TriGAN-SiaMT: A triple-segmentor adversarial network with bounding box priors for semi-supervised brain lesion segmentation

Accurate brain lesion segmentation in MRI is critical for clinical decision-making, but pixel-wise annotations remain costly and time-consuming. We propose TriGAN-SiaMT, a novel semi-supervised segmentation framework that combines adversarial learning, consistency regularization, and bounding box priors. Our architecture comprises three segmentors ( S ₀, S ₁, S ₂) and two discriminators ( D ₀, D ₁). It includes: (1) a supervised branch ( S ₀↔ D ₀) trained on a small labeled subset; (2) a Siamese branch ( S ₁↔ D ₁) with an identical architecture to S ₀↔ D ₀, but trained on unlabeled data; and (3) a teacher branch ( S ₂) updated via exponential moving average (EMA) from S ₁, following the Mean Teacher (MT) paradigm. The teacher S ₂ generates pseudo-labels to supervise S ₁. It also provides soft segmentations to guide D ₁, which does not see any labeled data. The model enforces consistency at multiple levels: between S ₀ and S ₁ (Siamese consistency), and between S ₁ and S ₂ (EMA consistency). Bounding box priors are incorporated as weak supervision for both labeled and unlabeled images, improving lesion localization. Evaluated on the ISLES 2022 and BraTS 2019 datasets, TriGAN-SiaMT achieves DSC scores of 84.80 % and 86.32 %, respectively, using only 5 % labeled data. These results demonstrate strong performance under limited supervision and robust generalization across brain lesions.