SAM Meibomian gland unified dense segmentation method with introduction of automatic prompt encoder

doi:10.11772/j.issn.1001-9081.2025050613

Journal of Computer Applications ›› 2026, Vol. 46 ›› Issue (5): 1667-1676.DOI: 10.11772/j.issn.1001-9081.2025050613

• Frontier and comprehensive applications • Previous Articles

SAM Meibomian gland unified dense segmentation method with introduction of automatic prompt encoder

Ying JING, Ran LI(), Zhuo JIANG, Ziyang FU, Jingyi DU, Qi LIU, Jihang LIU

School of Information Engineering，Dalian Ocean University，Dalian Liaoning 116023，China

Received:2025-06-13 Revised:2025-09-16 Accepted:2025-09-25 Online:2025-10-17 Published:2026-05-10
Contact: Ran LI
About author:JING Ying， born in 2001， M. S. candidate. Her research interests include deep learning， image processing.
JIANG Zhuo， born in 2000， M.S. candidate. Her research interests include textual semantic comprehension enhancement， structured knowledge fusion， interpretable reasoning.
FU Ziyang， born in 2002， M. S. candidate. Her research interests include deep learning， image hierarchical processing.
DU Jingyi， born in 1999， M. S. candidate. Her research interests include artificial intelligence， fault diagnosis.
LIU Qi， born in 1999， M. S. candidate. His research interests include artificial intelligence， fault diagnosis.
LIU Jihang， born in 2000， M. S. candidate. His research interests include fish abnormal behavior recognition and segmentation based on computer vision.
Supported by:
Key Topics of Major Scientific Challenges and Pharmaceutical Technology Issues in 2022 by Chinese Medicine Education Association(2022KTM036)

引入自动提示编码器的SAM睑板腺统一密集分割方法

荆莹, 李然(), 蒋卓, 付子扬, 杜晶颐, 刘琪, 刘吉航

大连海洋大学信息工程学院，辽宁大连 116023

通讯作者: 李然
作者简介:荆莹（2001—），女，山东临清人，硕士研究生，主要研究方向：深度学习、图像处理
蒋卓（2000—），女，辽宁沈阳人，硕士研究生，主要研究方向：文本语义理解增强、结构化知识融合、可解释推理
付子扬（2002—），女，辽宁沈阳人，硕士研究生，主要研究方向：深度学习、图像分级处理
杜晶颐（1999—），女，辽宁凌源人，硕士研究生，主要研究方向：人工智能、故障诊断
刘琪（1999—），男，江苏扬州人，硕士研究生，主要研究方向：人工智能、故障诊断
刘吉航（2000—），男，辽宁庄河人，硕士研究生，主要研究方向：基于计算机视觉的鱼类异常行为识别与分割。
基金资助:
中国医药教育协会2022重大科学攻关问题和医药技术难题重点课题(2022KTM036)

Abstract

Abstract:

The traditional Segment Anything Model （SAM） relies on manual prompts during segmentation of Meibomian gland images， making it difficult to handle issues such as dense glands， irregular shapes， and blurred boundaries. To address this， an improved model， namely ResSAM， was proposed. ResSAM eliminated the reliance on manual intervention by introducing an automatic prompt encoder. The backbone network was pruned and optimized to further enhance the model's segmentation efficiency. Focal Loss and Smooth IoU Loss were used for training optimization， and the SE （Squeeze-and-Excitation） and cross-attention mechanisms were integrated to reduce the impact of individual differences and blurred boundaries， thereby improving the model's segmentation accuracy. Experimental results on two self-built datasets， Lower Lid and Upper Lid， showed that ResSAM achieved the best performance in terms of the number of parameters and Giga FLoating-point OPerations （GFLOPs）； its segmentation results obtained the highest Dice scores （88.69% and 87.75%， respectively） and the highest Intersection-over-Union （IoU） values （79.69% and 78.58%， respectively）. The research results indicate that the ResSAM optimizes both efficiency and accuracy， supporting early prevention and clinical diagnosis of Meibomian Gland Dysfunction （MGD）.

