Health AI Central
Peer-Reviewed Publication
Phys Med Biol2026;71(5)March 13, 2026Journal Article

Transformer-encoded nnU-Net with local region perceptron and contrastive learning (TLC-nnUNet) for multiple brain metastasis detection and delineation.

Kangning Zhang1, Gregory Arthur Szalkowski1, Scott Soltys1, Yusuke S Hori2, David J Park2, Robert Timmerman3, Zabi Wardak3, Lili He4, Qingying Wang3, Mingli Chen3, Hao Jiang3, Weiguo Lu3, Xuejun Gu1,3
1Department of Radiation Oncology, Stanford University, Stanford, CA 94305, United States of America.
2Department of Neurosurgery, Stanford University, Stanford, CA 94305, United States of America.
3Department of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America.
4Cincinnati Children's AI Imaging Research (CAIIR) Center, Cincinnati, OH 45229, United States of America.
PubMed PMC DOI PDF

Abstract

Accurate detection and segmentation of multiple brain metastases (BMs) on magnetic resonance image 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…

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