The accurate segmentation of brain tumor is significant in clinical practice. Convolutional Neural Network (CNN)-based methods have made great progress in brain tumor segmentation due to powerful local modeling ability. However, brain tumors are frequently pattern-agnostic, i.e. variable in shape, size and location, which can not be effectively matched by traditional CNN-based methods with local and regular receptive fields. To address the above issues, we propose a shape-scale co-awareness network (S2CA-Net) for brain tumor segmentation, which can efficiently learn shape-aware and scale-aware features simultaneously to enhance pattern-agnostic representations. Primarily, three key components are proposed to accomplish the co-awareness of shape and scale. The Local-Global Scale Mixer (LGSM) decouples the extraction of local and global context by adopting the CNN-Former parallel structure, which contributes to obtaining finer hierarchical features. The Multi-level Context Aggregator (MCA) enriches the scale diversity of input patches by modeling global features across multiple receptive fields. The Multi-Scale Attentive Deformable Convolution (MS-ADC) learns the target deformation based on the multiscale inputs, which motivates the network to enforce feature constraints both in terms of scale and shape for optimal feature matching. Overall, LGSM and MCA focus on enhancing the scale-awareness of the network to cope with the size and location variations, while MS-ADC focuses on capturing deformation information for optimal shape matching. Finally, their effective integration prompts the network to perceive variations in shape and scale simultaneously, which can robustly tackle the variations in patterns of brain tumors. The experimental results on BraTS 2019, BraTS 2020, MSD BTS Task and BraTS2023-MEN show that S2CA-Net has superior overall performance in accuracy and efficiency compared to other state-of-the-art methods. Code: https://github.com/jiangyu945/S2CA-Net.

中文翻译：

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用于 3D 脑肿瘤分割的形状尺度协同感知网络


脑肿瘤的准确分割在临床实践中具有重要意义。基于卷积神经网络（CNN）的方法凭借强大的局部建模能力，在脑肿瘤分割方面取得了巨大进展。然而，脑肿瘤通常是模式不可知的，即形状、大小和位置可变，这不能通过具有局部和规则感受野的基于 CNN 的传统方法有效匹配。为了解决上述问题，我们提出了一种用于脑肿瘤分割的形状尺度协同感知网络（S2CA-Net），它可以有效地同时学习形状感知和尺度感知特征，以增强与模式无关的表示。首先，提出了三个关键组成部分来实现形状和尺度的共同意识。局部-全局尺度混合器（LGSM）通过采用CNN-Former并行结构解耦局部和全局上下文的提取，这有助于获得更精细的层次特征。多级上下文聚合器 (MCA) 通过对多个感受野的全局特征进行建模，丰富了输入块的尺度多样性。多尺度注意力变形卷积（MS-ADC）根据多尺度输入学习目​​标变形，这促使网络在尺度和形状方面强制实施特征约束，以实现最佳特征匹配。总体而言，LGSM 和 MCA 侧重于增强网络的尺度感知，以应对尺寸和位置变化，而 MS-ADC 侧重于捕获变形信息以实现最佳形状匹配。最后，它们的有效整合促使网络同时感知形状和尺度的变化，这可以稳健地应对脑肿瘤模式的变化。 在BraTS 2019、BraTS 2020、MSD BTS Task和BraTS2023-MEN上的实验结果表明，与其他最先进的方法相比，S2CA-Net在准确性和效率方面具有优越的整体性能。代码：https://github.com/jianyu945/S2CA-Net。