TumorSynth: Integrated Brain Tumor and Tissue Segmentation on Brain MRI Scans of Any Resolution and Contrast

Purpose To develop and validate a deep neural network that simultaneously segments brain tumors and anatomic structures, regardless of the contrast and resolution of the input scans, and can effortlessly adapt to unseen modalities. Materials and Methods The authors included various MRI scans from patients with and without brain tumors from four different datasets. Patient data were divided into a training set and a test set. The authors' method, TumorSynth, combines a Bayesian generative model and a deep learning segmentation model. The generative model creates paired synthetic labels and images with simulated tumors and brain tissues, providing a rich dataset for training the segmentation model. The authors quantitatively compared its performance with that of other widely used methods by calculating Dice similarity coefficients (DSCs). Results A total of 1971 patients with and without tumors were included in the study (training set, n = 351 patients; test set, n = 1620 patients). The median DSCs for segmentation (authors' method vs reference standard) were 0.89 (IQR, 0.83-0.95; P < .001) for the unaffected brain volume and 0.89 (IQR, 0.84-0.94; P < .001) for the tumor region. There were no differences in parcellation performance when an MRI sequence was missing (P = .07). In cross-modality validation, the authors' method achieved DSC values of 0.88 for apparent diffusion coefficient, 0.85 for diffusion-weighted imaging, 0.80 for susceptibility-weighted imaging, and 0.79 for fractional anisotropy images. The authors observed a 4% false-positive rate when processing tumor-free MR images. Conclusion The authors developed a deep neural network for brain tumor and tissue segmentation, validated its performance across standard structural MRI sequences, and determined its generalizability to unseen data. Keywords: Segmentation, Neuro-Oncology, CNS, Deep Learning, Neurosurgery Supplemental material is available online for this article. © RSNA, 2026.