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

Abstract

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 cm3 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 cm3 and 308 ± 9.6 cm3, 306 ± 9.5 cm3 and 243 ± 7.3 cm3 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.

Development and validation of a reliable method for automated measurements of psoas muscle volume in CT scans using deep learning-based segmentation: a cross-sectional study

“…Although many studies have examined muscle segmentation, the focus on the measurement of the psoas muscle volume has been relatively limited. For instance, Duong et al applied a convolutional neural network method to measure the psoas major muscle volume; however, their training data set comprised only 34 CT scans, 38 demonstrating limitations in the approach, with the requirement of manual measurements for accuracy. In contrast, this study employed a training data set consisting of 320 CT scans, leading to significantly higher accuracy in both qualitative and quantitative comparative analyses.…”

Digital Management of Educational Resource Allocation in Colleges and Universities Based on PSO Algorithm

“…There are many scholars studying PSO algorithm and DM of resource allocation. For example, some scholars use cloud computing technology to analyze the problems in the current management of CE resources [1][2]. Other scholars believe that the reconstruction of resource construction work mode is first reflected in the demand for digital resources [3][4].…”