通过融合轴向、冠状、矢状卷积与ImageNet预训练权重，提升脑肿瘤分割效率与精度。
Efficient 3D Brain Tumor Segmentation with Axial-Coronal-Sagittal Embedding
- 引入轴向-冠状-矢状卷积与ImageNet预训练权重，优化nnU-Net结构。
- 训练轮次减少，可训练参数降低，且在快速训练下性能媲美交叉验证集成模型。
- 适用于需要高效训练的医学图像分割场景，尤其适合资源受限环境。
本文针对医学影像中的脑肿瘤分割任务提出创新方法。当前最先进的nnU-Net虽表现良好，但存在训练耗时长、预训练权重利用率低的问题。为此，本文将轴向-冠状-矢状卷积与ImageNet预训练权重融入nnU-Net框架，显著减少训练轮次与可训练参数量，提升整体效率。提出了两种将2D预训练权重迁移至3D域的策略，有效保留关键特征关系与信息传播能力。此外，探索了基于脑胶质瘤分级分类代理任务的预训练编码器联合分割模型，显著提升对复杂肿瘤标签的分割性能。实验表明，在快速训练设置下，所提方法达到或超过文献中常见的交叉验证模型集成效果。
原文摘要 · Abstract (English)
In this paper, we address the crucial task of brain tumor segmentation in medical imaging and propose innovative approaches to enhance its performance. The current state-of-the-art nnU-Net has shown promising results but suffers from extensive training requirements and underutilization of pre-trained weights. To overcome these limitations, we integrate Axial-Coronal-Sagittal convolutions and pre-trained weights from ImageNet into the nnU-Net framework, resulting in reduced training epochs, reduced trainable parameters, and improved efficiency. Two strategies for transferring 2D pre-trained weights to the 3D domain are presented, ensuring the preservation of learned relationships and feature representations critical for effective information propagation. Furthermore, we explore a joint classification and segmentation model that leverages pre-trained encoders from a brain glioma grade classification proxy task, leading to enhanced segmentation performance, especially for challenging tumor labels. Experimental results demonstrate that our proposed methods in the fast training settings achieve comparable or even outperform the ensemble of cross-validation models, a common practice in the brain tumor segmentation literature.
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