitative biomarkers more sensitive to the evaluation of pathological changes in muscle tissue. However, in order to extract these biomarkers from specific regions of interest, muscle segmentation is mandatory. The time-consuming aspect of manual segmentation has limited the evaluation of these biomarkers on large cohorts. In recent years, several methods have been proposed to make the segmentation step automatic or semi-automatic. The purpose of this study was to review these methods and discuss their reliability, reproducibility, and limitations in the context of NMD. A particular attention has been paid to recent deep learning methods, as they have emerged as an effective method of image segmentation in many other clinical contexts.

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Assisted annotation in Deep LOGISMOS: Simultaneous multi‐compartment 3D MRI segmentation of calf muscles

et al. 2023

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BackgroundAutomated segmentation of individual calf muscle compartments in 3D MR images is gaining importance in diagnosing muscle disease, monitoring its progression, and prediction of the disease course. Although deep convolutional neural networks have ushered in a revolution in medical image segmentation, achieving clinically acceptable results is a challenging task and the availability of sufficiently large annotated datasets still limits their applicability.PurposeIn this paper, we present a novel approach combing deep learning and graph optimization in the paradigm of assisted annotation for solving general segmentation problems in 3D, 4D, and generally n‐D with limited annotation cost.MethodsDeep LOGISMOS combines deep‐learning‐based pre‐segmentation of objects of interest provided by our convolutional neural network, FilterNet+, and our 3D multi‐objects LOGISMOS framework (layered optimal graph image segmentation of multiple objects and surfaces) that uses newly designed trainable machine‐learned cost functions. In the paradigm of assisted annotation, multi‐object JEI for efficient editing of automated Deep LOGISMOS segmentation was employed to form a new larger training set with significant decrease of manual tracing effort.ResultsWe have evaluated our method on 350 lower leg (left/right) T1‐weighted MR images from 93 subjects (47 healthy, 46 patients with muscular morbidity) by fourfold cross‐validation. Compared with the fully manual annotation approach, the annotation cost with assisted annotation is reduced by 95%, from 8 h to 25 min in this study. The experimental results showed average Dice similarity coefficient (DSC) of and average absolute surface positioning error of 0.63 pixels (0.44 mm) for the five 3D muscle compartments for each leg. These results significantly improve our previously reported method and outperform the state‐of‐the‐art nnUNet method.ConclusionsOur proposed approach can not only dramatically reduce the expert's annotation efforts but also significantly improve the segmentation performance compared to the state‐of‐the‐art nnUNet method. The notable performance improvements suggest the clinical‐use potential of our new fully automated simultaneous segmentation of calf muscle compartments.

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Automatic Segmentation of Muscle Tissue and Inter-muscular Fat in Thigh and Calf MRI Images

Cited by 21 publications

References 10 publications

Automation of Quantifying Axonal Loss in Patients with Peripheral Neuropathies through Deep Learning Derived Muscle Fat Fraction

Automation of Quantifying Axonal Loss in Patients with Peripheral Neuropathies through Deep Learning Derived Muscle Fat Fraction

Overview of MR Image Segmentation Strategies in Neuromuscular Disorders

Assisted annotation in Deep LOGISMOS: Simultaneous multi‐compartment 3D MRI segmentation of calf muscles

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