Transformer-encoded nnU-Net with local region perceptron and contrastive learning (TLC-nnUNet) for multiple brain metastasis detection and delineation.
Authors
Affiliations (13)
Affiliations (13)
- Stanford University, 875 Blake Wilbur Dr, Palo Alto, Palo Alto, California, 94304-2205, United States.
- Stanford University, 875 Blake Wilbur Dr, Palo Alto, Palo Alto, California, 94305-2004, United States.
- Stanford University, 875 Blake Wilbur Dr., CA 94304, Stanford, Stanford, California, 94305, United States.
- Stanford University, 875 Blake Wilbur Dr., CA 94304, Stanford, California, 94305, United States.
- Stanford University, 875 Blake Wilbur Dr., CA 94304, Stanford, California, 94305-2004, United States.
- Department of Radiation Oncology, The University of Texas Southwestern Medical Center, 2280 Inwood Rd, Dallas, Texas, 75390, United States.
- Department of Radiation Oncology, University of Texas Southwestern Medical Center at Dallas, 2280 Inwood Rd, Dallas, Texas, 75390, United States.
- Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, 45229-3026, United States.
- Radiation Oncology, UT Southwestern Medical Center, 2280 Inwood Rd, Dallas, Texas, 75390, United States.
- Radiation Oncology, University of Texas Southwestern Medical Center at Dallas, 6363 Forest Park Rd, Dallas, Texas, 75390-9315, United States.
- UT Southwestern Medical Center, 2280 Inwood Rd, Dallas, Texas, 75390, United States.
- Department of Radiation Oncology, UT Southwestern Medical Center, 2280 Inwood Rd, Dallas, Texas, 75390, United States.
- Department of Radiation Oncology, Stanford University, 875 Blake Wilbur Dr., CA 94304, Stanford, California, 94304, United States.
Abstract
Accurate detection and segmentation of multiple brain metastases (BMs) on MRI remain challenging, particularly for those involving small lesions (longest axis length <3 mm), due to limitations in sensitivity, precision, and feature representation in existing deep learning frameworks. To address this challenge, we develop TLC-nnUNet, a novel integration of two advancements into transformer-enhanced nnU-Net (T-nnUNet) architecture: 1) Local Region Perceptron (LRP), a loss constraint prioritizing small BM detection by up-weighting underrepresented voxels; and 2) Contrastive Learning Pretraining (CLP), a supervised model pre-training strategy to amplify latent-space divergence between BM and non-BM regions, reducing false positives (FPs). The developed TLC-nnUNet is trained and evaluated on a multi-institutional dataset and achieves state-of-the-art performance, with 89.70% sensitivity, 97.34% precision, and a Dice coefficient (DC) of 0.92 at per-patient level. Further ablation studies confirm the synergistic contributions of each component: LRP enhances small BM detection, while CLP refines feature contrast, reducing FPs. Visualization via t-SNE underscores CLP's role in disentangling BM and non-BM latent representations. Compared to existing methods, TLC-nnUNet demonstrates consistent accuracy of detection and segmentation cross lesion sizes. This framework holds significant promise for clinical workflows, enabling precise BM detection and segmentation in stereotactic radiosurgery (SRS) and reducing manual contouring time.