A Dual-Branch Network with Mixed and Self-Supervision for Medical Image Segmentation: An Application to Segment Edematous Adipose Tissue

Liu, Jianfei; Shafaat, Omid; Summers, Ronald M.

doi:10.1007/978-3-031-44917-8_15

Lecture Notes in Computer Science

2023

DOI: 10.1007/978-3-031-44917-8_15

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A Dual-Branch Network with Mixed and Self-Supervision for Medical Image Segmentation: An Application to Segment Edematous Adipose Tissue

Jianfei Liu,

Omid Shafaat,

Ronald M. Summers

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2024

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Cited by 2 publications

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Enhanced muscle and fat segmentation for CT-based body composition analysis: a comparative study

Hou,

Mathai,

Liu

et al. 2024

Int J CARS

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Purpose Body composition measurements from routine abdominal CT can yield personalized risk assessments for asymptomatic and diseased patients. In particular, attenuation and volume measures of muscle and fat are associated with important clinical outcomes, such as cardiovascular events, fractures, and death. This study evaluates the reliability of an Internal tool for the segmentation of muscle and fat (subcutaneous and visceral) as compared to the well-established public TotalSegmentator tool. Methods We assessed the tools across 900 CT series from the publicly available SAROS dataset, focusing on muscle, subcutaneous fat, and visceral fat. The Dice score was employed to assess accuracy in subcutaneous fat and muscle segmentation. Due to the lack of ground truth segmentations for visceral fat, Cohen’s Kappa was utilized to assess segmentation agreement between the tools. Results Our Internal tool achieved a 3% higher Dice (83.8 vs. 80.8) for subcutaneous fat and a 5% improvement (87.6 vs. 83.2) for muscle segmentation, respectively. A Wilcoxon signed-rank test revealed that our results were statistically different with p < 0.01. For visceral fat, the Cohen’s Kappa score of 0.856 indicated near-perfect agreement between the two tools. Our internal tool also showed very strong correlations for muscle volume (R$$^2$$ 2 =0.99), muscle attenuation (R$$^2$$ 2 =0.93), and subcutaneous fat volume (R$$^2$$ 2 =0.99) with a moderate correlation for subcutaneous fat attenuation (R$$^2$$ 2 =0.45). Conclusion Our findings indicated that our Internal tool outperformed TotalSegmentator in measuring subcutaneous fat and muscle. The high Cohen’s Kappa score for visceral fat suggests a reliable level of agreement between the two tools. These results demonstrate the potential of our tool in advancing the accuracy of body composition analysis.

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Enhanced muscle and fat segmentation for CT-based body composition analysis: a comparative study

Hou,

Mathai,

Liu

et al. 2024

Int J CARS

View full text Add to dashboard Cite

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Subcutaneous edema segmentation on abdominal CT using multi-class labels and iterative annotation

Bhadra,

Liu,

Summers

2024

Int J CARS

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Purpose: Anasarca is a condition that results from organ dysfunctions, such as heart, kidney, or liver failure, characterized by the presence of edema throughout the body. The quantification of accumulated edema may have potential clinical benefits. This work focuses on accurately estimating the amount of edema non-invasively using abdominal CT scans, with minimal false positives. However, edema segmentation is challenging due to the complex appearance of edema and the lack of manually annotated volumes. Methods: We propose a weakly supervised approach for edema segmentation using initial edema labels from the current state-of-the-art method for edema segmentation (Intensity Prior), along with labels of surrounding tissues as anatomical priors. A multi-class 3D nnU-Net was employed as the segmentation network, and training was performed using an iterative annotation workflow. Results: We evaluated segmentation accuracy on a test set of 25 patients with edema. The average Dice Similarity Coefficient of the proposed method was similar to Intensity Prior (61.5% vs. 61.7%; $$p=0.83$$ p = 0.83 ). However, the proposed method reduced the average False Positive Rate significantly, from 1.8% to 1.1% ($$p<0.001$$ p < 0.001 ). Edema volumes computed using automated segmentation had a strong correlation with manual annotation ($$R^2=0.87$$ R 2 = 0.87 ). Conclusion: Weakly supervised learning using 3D multi-class labels and iterative annotation is an efficient way to perform high-quality edema segmentation with minimal false positives. Automated edema segmentation can produce edema volume estimates that are highly correlated with manual annotation. The proposed approach is promising for clinical applications to monitor anasarca using estimated edema volumes.

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A Dual-Branch Network with Mixed and Self-Supervision for Medical Image Segmentation: An Application to Segment Edematous Adipose Tissue

Cited by 2 publications

References 16 publications

Enhanced muscle and fat segmentation for CT-based body composition analysis: a comparative study

Enhanced muscle and fat segmentation for CT-based body composition analysis: a comparative study

Subcutaneous edema segmentation on abdominal CT using multi-class labels and iterative annotation

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