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.

OBJECTIVES: We aimed to develop an automated method for measuring the volume of the psoas muscle using CT to aid sarcopenia research efficiently. METHODS: We used a data set comprising the CT scans of 520 participants who underwent health check-ups at a health promotion centre. We developed a psoas muscle segmentation model using deep learning in a three-step process based on the nnU-Net method. The automated segmentation method was evaluated for accuracy, reliability, and time required for the measurement. RESULTS: The Dice similarity coefficient was used to compare the manual segmentation with automated segmentation; an average Dice score of 0.927 ± 0.019 was obtained, with no critical outliers. Our automated segmentation system had an average measurement time of 2 min 20 s ± 20 s, which was 48 times shorter than that of the manual measurement method (111 min 6 s ± 25 min 25 s). CONCLUSION: We have successfully developed an automated segmentation method to measure the psoas muscle volume that ensures consistent and unbiased estimates across a wide range of CT images.

Inter-Slice Resolution Improvement Using Convolutional Neural Network with Orbital Bone Edge-Aware in Facial CT Images.

The 3D modeling of orbital bones in facial CT images is essential to provide a customized implant for reconstructions of orbit and related structures during surgery. However, 3D models of the orbital bone show an aliasing effect and disconnected thin bone in the inter-slice direction because the slice thickness is two to three times larger than the pixel spacing. To improve the inter-slice resolution of facial CT images, we propose a method based on a 2D convolutional neural network (CNN) that uses the spatial information on the sagittal and axial planes and the orbital bone edge-aware (OBE) loss. First, intermediate slices are generated on the sagittal plane. Second, the generated intermediate slices are transformed to an axial image, which is then compared with the original axial image. To generate intermediate slices with an accurate orbital bone structure, the OBE loss considering the orbital bone structure on the sagittal and axial planes is used. To improve the perceptual quality of the generated intermediate slices, the feature map difference loss is additionally used on the axial plane. In the experiment, the proposed method showed the best performance among bilinear and bicubic interpolations, 3D SRGAN, and a 2D CNN-based method. Experimental results confirmed that the proposed method can generate intermediate slices with clear edges of thin bones as well as cortical bones on both the sagittal and the axial plane.