Clinical evaluation of automated segmentation for body composition analysis on abdominal L3 CT slices in polytrauma patients

Ackermans, Leanne L.G.C.; Volmer, Leroy; Timmermans, Quince M.M.A.; Brecheisen, Ralph; Damink, Steven W. M. Olde; Dekker, André; Loeffen, D.; Poeze, Martijn; Blokhuis, Taco J.; Wee, Leonard; Bosch, Jan A. Ten

doi:10.1016/j.injury.2022.05.004

Cited by 13 publications

(7 citation statements)

References 38 publications

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“…It will require further software improvements before it is applicable for clinical use. The time requirements could be reduced further by applying fully automated software based on artificial intelligence, machine learning, and neural networks, as seen in other studies with abdominal segmentation [ 31 , 32 ], thereby reducing the operator workload.…”

Section: Discussionmentioning

confidence: 99%

Association between thoracic and third lumbar CT-derived muscle mass and density in Caucasian patients without chronic disease: a proof-of-concept study

et al. 2023

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Background Computed tomography (CT) is increasingly used in the clinical workup, and existing scan contains unused body composition data, potentially useful in a clinical setting. However, there is no healthy reference for contrast-enhanced thoracic CT-derived muscle measures. Therefore, we aimed at investigating whether there is a correlation between each of the thoracic and third lumbar vertebra level (L3) skeletal muscle area (SMA), skeletal muscle index (SMI), and skeletal muscle density (SMD) at contrast-enhanced CT in patients without chronic disease. Methods A proof-of-concept retrospective observational study was based on Caucasian patients without chronic disease, who received CT for trauma between 2012 and 2014. Muscle measures were assessed using a semiautomated threshold-based software by two raters independently. Pearson’s correlation between each thoracic level and third lumbar and intraclass correlation between two raters and test–retest with SMA as proxy parameters were used. Results Twenty-one patients (11 males, 10 females; median age 29 years) were included. The second thoracic vertebra (T2) had the highest median of cumulated SMA (males 314.7 cm2, females 118.5 cm2) and SMI (97.8 cm2/m2 and 70.4 cm2/m2, respectively). The strongest SMA correlation was observed between T5 and L3 (r = 0.970), the SMI between T11 and L3 (r = 0.938), and the SMD between the T10 and L3 (r = 0.890). Conclusions This study suggests that any of the thoracic levels can be valid to assess skeletal muscle mass. However, the T5 may be most favourable for measuring SMA, the T11 for SMI, and T10 for SMD when using contrast-enhanced thoracic CT. Relevance statement In COPD patients, a CT-derived thoracic muscle mass assessment may help identify who would benefit from focused pulmonary rehabilitation: thoracic contrast-enhanced CT conducted as part of the standard clinical workup can be used for this evaluation. Key points • Any thoracic level can be used to assess thoracic muscle mass. • Thoracic level 5 is strongly associated with the 3rd lumbar muscle area. • A strong correlation between the thoracic level 11 and the 3rd lumbar muscle index. • Thoracic level 10 is strongly associated with the 3rd lumbar muscle density. Graphical Abstract

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Section: Discussionmentioning

confidence: 99%

Association between thoracic and third lumbar CT-derived muscle mass and density in Caucasian patients without chronic disease: a proof-of-concept study

et al. 2023

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“…Particularly in analyzing body composition from abdominal CT scans, advancements in automatic positioning, recognition, and segmentation technologies have been momentous. Automated segmentation techniques have demonstrated not only time e ciency but also comparative, if not superior, accuracy to manual methodologies.The studies by Dabiri et al [36] and Ackermans et al [37] provide strong evidence for the application of automated segmentation techniques. In particular, for body composition analysis at the L3 position, this automated method not only saves signi cant time, but also provides similar or even higher accuracy compared to conventional manual analysis methods.…”

Section: Progress In Body Composition Segmentation On Ct Imagesmentioning

confidence: 98%

A new attempt at full-scale jump connectivity and Transformer

yin,

chen

2023

Preprint

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In this research, we introduce SwinUnet3+, a pioneering algorithm that integrates Unet with Transformer, to facilitate the automatic segmentation of three primary tissues—subcutaneous fat layer, muscle, and intramuscular fat—in the thoracoabdominal region under challenging conditions, including subcutaneous soft tissue swelling, gas accumulation, artifacts, and fistulas. Our model showcases superior performance in body composition segmentation tasks, with improvements in DSC, IoU, sensitivity, and positive predictive value by 3.2%, 6.05%, 4.03%, and 2.34%, respectively. Notably, in segmenting subcutaneous fat, intramuscular fat, and muscle, SwinUnet3 + yielded the best outcomes. However, the model does exhibit certain limitations, such as a reliance on vast amounts of training data and potential challenges in handling certain image types. Additionally, high-resolution images may pose computational efficiency concerns. In conclusion, while SwinUnet3 + offers considerable advantages in complex medical image segmentation tasks, its limitations warrant acknowledgment. Future research will focus on addressing these challenges and enhancing the model's robustness and generalization capabilities.

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“…Automatically identifying and segmenting different tissue types in medical images with deep learning methods has raised great interest among researchers [5,6] , especially in computed tomography (CT) images [4,7,8] . This method has achieved comparable performance with manual labeling in CT image segmentation tasks.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Body Compositions Assessment Application with L3 CT Images: Multiple Validations

Zhang,

Li,

Yang

et al. 2024

Preprint

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Objectives: Body composition assessment is very useful for evaluating a patient’s status in the clinic, but recognizing, labeling, and calculating the body compositions would be burdensome.This study aims to develop a web-based service that could automate calculating the areas of skeleton muscle (SM), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) according to L3 computed tomography (CT) images. Materials & Methods: 1200 L3 CT images were gathered from Xuzhou Central Hospital. Of these, 70% were used as the training dataset, while the remaining 30% were used as the validating dataset. The UNet framework was combined with attention gate, Squeeze and Excitation block (SEblock), and Atrous Spatial Pyramid Pooling (ASSP) modules to construct the segmentation deep learning model. The model's efficacy was externally validated using two other test datasets with multiple metrics, the consistency test and manual result checking. A graphic user interface was also created and deployed using the Streamlit Python package. Results： The custom deep learning model named L3 Body Composition Segmentation Model (L3BCSM) was constructed. The model's Mean Dice is 0.932(SATA)，0.915(VATA), and 0.956(SMA), which is equal to classic models including UNETR and AHNet. L3BCSM also achieved satisfactory metrics in two external test datasets, consistent with the qualified label and resistant to the poor label. An internet-based application was developed using L3BCSM, which has four functional modules: population analysis, time series analysis, consistency analysis, and manual result checking. Conclusion: The body composition assessment application was well developed, which would benefit the clinical practice and related research.

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Clinical evaluation of automated segmentation for body composition analysis on abdominal L3 CT slices in polytrauma patients

Cited by 13 publications

References 38 publications

Association between thoracic and third lumbar CT-derived muscle mass and density in Caucasian patients without chronic disease: a proof-of-concept study

Association between thoracic and third lumbar CT-derived muscle mass and density in Caucasian patients without chronic disease: a proof-of-concept study

A new attempt at full-scale jump connectivity and Transformer

A Deep Learning Body Compositions Assessment Application with L3 CT Images: Multiple Validations

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