LiT-HiSegFormer-Net: A hybrid SegFormer network with Bi-level context fusion for liver and tumor segmentation in CT images
Year: 2026
Type: article
Abstract: Precise segmentation of the liver and associated tumors is vital for diagnosing and treatment planning of hepatocellular carcinoma, particularly in radiation therapy. Although U-Net is a CNN-based architecture that has accomplished remarkable achievement in biomedical image segmentation, its dependence on convolutional operations limits its capacity to extract long-range contextual information and spatial dependencies in CT images with low tissue contrast and blurred organ boundaries. Transformer-based models address this issue by modeling global context but often fail to extract low-level features essential for precise localization. Since both global and local level features are crucial for accurate segmentation in complex anatomical scenarios, we propose LiT-HiSegFormer-Net, a U-shaped encoder–decoder architecture that integrates the advantages of CNNs and Transformers. A CNN and Swin Transformer are used in the encoder to extract required features, which are fused using a cross-attention-based fusion module in the skip connections. Experiments conducted on the LiTS-ISBI and 3D-IRCADb datasets indicate the efficacy of the presented method, achieving enhanced performance in liver organ and tumor segmentation compared to existing approaches. • Hybrid U-shaped architecture that integrates CNN-based local feature extraction with Swin Transformer-based global context modeling for liver and tumor segmentation. • An approach that fuses the global context of transformers with local features of CNNs using bidirectional cross-attention formulation. • Skip connection embedding of the bidirectional cross-attention module to strengthen the detection of small tumors.
Source: Intelligence-Based Medicine
Authors: Sushma B., Rangeet Mitra
Institutions: Amrita Vishwa Vidyapeetham, CMR University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,420
OpenAlex ID: W7140226552