Key words: Segment Anything Model (SAM), image segmentation, automatic prompt encoder, backbone network pruning, Squeeze-and-Excitation (SE), cross-attention mechanism, Meibomian gland

摘要：

针对传统SAM（Segment Anything Model）在睑板腺图像分割中依赖人工提示，难以应对腺体密集、形态不规则及边界模糊的问题，提出改进模型ResSAM。该模型引入自动提示编码器消除人工干预的依赖；针对骨干网络进行剪枝优化，进一步提升模型分割效率；采用Focal Loss和Smooth IoU Loss优化训练，并融合SE（Squeeze-and-Excitation）与交叉注意力机制降低个体差异和边界模糊的影响，提升模型分割精度。在2个自建数据集Lower Lid和Upper Lid上的实验结果显示，ResSAM的参数量和十亿次浮点运算次数（GFLOPs）指标表现最优；分割结果具有最高Dice值，分别为88.69%和87.75%，以及最高的交并比（IoU）值，分别为79.69%和78.58%。研究结果表明，ResSAM在效率与精度方面均实现了优化，可为睑板腺功能障碍（MGD）的早期预防和临床诊断提供支持。

关键词: SAM, 图像分割, 自动提示编码器, 骨干网络剪枝, SE, 交叉注意力机制, 睑板腺

CLC Number:

TP391

Ying JING, Ran LI, Zhuo JIANG, Ziyang FU, Jingyi DU, Qi LIU, Jihang LIU. SAM Meibomian gland unified dense segmentation method with introduction of automatic prompt encoder[J]. Journal of Computer Applications, 2026, 46(5): 1667-1676.

荆莹, 李然, 蒋卓, 付子扬, 杜晶颐, 刘琪, 刘吉航. 引入自动提示编码器的SAM睑板腺统一密集分割方法[J]. 《计算机应用》唯一官方网站, 2026, 46(5): 1667-1676.

