Fig. 1: Integrated bone marrow automatic segmentation and fat fraction calculation strategy (IBAS-FFCS).

a IBAS-FFCS can calculate the fat fraction of the 8th to 12th thoracic vertebra, the 1st to 5th lumbar vertebra, and the proximal femur on both sides. b Part1. This study aligned and concatenated the six 3D 2-point Dixon MRI images and applied image enhancement strategies such as scaling intensity and random flipping to the images. All data were cropped according to the bounding box of the spine and femur, and then fed into the 3D-Unet model. Part2. The preprocessed images were fed into a deep learning 3D-Unet model for training, leveraging a GPU server equipped with an NVIDIA RTX 4090D GPU. The U-Net segmentation algorithm was continuously optimized based on the Dice loss function and the Adam algorithm. The topology of the whole network consists of the encoding and decoding subnetworks. Four stages in encoding and decoding counterparts indicate that four-level scales of feature maps were formulated for automatic feature extraction. Part3. The 3D-Unet outputted visual masks of the femur and spine, with different colors representing the left and right femurs, as well as the spine from L1 to Th8. Part4. The volume of interest (VOI) for the spine and femurs was extracted and transferred to the fat and water images of the two-point Dixon sequence, where the bone marrow fat fraction is calculated based on a computational formula. Panel a was created in BioRender. Wu, Z. (2025) https://BioRender.com/qdjyk0e.