Multi-view parallel convolutional network for organ segmentation in mediastinal region on CT images

In lung CT images, mediastinal organ segmentation is crucial for localizing different mediastinal regions. However, existing medical image segmentation methods exhibit significant limitations in modeling the diverse topological structures of organs, sensitivity to intra-class morphological variations, and inter-class feature differentiation. To address these limitations, we propose a novel multi-view parallel convolutional network (MVPCNet), built on an efficient U-shaped encoder-decoder framework. The shallow and deep information encoders are respectively composed of alternating multi-view parallel convolution module (MVPM) and the dual-path backbone structure (DPBS) at different scales. MVPM is designed as a parallel convolutional structure to enhance the model's ability to capture complex structural features, enabling complementary extraction of morphological and detailed features. DPBS comprises the efficient dual-channel bottleneck structures (EDC-BS) and the region fusion small-kernel deformable attention mechanism (RF-SKDA). EDC-BS employs a branched convolutional architecture, effectively reducing computational complexity while ensuring accurate recognition of the same organ across varying morphologies. RF-SKDA captures the spatial structural information of different organs by combining regional and global average pooling, and further extracts organ-specific morphological features through the deformable convolutions. The decoder utilizes lightweight parameterization through depthwise separable convolutions and integrates multi-scale features during the decoding process. Experimental results demonstrate that MVPCNet achieves an average Dice Coefficient of 90.59 % and an mIoU of 82.80 % on mediastinal organ dataset. With a parameter size of only 8.21 MB, it outperforms advanced medical segmentation algorithms and classical lightweight semantic segmentation models.