Figures/Tables 16

References 33

[1]	陈远远.基于深度学习的医学图像分割技术研究［D］.开封：河南大学，2024.
	CHEN Y Y. Research on medical image segmentation technology based on deep learning［D］. Kaifeng： Henan University， 2024.
[2]	RONNEBERGER O， FISCHER P， BROX T. U-Net： convolutional networks for biomedical image segmentation［C］// Proceedings of the 2015 Medical Image Computing and Computer-Assisted Intervention， LNCS 9351. Cham： Springer， 2015： 234-241.
[3]	BADRINARAYANAN V， KENDALL A， CIPOLLA R. SegNet： a deep convolutional encoder-decoder architecture for image segmentation［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence， 2017， 39（12）： 2481-2495.
[4]	CHEN L C， PAPANDREOU G， SCHROFF F， et al. Rethinking atrous convolution for semantic image segmentation［EB/OL］. ［2025-09-15］..
[5]	HE K， ZHANG X， REN S， et al. Deep residual learning for image recognition［C］// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2016： 770-778.
[6]	曹玉红，徐海，刘荪傲，等.基于深度学习的医学影像分割研究综述［J］.计算机应用，2021，41（8）：2273-2287.
	CAO Y H， XU H， LIU S A， et al. Review of deep learning-based medical image segmentation［J］. Journal of Computer Applications， 2021， 41（8）： 2273-2287.
[7]	CHHADVA P， GOLDHARDT R， GALOR A. Meibomian gland disease： the role of gland dysfunction in dry eye disease［J］. Ophthalmology， 2017， 124（S11）： S20-S26.
[8]	YAN X， LI J， LIU Z， et al. Quantitative analysis of morphological and functional features in meibography for Meibomian Gland Dysfunction： diagnosis and grading［J］. Eye and Contact Lens， 2022， 48（1）： 32-39.
[9]	LIN J W， LIN L J， LU F， et al. Meibomian glands segmentation in infrared images with limited annotation［J］. International Journal of Ophthalmology， 2024， 17（3）： 401-407.
[10]	GHOSH S， DAS N， DAS I， et al. Understanding deep learning techniques for image segmentation［J］. ACM Computing Surveys， 2020， 52（4）： No.73.
[11]	AMANO S， SHIMAZAKI J， YOKOI N， et al. Meibomian gland dysfunction clinical practice guidelines［J］. Japanese Journal of Ophthalmology， 2023， 67（4）： 448-539.
[12]	MARUOKA S， TABUCHI H， NAGASATO D， et al. Deep neural network-based method for detecting obstructive meibomian gland dysfunction with in vivo laser confocal microscopy［J］. Cornea， 2020， 39（6）： 720-725.
[13]	LAI L， WU Y， FAN J， et al. Automatic meibomian gland segmentation and assessment based on TransUnet with data augmentation［C］// Proceedings of the 2024 International Conference on Intelligent Computing， LNCS 14863. Singapore： Springer， 2024： 154-165.
[14]	SHEN N， WANG Z， LI J， et al. Multi-organ segmentation network for abdominal CT images based on spatial attention and deformable convolution［J］. Expert Systems with Applications， 2023， 211： No.118625.
[15]	MAZUROWSKI M A， DONG H， GU H， et al. Segment anything model for medical image analysis： an experimental study［J］. Medical Image Analysis， 2023， 89： No.102918.
[16]	孙兴，蔡肖红，李明，等.视觉大模型SAM在医学图像分割中的应用综述［J］.计算机工程与应用，2024，60（17）：1-16.
	SUN X， CAI X H， LI M， et al. Review of application of the visual foundation model SAM in medical image segmentation［J］. Computer Engineering and Applications， 2024， 60（17）： 1-16.
[17]	XIE B， TANG H， CAI D， et al. Self-Prompt SAM： medical image segmentation via automatic prompt SAM adaptation［EB/OL］. ［2025-01-15］..
[18]	XIE Z， GUAN B， JIANG W， et al. PA-SAM： prompt adapter SAM for high-quality image segmentation［C］// Proceedings of the 2024 IEEE International Conference on Multimedia and Expo. Piscataway： IEEE， 2024： 1-6.
[19]	DONG G， WANG Z， CHEN Y， et al. An efficient segment anything model for the segmentation of medical images［J］. Scientific Reports， 2024， 14： No.19425.
[20]	CHENG Z， WEI Q， ZHU H， et al. Unleashing the potential of SAM for medical adaptation via hierarchical decoding［C］// Proceedings of the 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2024： 3511-3522.
[21]	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. Piscataway： IEEE， 2017： 2999-3007.
[22]	LEE Y， PARK J. CenterMask： real-time anchor-free instance segmentation［C］// Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2020： 13903-13912.
[23]	戚翔宇，苏庆华，张智超，等. 改进YOLOv11的缺陷检测算法［J］.计算机科学与应用，2025，15（1）：108-116.
	QI X Y， SU Q H， ZHANG Z C， et al. Improvement of the YOLOv11 defect detection algorithm［J］. Computer Science and Application， 2025， 15（1）： 108-116.
[24]	DOSOVITSKIY A， BEYER L， KOLESNIKOV A， et al. An image is worth 16x16 words： Transformers for image recognition at scale［EB/OL］. ［2024-12-15］..
[25]	HUANG S， LIANG H， WANG Q， et al. SEG-SAM： semantic-guided SAM for unified medical image segmentation［EB/OL］. ［2025-08-21］..
[26]	CHEN P， XIE L， HUO X， et al. SAM-CP： marrying SAM with composable prompts for versatile segmentation［EB/OL］. ［2025-03-11］..
[27]	MA J， HE Y， LI F， et al. Segment anything in medical images［J］. Nature Communications， 2024， 15： No.654.
[28]	周涛，刘赟璨，陆惠玲，等.ResNet及其在医学图像处理领域的应用：研究进展与挑战［J］.电子与信息学报，2022，44（1）：149-167.
	ZHOU T， LIU Y C， LU H L， et al. ResNet and its applications to medical image processing： research progress and challenges［J］. Journal of Electronics and Information Technology， 2022， 44（1）： 149-167.
[29]	ZHU K， HU F， DING Y， et al. A comprehensive review of network pruning based on pruning granularity and pruning time perspectives［J］. Neurocomputing， 2025， 626： No.129382.
[30]	PAN P， ZHANG C， SUN J， et al. Multi-scale Conv-Attention U‑Net for medical image segmentation［J］. Scientific Reports， 2025， 15： No.12041.
[31]	CHENG B， MISRA I， SCHWING A G， et al. Masked-attention mask transformer for universal image segmentation［C］// Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2022： 1280-1289.
[32]	ZHAO H， SHI J， QI X， et al. Pyramid scene parsing network［C］// Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway： IEEE， 2017： 6230-6239.
[33]	XIE E， WANG W， YU Z， et al. SegFormer： simple and efficient design for semantic segmentation with Transformers［C］// Proceedings of the 35th International Conference on Neural Information Processing Systems. Red Hook： Curran Associates Inc.， 2021： 12077-12090.

