Pancreatic Image Segmentation Approach Based on Improved SegFormer

doi:10.3969/j.issn.1006-2475.2025.06.012

Abstract

Abstract: Abstract: To address the issue of low segmentation accuracy in CT images due to the small volume of the pancreas and significant individual differences in its position and shape， we propose an improved SegFormer-based method for pancreatic image segmentation. Prior to model training， we construct candidate regions based on the distribution of the pancreas and perform cropping to effectively reduce background interference and lower the input image resolution. Next， we employ the SegFormer network and introduce an encoding resolution enhancement strategy by adjusting the downsampling ratio to increase the size of the encoder's output feature maps， which retains more detail information to better handle morphological variations of the pancreas. We then incorporate residual polarized self-attention modules to compute channel and spatial attention on the encoded features， highlighting key characteristics of the pancreatic region while suppressing the activation of irrelevant features， thus improving the model's segmentation accuracy. The proposed method achieved an average DSC of 85.5% on the NIH dataset， with a parameter count of 3.91 M and a computational load of 6.89 G FLOPs， indicating its effectiveness in the pancreatic segmentation task and its potential for clinical applications.

Key words: Key words: pancreas segmentation, SegFormer, polarized self-attention, lightweight, CT

CLC Number:

TP391

LIANG Panru, XIN Guojiang, DING Changsong. Pancreatic Image Segmentation Approach Based on Improved SegFormer[J]. Computer and Modernization, 2025, 0(06): 71-78.

References

［1］ SIEGEL R L， MILLER K D， JEMAL A. Cancer statistics， 2019［J］. CA: A Cancer Journal for Clinicians， 2019，69（1）:7-34.
［2］ SUNG H， FERLAY J， SIEGEL R L， et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries［J］. CA: A Cancer Journal for Clinicians， 2021，71（3）:209-249.
［3］ LIU S Q， LIANG S J， HUANG X， et al. Graph-enhanced U-Net for semi-supervised segmentation of pancreas from abdomen CT scan［J］. Physics in Medicine & Biology， 2022，67（15）. DOI: 10.1088/1361-6560/ac80e4.
［4］ CHU L C， GOGGINS M G， FISHMAN E K. Diagnosis and detection of pancreatic cancer［J］. The Cancer Journal， 2017，23（6）:333-342.
［5］ KARASAWA K， ODA M， KITASAKA T， et al. Multi-atlas pancreas segmentation: Atlas selection based on vessel structure［J］. Medical Image Analysis， 2017，39:18-28.
［6］ TAM T D， BINH N T. Efficient pancreas segmentation in computed tomography based on region-growing［C］// Nature of Computation and Communication: International Conference. Springer， 2015:332-340.
［7］ FARAG A， LU L， TURKBEY B， et al. A bottom-up approach for automatic pancreas segmentation in abdominal CT scans［C］// Abdominal Imaging. Computational and Clinical Applications: 6th International Workshop. ABDI 2014， Held in Conjunction with MICCAI 2014， Springer， 2014: 103-113.
［8］曹路洋，李建微. 面向深度学习的胰腺医学图像分割方法研究进展［J］.小型微型计算机系统， 2022，43（12）:2591-2604.
［9］ ZHOU Y Y， XIE L X， SHEN W， et al. A fixed-point model for pancreas segmentation in abdominal CT scans［C］// International Conference on Medical Image Computing and Computer Assisted Intervention. Springer， 2017:693-701.
［10］ XIE L X， YU Q H， ZHOU Y Y， et al. Recurrent saliency transformation network for tiny target segmentation in abdominal CT scans［J］.IEEE Transactions on Medical Imaging， 2019，39（2）:514-525
［11］ OKTAY O， SCHLEMPER J， FOLGOC L L， et al. Attention u-net: Learning where to look for the pancreas［J］. arXiv preprint arXiv:1804.03999， 2018.
［12］ RONNEBERGER O， FISCHER P， BROX T. U-Net: Convolutional networks for biomedical image segmentation［C］//Medical Image Computing and Computer-Assisted Intervention 18th International Conference. Springer， 2015:234-241.
［13］ LI M Y， LIAN F H， GUO S X. Pancreas segmentation based on an adversarial model under two-tier constraints［J］.Physics in Medicine & Biology，2020，65（22）. DOI: 10.1088/1361-6560/abb6bf.
［14］ LI M， LIAN F， WANG C， et al. Accurate pancreas segmentation using multi-level pyramidal pooling residual U-Net with adversarial mechanism［J］.BMC Medical Imaging，2021，21（1）:1-8.
［15］毕秀丽，陆猛，肖斌，等. 基于双解码U型卷积神经网络的胰腺分割［J］. 软件学报， 2022，33（5）:1947-1958.
［16］张国栋，唐晓艺，鞠蓉晖，等.融合双注意力机制的多尺度胰腺分割方法［J］. 计算机工程与设计， 2024，45（4）:1189-1194.
［17］ DOSOVITSKIY A， BEYER L， KOLESNIKOV A， et al. An image is worth 16x16 words: Transformers for image recognition at scale［J］.arXiv preprint arXiv:2010.11929，2020.
［18］ VASWANI A， SHAZEER N， PARMAR N， et al. Attention is all you need［J］. avXiv preprint avXiv: 1706.03762， 42017.
［19］ CHEN J， LU Y Y， YU Q H， et al. Transunet: Transformers make strong encoders for medical image segmentation［J］.arXiv preprint arXiv:2102.04306， 2021.
［20］ XIE E Z，WANG W H，YU Z D，et al. SegFormer: Simple and efficient design for semantic segmentation with transformers［J］. Advances in Neural Information Processing Systems， 2021，34:12077-12090.
［21］ LIU H J， LIU F Y， FAN X Q， et al. Polarized self-attention: Towards high-quality pixel-wise regression［J］. arxiv preprint arxiv:2107.00782， 2021.
［22］ CHEN H P， LIU Y J， SHI Z A， et al. Pancreas segmentation by two-view feature learning and multi-scale supervision［J］.Biomedical Signal Processing and Control，2022， 74. DOI: 10.1016/j.bspc.2022.103519.
［23］朱纷，何立风，孙爽，等.基于形变残差和级联编码的胰腺分割模型［J］. 计算机与现代化， 2024，（6）:83-88.
［24］ LIU Z， SU J， WANG R H， et al. Pancreas co-segmentation based on dynamic ROI extraction and VGGU-Net［J］. Expert Systems with Applications， 2022，192. DOI: 10.1016/j.eswa.2021.116444.
［25］ ROTH H R， LU L， FARAG A， et al. Deeporgan: Multi-level deep convolutional networks for automated pancreas segmentation［C］// Medical Image Computing and Computer-Assisted Intervention 18th International Conference， Springer， 2015:
556-564.
［26］ LIN T Y， GOYAL P， GIRSHICK R， et al. Focal loss for dense object detection［C］// Proceedings of the 2017 IEEE International Conference on Computer Vision，2017:2980-2988.
［27］ ZHAO H S， SHI J P， QI X J， et al. Pyramid scene parsing network［C］// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2017: 2881-2890.
［28］ CHEN L C， ZHU Y K， PAPANDREOU G， et al. Encoder-decoder with atrous separable convolution for semantic image segmentation［C］// Proceedings of the European Conference on Computer Vision （ECCV）. 2018:801-818.
［29］ SUN K， XIAO B， LIU D， et al. Deep high-resolution representation learning for human pose estimation［C］// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019:5693-5703.

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