Hybrid Transformer-CNN Framework for Multi-Organ Tumor Segmentation in Abdominal CT Scans

Dandagula Jagadeesh; Chitla Vignesh; Vaddelli Srinivas Rao

doi:10.64388/IREV9I12-1718946

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Hybrid Transformer-CNN Framework for Multi-Organ Tumor Segmentation in Abdominal CT Scans

Dandagula Jagadeesh Chitla Vignesh Vaddelli Srinivas Rao

Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence, Medical Image Processing

DOI: 10.64388/IREV9I12-1718946

Abstract

Accurate segmentation of multiple abdominal organs and co-occurring tumors in CT imaging presents a significant challenge due to high inter-patient anatomical variability, low contrast boundaries, and class imbalance. This paper proposes HybridSegNet, a novel architecture that integrates a Swin Transformer encoder with a multi-scale convolutional decoder equipped with dense skip connections and a dual-branch feature fusion module. HybridSegNet is evaluated on the publicly available CHAOS dataset (liver, kidney segmentation) and the KiTS23 challenge dataset (kidney tumor segmentation). The model achieves a mean Dice Similarity Coefficient (DSC) of 0.913 on liver segmentation, 0.897 on kidney segmentation, and 0.884 on kidney tumor segmentation, outperforming leading methods including nnU-Net (DSC: 0.876) and Swin-UNet (DSC: 0.861). A lightweight model variant is also proposed for deployment on resource-constrained clinical workstations without significant performance degradation. Results demonstrate HybridSegNet's suitability for real-time clinical decision support in abdominal oncology.

Keywords

Abdominal CT, Deep Learning, Feature Fusion, Medical Image Segmentation, Multi-Organ Segmentation, Swin Transformer, Tumor Detection

References

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[2] O. Ronneberger, P. Fischer, and T. Brox, "U-Net: Convolutional Networks for Biomedical Image Segmentation," in Proc. MICCAI, 2015, pp. 234-241.

[3] F. Isensee, P. F. Jaeger, S. A. A. Kohl, J. Petersen, and K. H. Maier-Hein, "nnU-Net: A Self-Configuring Method for Deep Learning-Based Biomedical Image Segmentation," Nature Methods, vol. 18, pp. 203-211, 2021.

[4] J. Chen et al., "TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation," arXiv preprint arXiv:2102.04306, 2021.

[5] H. Cao et al., "Swin-UNet: Unet-Like Pure Transformer for Medical Image Segmentation," in Proc. ECCV Workshops, 2022.

[6] A. Hatamizadeh et al., "UNETR: Transformers for 3D Medical Image Segmentation," in Proc. WACV, 2022, pp. 574-584.

[7] Z. Liu et al., "Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows," in Proc. ICCV, 2021, pp. 10012-10022.

[8] A. E. Kavur et al., "CHAOS Challenge - Combined (CT-MR) Healthy Abdominal Organ Segmentation," Medical Image Analysis, vol. 69, p. 101950, 2021.

[9] N. Heller et al., "The KiTS23 Challenge Dataset: 500 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes," arXiv preprint arXiv:2307.01984, 2023.

How to cite this paper

Dandagula Jagadeesh, Chitla Vignesh, Vaddelli Srinivas Rao "Hybrid Transformer-CNN Framework for Multi-Organ Tumor Segmentation in Abdominal CT Scans" Iconic Research And Engineering Journals Volume 9 Issue 12 2026 Page 2016-2019 https://doi.org/10.64388/IREV9I12-1718946

Dandagula Jagadeesh, Chitla Vignesh, Vaddelli Srinivas Rao "Hybrid Transformer-CNN Framework for Multi-Organ Tumor Segmentation in Abdominal CT Scans" Iconic Research And Engineering Journals, vol. 9, no. 12, Jun. 2026, doi: https://doi.org/10.64388/IREV9I12-1718946

Dandagula Jagadeesh, Chitla Vignesh, Vaddelli Srinivas Rao (2026). Hybrid Transformer-CNN Framework for Multi-Organ Tumor Segmentation in Abdominal CT Scans. Iconic Research And Engineering Journals, 9(12). doi: https://doi.org/10.64388/IREV9I12-1718946

Dandagula Jagadeesh, Chitla Vignesh, Vaddelli Srinivas Rao "Hybrid Transformer-CNN Framework for Multi-Organ Tumor Segmentation in Abdominal CT Scans" Iconic Research And Engineering Journals, vol. 9, no. 12, Jun. 2026. Crossref, https://doi.org/10.64388/IREV9I12-1718946

@article{1718946,
      author = {Dandagula Jagadeesh, Chitla Vignesh, Vaddelli Srinivas Rao},
      title = {Hybrid Transformer-CNN Framework for Multi-Organ Tumor Segmentation in Abdominal CT Scans},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {12},
      pages = {2016-2019},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1718946.pdf},
      abstract = {Accurate segmentation of multiple abdominal organs and co-occurring tumors in CT imaging presents a significant challenge due to high inter-patient anatomical variability, low contrast boundaries, and class imbalance. This paper proposes HybridSegNet, a novel architecture that integrates a Swin Transformer encoder with a multi-scale convolutional decoder equipped with dense skip connections and a dual-branch feature fusion module. HybridSegNet is evaluated on the publicly available CHAOS dataset (liver, kidney segmentation) and the KiTS23 challenge dataset (kidney tumor segmentation). The model achieves a mean Dice Similarity Coefficient (DSC) of 0.913 on liver segmentation, 0.897 on kidney segmentation, and 0.884 on kidney tumor segmentation, outperforming leading methods including nnU-Net (DSC: 0.876) and Swin-UNet (DSC: 0.861). A lightweight model variant is also proposed for deployment on resource-constrained clinical workstations without significant performance degradation. Results demonstrate HybridSegNet's suitability for real-time clinical decision support in abdominal oncology.},
      keywords = {Abdominal CT, Deep Learning, Feature Fusion, Medical Image Segmentation, Multi-Organ Segmentation, Swin Transformer, Tumor Detection},
      month = {June},
      doi = {https://doi.org/10.64388/IREV9I12-1718946}
  }