轻量级3D U-Net用注意力门提升脑肿瘤分割精度
Efficient Brain Tumor Segmentation Using a Dual-Decoder 3D U-Net with Attention Gates (DDUNet)
- 双解码器结构加注意力门，减少计算开销
- 50轮训练达85.06%整体肿瘤分割率
- 适合算力有限的临床或研究场景
癌症是全球主要死亡原因之一，其中脑肿瘤因侵袭性强且早期诊断困难而尤为棘手。人工智能在辅助医生精准分割脑肿瘤方面展现出巨大潜力，但多数先进方法需大量计算资源与长时间训练，限制了其在资源受限环境中的应用。本文提出一种新型双解码器3D U-Net架构，结合注意力门跳过连接，专为MRI脑肿瘤分割设计。该模型在保证高精度的同时显著降低训练需求。在BraTS 2020数据集上，仅用50个训练周期即达到85.06%（整体肿瘤，WT）、80.61%（肿瘤核心，TC）和71.26%（增强肿瘤，ET）的Dice分数，优于多个常用U-Net变体。结果表明，在有限算力条件下亦可实现高质量分割，为算力不足的研究者与临床工作者提供可行方案，有望提升脑肿瘤早期检测与诊断水平。
原文摘要 · Abstract (English)
Cancer remains one of the leading causes of mortality worldwide, and among its many forms, brain tumors are particularly notorious due to their aggressive nature and the critical challenges involved in early diagnosis. Recent advances in artificial intelligence have shown great promise in assisting medical professionals with precise tumor segmentation, a key step in timely diagnosis and treatment planning. However, many state-of-the-art segmentation methods require extensive computational resources and prolonged training times, limiting their practical application in resource-constrained settings. In this work, we present a novel dual-decoder U-Net architecture enhanced with attention-gated skip connections, designed specifically for brain tumor segmentation from MRI scans. Our approach balances efficiency and accuracy by achieving competitive segmentation performance while significantly reducing training demands. Evaluated on the BraTS 2020 dataset, the proposed model achieved Dice scores of 85.06% for Whole Tumor (WT), 80.61% for Tumor Core (TC), and 71.26% for Enhancing Tumor (ET) in only 50 epochs, surpassing several commonly used U-Net variants. Our model demonstrates that high-quality brain tumor segmentation is attainable even under limited computational resources, thereby offering a viable solution for researchers and clinicians operating with modest hardware. This resource-efficient model has the potential to improve early detection and diagnosis of brain tumors, ultimately contributing to better patient outcomes
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