LPM-GAN: Lumbar Paraspinal Muscle Segmentation Using a Generative Adversarial Network

After posterior lumbar surgeries (PLS), the change of the cross-sectional area(CSA) and fatty infiltration (FI) of paraspinal muscle can deeply affect the muscle activity pattern and spinal stability. The objective of this work is to perform automated paraspinal muscle (multifidus and erector spine) segmentation in magnetic resonance imaging (MRI) image. However, no work has achieved the semantic segmentation of multifidus (MF) and erector spinae (ES) due to three unusual challenges: (1) the distribution of paraspinal muscle overlaps with the distribution of other anatomical structures; (2) the fascia between MF and ES is unclear; (3) the intra- and inter-patient shape is variable. In this paper, we proposed a generative adversarial network called LPM-GAN which contains a generator and a discriminator to resolve above challenges. The generator solves the high variability and variety of paraspinal muscle through extracting high-level semantics of images and preserving the paraspinal muscle anatomy. And then, the discriminator is trained to optimize the predicted mask to make it closer to ground truth. Finally, we obtain the CSA and FI of paraspinal muscle by utilizing Otsu. Extensive experiments on MRIs of 69 patients have demonstrated that LPM-GAN achieves high Recall of 0.931 and 0.904, and Dice coefficient of 0.920 and 0.903, which reveals the method is effective