  In medical imaging, accurate image segmentation is crucial for quantifying
diseases, assessing prognosis, and evaluating treatment outcomes. However,
existing methods lack an in-depth integration of global and local features,
failing to pay special attention to abnormal regions and boundary details in
medical images. To this end, we present a novel deep learning-based approach,
MIPC-Net, for precise boundary segmentation in medical images. Our approach,
inspired by radiologists' working patterns, features two distinct modules: (i)
\textbf{Mutual Inclusion of Position and Channel Attention (MIPC) module}: To
enhance the precision of boundary segmentation in medical images, we introduce
the MIPC module, which enhances the focus on channel information when
extracting position features and vice versa; (ii) \textbf{GL-MIPC-Residue}: To
improve the restoration of medical images, we propose the GL-MIPC-Residue, a
global residual connection that enhances the integration of the encoder and
decoder by filtering out invalid information and restoring the most effective
information lost during the feature extraction process. We evaluate the
performance of the proposed model using metrics such as Dice coefficient (DSC)
and Hausdorff Distance (HD) on three publicly accessible datasets: Synapse,
ISIC2018-Task, and Segpc. Our ablation study shows that each module contributes
to improving the quality of segmentation results. Furthermore, with the
assistance of both modules, our approach outperforms state-of-the-art methods
across all metrics on the benchmark datasets, notably achieving a 2.23mm
reduction in HD on the Synapse dataset, strongly evidencing our model's
enhanced capability for precise image boundary segmentation. Codes will be
available at https://github.com/SUN-1024/MIPC-Net.
