Automated major psoas muscle volumetry in computed tomography using machine learning algorithms

• Published in: International journal for computer assisted radiology and surgery Vol. 17; no. 2; pp. 355 - 361
• Main Authors: Duong, Felix, Gadermayr, Michael, Merhof, Dorit, Kuhl, Christiane, Bruners, Philipp, Loosen, Sven H., Roderburg, Christoph, Truhn, Daniel, Schulze-Hagen, Maximilian F.
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.02.2022; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Automation; Computed tomography; Computer architecture; Computer Imaging; Computer Science; Correlation coefficients; Datasets; Generative adversarial networks; Health Informatics; Humans; Image Processing, Computer-Assisted; Image segmentation; Imaging; Machine Learning; Medical imaging; Medicine; Medicine & Public Health; Muscles; Original; Original Article; Pattern Recognition and Graphics; Psoas Muscles - diagnostic imaging; Radiology; Rank tests; Surgery; Tomography, X-Ray Computed; Training; Vision; Psoas major muscle; Opportunistic imaging; Machine learning; Generative adversarial network
• ISSN: 1861-6410; 1861-6429; 1861-6429
• DOI: 10.1007/s11548-021-02539-2

Purpose The psoas major muscle (PMM) volume serves as an opportunistic imaging marker in cross-sectional imaging datasets for various clinical applications. Since manual segmentation is time consuming, two different automated segmentation methods, a generative adversarial network architecture (GAN) and a multi-atlas segmentation (MAS), as well as a combined approach of both, were investigated in terms of accuracy of automated volumetrics in given CT datasets. Materials and methods The bilateral PMM was manually segmented by a radiologist in 34 abdominal CT scans, resulting in 68 single 3D muscle segmentations as training data. Three different methods were tested for their ability to generate automated image segmentations: a GAN- and MAS-based approach and a combined approach of both methods (COM). Bilateral PMM volume (PMMV) was calculated in cm 3 by each algorithm for every CT. Results were compared to the corresponding ground truth using the Dice similarity coefficient (DSC), Spearman’s correlation coefficient and Wilcoxon signed-rank test. Results Mean PMMV was 239 ± 7.0 cm 3 and 308 ± 9.6 cm 3 , 306 ± 9.5 cm 3 and 243 ± 7.3 cm 3 for the CNN, MAS and COM, respectively. Compared to the ground truth the CNN and MAS overestimated the PMMV significantly (+ 28.9% and + 28.0%, p  < 0.001), while results of the COM were quite accurate (+ 0.7%, p  = 0.33). Spearman’s correlation coefficients were 0.38, 0.62 and 0.73, and the DSCs were 0.75 [95%CI: 0.56–0.88], 0.73 [95%CI: 0.54–0.85] and 0.82 [95%CI: 0.65–0.90] for the CNN, MAS and COM, respectively. Conclusion The combined approach was able to efficiently exploit the advantages of both methods (GAN and MAS), resulting in a significantly higher accuracy in PMMV predictions compared to the isolated implementations of both methods. Even with the relatively small set of training data, the segmentation accuracy of this hybrid approach was relatively close to that of the radiologist.