标题： 基于轴位-冠状位-矢状位嵌入的高效三维脑肿瘤分割 显示英文标题

标题： Efficient 3D Brain Tumor Segmentation with Axial-Coronal-Sagittal Embedding

摘要： 本文针对医学影像中脑肿瘤分割这一关键任务，提出了创新的方法以提升其性能。目前最先进的nnU-Net已显示出良好的结果，但存在训练需求繁重且未能充分利用预训练权重的问题。 为克服这些局限性，我们将轴向、冠状面和矢状面卷积以及ImageNet的预训练权重整合到nnU-Net框架中，从而减少了训练周期数、可训练参数量，并提高了效率。提出两种策略将二维预训练权重转移到三维领域，确保了有效信息传播至关重要的学习关系和特征表示得以保留。 此外，我们探索了一种联合分类与分割模型，利用来自脑胶质瘤分级分类代理任务的预训练编码器，这显著提升了分割性能，特别是在处理具有挑战性的肿瘤标签时。 实验结果显示，在快速训练设置下，我们提出的方案达到了与交叉验证模型集成方法相当甚至更好的效果，这是脑肿瘤分割文献中的常见做法。 显示英文摘要

摘要： 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.