PURPOSE

3D ultrasound delivers high-resolution, real-time images of soft tissues, which are essential for pain research. However, manually distinguishing various tissues for quantitative analysis is labor-intensive. We aimed to automate multilayer segmentation in 3D ultrasound volumes using minimal annotated data by developing generative reinforcement network plus (GRN+), a semi-supervised multi-model framework.

APPROACH

GRN+ integrates a ResNet-based generator and a U-Net segmentation model. Through a method called segmentation-guided enhancement (SGE), the generator produces new images under the guidance of the segmentation model, with its weights adjusted according to the segmentation loss gradient. To prevent gradient explosion and secure stable training, a two-stage backpropagation strategy was implemented: the first stage propagates the segmentation loss through both the generator and segmentation model, whereas the second stage concentrates on optimizing the segmentation model alone, thereby refining mask prediction using the generated images.

RESULTS

Tested on 69 fully annotated 3D ultrasound scans from 29 subjects with six manually labeled tissue layers, GRN+ outperformed all other semi-supervised methods in terms of the Dice coefficient using only 5% labeled data, despite not using unlabeled data for unsupervised training. In addition, when applied to fully annotated datasets, GRN+ with SGE achieved a 2.16% higher Dice coefficient while incurring lower computational costs compared to other models.

CONCLUSIONS

GRN+ provides accurate tissue segmentation while reducing both computational expenses and the dependency on extensive annotations, making it an effective tool for 3D ultrasound analysis in patients with chronic lower back pain.