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DALight-3D, a lightweight 3D U-Net for brain tumor segmentation, is introduced, combining depthwise separable convolutions, identifier-conditioned normalization, cross-slice attention, and adaptive skip fusion to reduce computational cost. Evaluated on the Medical Segmentation Decathlon Task01 BrainTumour benchmark, DALight-3D achieves a Dice score of 0.727 with 2.22M parameters. Ablation studies confirm the contribution of each component to the overall performance, demonstrating an improved accuracy-efficiency trade-off compared to standard 3D U-Net variants.
Brain tumor segmentation gets a lightweight boost: DALight-3D achieves comparable accuracy to larger U-Nets with significantly fewer parameters.
Automatic brain tumor segmentation from multi-modal MRI remains challenging because volumetric models often incur substantial computational cost. This paper presents DALight-3D, a compact 3D U-Net variant that combines depthwise separable 3D convolutions, identifier-conditioned normalization, cross-slice attention, and adaptive skip fusion. The method is evaluated on the Medical Segmentation Decathlon Task01 BrainTumour benchmark under matched optimization settings against standard 3D U-Net, Attention U-Net, Residual 3D U-Net, and V-Net baselines. In the reported 50-epoch comparison, DALight-3D achieves a mean Dice of 0.727 with 2.22M parameters, compared with 0.710 Dice and 3.20M parameters for Residual 3D U-Net. Component-wise ablations show consistent performance degradation when SepConv, identifier-conditioned normalization, CSA, or SSFB is removed. These results indicate that DALight-3D offers a favorable accuracy-efficiency trade-off within the present benchmark setting.