模型	Lower Lid					Upper Lid
模型	Dice/%	IoU/%	Sensitivity/%	Accuracy/%	Time/s	Dice/%	IoU/%	Sensitivity/%	Accuracy/%	Time/s
SAM（10 point）	47.83	31.46	73.32	92.31	20	39.24	24.66	71.29	91.12	19
SAM（20 point）	55.92	38.79	74.66	93.86	24	47.31	30.69	73.61	91.91	24
SAM（30 point）	60.77	43.72	76.72	94.02	25	54.87	37.15	74.78	92.72	26
SAM（40 point）	60.14	48.23	77.83	94.59	31	58.16	40.19	75.92	93.60	29
SAM（50 point）	67.95	51.48	79.25	94.93	38	61.73	43.42	76.45	94.13	34
ResSAM	88.69	79.69	88.75	98.85	8.7	87.75	78.58	88.09	98.63	8.4

模型	Lower Lid					Upper Lid
模型	Dice/%	IoU/%	Sensitivity/%	Accuracy/%	Time/s	Dice/%	IoU/%	Sensitivity/%	Accuracy/%	Time/s
SAM（10 point）	47.83	31.46	73.32	92.31	20	39.24	24.66	71.29	91.12	19
SAM（20 point）	55.92	38.79	74.66	93.86	24	47.31	30.69	73.61	91.91	24
SAM（30 point）	60.77	43.72	76.72	94.02	25	54.87	37.15	74.78	92.72	26
SAM（40 point）	60.14	48.23	77.83	94.59	31	58.16	40.19	75.92	93.60	29
SAM（50 point）	67.95	51.48	79.25	94.93	38	61.73	43.42	76.45	94.13	34
ResSAM	88.69	79.69	88.75	98.85	8.7	87.75	78.58	88.09	98.63	8.4

数据集	SEF-CAM	损失函数	密集提示编码器	Dice	IoU	Precision	Sensitivity	Accuracy
Lower Lid				30.25	17.57	33.66	28.12	95.66
Upper Lid				29.78	16.92	32.42	27.85	95.42
Lower Lid	√			77.52	63.65	82.15	78.34	96.95
Upper Lid	√			76.41	61.62	80.52	77.11	96.52
Lower Lid	√	√		83.35	75.79	88.94	84.79	97.94
Upper Lid	√	√		82.24	74.73	87.71	83.73	97.71
Lower Lid	√	√	√	88.69	79.69	88.71	88.75	98.85
Upper Lid	√	√	√	87.75	78.58	88.05	88.09	98.63

数据集	SEF-CAM	损失函数	密集提示编码器	Dice	IoU	Precision	Sensitivity	Accuracy
Lower Lid				30.25	17.57	33.66	28.12	95.66
Upper Lid				29.78	16.92	32.42	27.85	95.42
Lower Lid	√			77.52	63.65	82.15	78.34	96.95
Upper Lid	√			76.41	61.62	80.52	77.11	96.52
Lower Lid	√	√		83.35	75.79	88.94	84.79	97.94
Upper Lid	√	√		82.24	74.73	87.71	83.73	97.71
Lower Lid	√	√	√	88.69	79.69	88.71	88.75	98.85
Upper Lid	√	√	√	87.75	78.58	88.05	88.09	98.63

骨干网络	Lower Lid					Upper Lid
骨干网络	Params/10⁶	GFLOPs	FPS	Dice/%	IoU/%	Params/10⁶	GFLOPs	FPS	Dice/%	IoU/%
ResNet18	11.73	24.17	92.14	80.13	71.42	11.71	24.19	92.03	79.21	67.08
ResNet34	21.81	44.46	78.29	83.27	75.48	21.79	44.52	78.25	82.13	71.57
ResNet50	25.59	52.08	70.66	85.92	79.95	25.62	52.13	70.64	85.37	74.93
ResNet101	44.48	95.23	52.47	91.97	87.82	44.51	95.18	52.43	91.48	87.91
ResDenNet101-Pruned1	31.12	66.59	66.21	91.60	87.38	31.09	66.61	66.15	91.10	86.50
ResDenNet101-Pruned2	23.71	50.82	75.37	90.83	86.71	23.68	50.79	75.32	90.28	85.85
ResDenNet101-Pruned3	18.31	38.11	85.58	88.69	79.69	18.29	38.09	85.46	87.75	78.58

SAM Meibomian gland unified dense segmentation method with introduction of automatic prompt encoder

