Multi-organ segmentation in medical image analysis is crucial for diagnosis and treatment planning. However, many factors complicate the task, including variability in different target categories and interference from complex backgrounds. In this paper, we utilize the knowledge of Deformable Convolution V3 (DCNv3) and multi-object segmentation to optimize our Spatially Adaptive Convolution Network (SACNet) in three aspects: feature extraction, model architecture, and loss constraint, simultaneously enhancing the perception of different segmentation targets. Firstly, we propose the Adaptive Receptive Field Module (ARFM), which combines DCNv3 with a series of customized block-level and architecture-level designs similar to transformers. This module can capture the unique features of different organs by adaptively adjusting the receptive field according to various targets. Secondly, we utilize ARFM as building blocks to construct the encoder-decoder of SACNet and partially share parameters between the encoder and decoder, making the network wider rather than deeper. This design achieves a shared lightweight decoder and a more parameter-efficient and effective framework. Lastly, we propose a novel continuity dynamic adjustment loss function, based on t-vMF dice loss and cross-entropy loss, to better balance easy and complex classes in segmentation. Experiments on 3D slice datasets from ACDC and Synapse demonstrate that SACNet delivers superior segmentation performance in multi-organ segmentation tasks compared to several existing methods.

翻译：医学图像分析中的多器官分割对于诊断和治疗规划至关重要。然而，多种因素使得该任务变得复杂，包括不同目标类别的变异性以及复杂背景的干扰。本文利用可变形卷积V3（DCNv3）和多目标分割的知识，从特征提取、模型架构和损失约束三个方面优化我们的空间自适应卷积网络（SACNet），同时增强对不同分割目标的感知能力。首先，我们提出了自适应感受野模块（ARFM），该模块将DCNv3与一系列类似于Transformer的定制化块级和架构级设计相结合。该模块能够根据不同目标自适应调整感受野，从而捕获不同器官的独特特征。其次，我们利用ARFM作为构建块来构造SACNet的编码器-解码器，并在编码器和解码器之间部分共享参数，使网络更宽而非更深。这种设计实现了共享的轻量化解码器，构建了参数效率更高且更有效的框架。最后，我们提出了一种基于t-vMF dice损失和交叉熵损失的新型连续性动态调整损失函数，以更好地平衡分割中的简单类别和复杂类别。在ACDC和Synapse的3D切片数据集上的实验表明，与现有多种方法相比，SACNet在多器官分割任务中实现了更优的分割性能。