  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.