引入自动提示编码器的SAM睑板腺统一密集分割方法

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 16

References 33

Related Articles 15

Recommended Articles

Metrics

模型	Dice	IoU	Precision	Sensitivity
U-Net	72.79	60.22	76.50	71.62
DeepLabv3	76.54	64.13	81.02	74.62
Mask2Former	78.62	67.88	77.24	80.54
PSPNet	78.51	69.31	84.11	75.77
SegFormer	83.47	72.79	82.97	84.02
ResSAM	88.69	79.69	88.71	88.75

模型	Dice	IoU	Precision	Sensitivity
U-Net	71.56	58.92	75.25	69.82
DeepLabv3	75.21	62.80	80.19	73.45
Mask2Former	77.47	65.73	76.51	79.31
PSPNet	77.29	67.53	83.24	74.14
SegFormer	82.12	72.51	81.72	83.28
ResSAM	87.75	78.58	88.05	88.09

[1]	Xiang BAI, Juchuan LI, Huimin WANG, Chao JING, Jian NIU, Xingzhong ZHANG, Yongqiang CHENG. Power image retrieval method based on improved Swin Transformer [J]. Journal of Computer Applications, 2026, 46(4): 1334-1343.
[2]	Qianhui XU, Ke NIU, Shunzhe ZHU, Lin SHI, Jun LI. GAB3D-SEVSN： enhanced video steganography model via invertible neural network [J]. Journal of Computer Applications, 2026, 46(2): 467-474.
[3]	Yanan LI, Mengyang GUO, Guojun DENG, Yunfeng CHEN, Jianji REN, Yongliang YUAN. Method for life prediction of parallel branching engine based on multi-modal fusion features [J]. Journal of Computer Applications, 2026, 46(1): 305-313.
[4]	Ning CAO, Xin WEN, Yanrong HAO, Rui CAO. Lightweight motor imagery electroencephalogram decoding neural network with multi-domain feature fusion [J]. Journal of Computer Applications, 2026, 46(1): 289-296.
[5]	Zhixiong XU, Bo LI, Xiaoyong BIAN, Qiren HU. Adversarial sample embedded attention U-Net for 3D medical image segmentation [J]. Journal of Computer Applications, 2025, 45(9): 3011-3016.
[6]	Fang WANG, Jing HU, Rui ZHANG, Wenting FAN. Medical image segmentation network with content-guided multi-angle feature fusion [J]. Journal of Computer Applications, 2025, 45(9): 3017-3025.
[7]	Yihan WANG, Chong LU, Zhongyuan CHEN. Multimodal sentiment analysis model with cross-modal text information enhancement [J]. Journal of Computer Applications, 2025, 45(7): 2237-2244.
[8]	Kunyuan JIANG, Xiaoxia LI, Li WANG, Yaodan CAO, Xiaoqiang ZHANG, Nan DING, Yingyue ZHOU. Boundary-cross supervised semantic segmentation network with decoupled residual self-attention [J]. Journal of Computer Applications, 2025, 45(4): 1120-1129.
[9]	Xinyao LINGHU, Yan CHEN, Pengcheng ZHANG, Yi LIU, Zhiguo GUI, Wei ZHAO, Zhanhao DONG. Cervical cell nucleus image segmentation based on multi-scale guided filtering [J]. Journal of Computer Applications, 2025, 45(4): 1333-1339.
[10]	Baohua YUAN, Jialu CHEN, Huan WANG. Medical image segmentation network integrating multi-scale semantics and parallel double-branch [J]. Journal of Computer Applications, 2025, 45(3): 988-995.
[11]	Zhanjun JIANG, Yang LI, Jing LIAN, Xinfa MIAO. Coordinate enhancement and multi-source sampling for brain tumor image segmentation [J]. Journal of Computer Applications, 2025, 45(3): 996-1002.
[12]	Ming DENG, Jinfan XU, Hongxiang XIAO, Xiaolan XIE. Medical image segmentation network based on improved TransUNet with efficient channel attention [J]. Journal of Computer Applications, 2025, 45(12): 4037-4044.
[13]	Na LIU, Jun FENG, Yiru HUO, Hongyang WANG, Liu YANG. SAMCP： lightweight fine-tuned SAM method for colon polyp segmentation [J]. Journal of Computer Applications, 2025, 45(10): 3390-3398.
[14]	Cong GU, Qiqiang DUAN, Siyu REN. Polyp segmentation algorithm based on context-aware network [J]. Journal of Computer Applications, 2024, 44(11): 3617-3622.
[15]	Li’an CHEN, Yi GUO. Text sentiment analysis model based on individual bias information [J]. Journal of Computer Applications, 2024, 44(1): 145-151.