Dataset Information

MRI adipose tissue segmentation and quantification in R (RAdipoSeg).

ABSTRACT:

Background

Excess adipose tissue is associated with increased cardiovascular and metabolic risk, but the volume of visceral and subcutaneous adipose tissue poses different metabolic risks. MRI with fat suppression can be used to accurately quantify adipose depots. We have developed a new semi-automatic method, RAdipoSeg, for MRI adipose tissue segmentation and quantification in the free and open source statistical software R.

Methods

MRI images were obtained from wild-type mice on high- or low-fat diet, and from 20 human subjects without clinical signs of metabolic dysfunction. For each mouse and human subject, respectively, 10 images were segmented with RAdipoSeg and with the commercially available software SliceOmatic. Jaccard difference, relative volume difference and Spearman's rank correlation coefficients were calculated for each group. Agreement between the two methods were analysed with Bland-Altman plots.

Results

RAdipoSeg performed similarly to the commercial software. The mean Jaccard differences were 10-29% and the relative volume differences were below ( ±) 20%. Spearman's rank correlation coefficient gave p-values below 0.05 for both mouse and human images. The Bland-Altman plots indicated some systematic and proporitional bias, which can be countered by the flexible nature of the method.

Conclusion

RAdipoSeg is a reliable and low cost method for fat segmentation in studies of mice and humans.

SUBMITTER: Haugen C

PROVIDER: S-EPMC9548171 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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Publications

MRI adipose tissue segmentation and quantification in R (RAdipoSeg).

Haugen Christine C Lysne Vegard V Haldorsen Ingfrid I Tjora Erling E Gudbrandsen Oddrun Anita OA Sagen Jørn Vegard JV Dankel Simon N SN Mellgren Gunnar G

Diabetology & metabolic syndrome 20221008 1

<h4>Background</h4>Excess adipose tissue is associated with increased cardiovascular and metabolic risk, but the volume of visceral and subcutaneous adipose tissue poses different metabolic risks. MRI with fat suppression can be used to accurately quantify adipose depots. We have developed a new semi-automatic method, RAdipoSeg, for MRI adipose tissue segmentation and quantification in the free and open source statistical software R.<h4>Methods</h4>MRI images were obtained from wild-type mice on ...[more]

PMID: 36209247

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Project description:Purpose: Infiltration of fat into lower limb muscles is one of the key markers for the severity of muscle pathologies. The level of fat infiltration varies in its severity across and within patients, and it is traditionally estimated using visual radiologic inspection. Precise quantification of the severity and spatial distribution of this pathological process requires accurate segmentation of lower limb anatomy into muscle and fat. Methods: Quantitative magnetic resonance imaging (qMRI) of the calf and thigh muscles is one of the most effective techniques for estimating pathological accumulation of intra-muscular adipose tissue (IMAT) in muscular dystrophies. In this work, we present a new deep learning (DL) network tool for automated and robust segmentation of lower limb anatomy that is based on the quantification of MRI's transverse (T2) relaxation time. The network was used to segment calf and thigh anatomies into viable muscle areas and IMAT using a weakly supervised learning process. A new disease biomarker was calculated, reflecting the level of abnormal fat infiltration and disease state. A biomarker was then applied on two patient populations suffering from dysferlinopathy and Charcot-Marie-Tooth (CMT) diseases. Results: Comparison of manual vs. automated segmentation of muscle anatomy, viable muscle areas, and intermuscular adipose tissue (IMAT) produced high Dice similarity coefficients (DSCs) of 96.4%, 91.7%, and 93.3%, respectively. Linear regression between the biomarker value calculated based on the ground truth segmentation and based on automatic segmentation produced high correlation coefficients of 97.7% and 95.9% for the dysferlinopathy and CMT patients, respectively. Conclusions: Using a combination of qMRI and DL-based segmentation, we present a new quantitative biomarker of disease severity. This biomarker is automatically calculated and, most importantly, provides a spatially global indication for the state of the disease across the entire thigh or calf.

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Dataset Information

MRI adipose tissue segmentation and quantification in R (RAdipoSeg).

Background

Methods

Results

Conclusion

Publications

MRI adipose tissue segmentation and quantification in R (RAdipoSeg).

Similar Datasets

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