• This paper is the first to propose a method for organ segmentation in mediastinal region on CT images. • This paper proposes multi-view parallel convolution module to capture the organ’s unique and complex morphological characteristics. • This paper introduces a region fusion small-kernel deformable attention mechanism to address the issue of spatial information and detailed deformation feature loss in medical image segmentation. • This paper design efficient dual-channel bottleneck structures to shared parameters and optimize the computation path. 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.

Immunohistochemistry (IHC) staining is crucial for determining tumor subtypes, obtaining protein expression information, and developing personalized treatment plans. But compared with hematoxylin and eosin (H&E) staining, IHC staining is more complex and expensive. With the advancement of deep learning, converting H&E stained images into IHC stained images has gradually emerged as a solution for obtaining IHC staining. However, current virtual staining processes suffer from difficulties in aligning pathological semantic features, posing significant challenges for network training, which poses significant challenges for network training. To solve these issues, we propose a multi-view multi-level pathology semantic perception learning method for H&E-to-IHC virtual staining (M2PL-GAN). Unlike prior approaches, M2PL-GAN introduces a comprehensive semantic learning paradigm from three views: structural contextual relations, feature distribution, and topology-aware fine-grained semantics. These correspond to the Context-aware Correlation Mechanism (CACM), the Local-aware Distribution Alignment Mechanism (LDAM), and the Graph- aware Bidirectional Contrastive Learning Mechanism (GBCLM) respectively. Among them, CACM enhances contextual consistency by establishing semantic correlations between virtual and real IHC images at local scales. LDAM ensures alignment of semantic feature distributions between virtual and real IHC images, mitigating semantic shifts caused by HE-IHC staining. GBCLM leverages graph neural network to capture topology-aware semantic representations and optimizes semantic feature alignment through bidirectional contrastive learning. Extensive experiments on both public and private datasets demonstrate that our method outperforms state-of-the-art approaches in both quantitative metrics and qualitative evaluations. Our code is available in https://github.com/Pikachu-one/M2PL-GAN.

Mask is considered as an important prior for fusion, which could selectively enhance specific regions to generate ideal fused images. However, masks used in the existing methods exhibit limitations in the precise representation of targets, and more importantly, these masks are generated from a single modality, which restricts the effective integration of multi-modal information. To address this issue, we propose a competitive mask-guidance fusion method for infrared and visible images. A multi-modal semantic-sensitive mask selection network is proposed to generate complementary-mask maps, which organically integrate advantageous target regions of different modalities by competitively comparing the qualities of masks. In this network, a pseudosiamese architecture is designed to obtain respective target masks, and specifically, a spatial-aligned-based feature aggregation module is devised to produce high-quality pseudo-labels which are served as references for the generation of the complementary-mask maps. Furthermore, we propose a bidirectional-collaboration region fusion strategy, which enhances the expression of advantageous target regions from each modality inforeground while suppressing the contribution of corresponding regions from the other modality in background. Compared to methods on public datasets, the results show that our method significantly enhances the description of semantic-sensitive targets in fused images, including the saliency and the integrity of structural information. Code are available athttps://github.com/xbsj-cool/MSCRFusion.