Intravascular Ultrasound Image Segmentation Fusing Transformer Branch and Topology Enforcement

Wenyue Hao; Huaiyu Cai; Tingtao Zuo; Zhongwei Jia; Yi Wang; Xiaodong Chen

doi:10.3788/LOP231918

Laser & Optoelectronics Progress, Volume. 61, Issue 12, 1237008(2024)

Intravascular Ultrasound Image Segmentation Fusing Transformer Branch and Topology Enforcement

Wenyue Hao¹, Huaiyu Cai^1、*, Tingtao Zuo², Zhongwei Jia³, Yi Wang¹, and Xiaodong Chen¹

¹Ministry of Education Key Laboratory of Optoelectronic Information Technology, School of Precision Instrument and Optoelectronic Engineering, Tianjin University, Tianjin 300072, China

²Lepu Medical Technology (Beijing) Co., Ltd., Beijing 102200, China

³Southwestern Lu Hospital, Liaocheng 252325, Shandong, China

show less

Abstract Get PDF(in Chinese)

References(30)

[1] Zhu F B, Gao Z Y, Zhao C et al. A deep learning-based method to extract lumen and media-adventitia in intravascular ultrasound images[J]. Ultrasonic Imaging, 44, 191-203(2022).

[2] Xia M H, Yan W J, Huang Y et al. Extracting membrane borders in IVUS images using a multi-scale feature aggregated U-net[C], 1650-1653(2020).

[3] Xia M H, Yang H B, Huang Y et al. 3D pyramidal densely connected network with cross-frame uncertainty guidance for intravascular ultrasound sequence segmentation[J]. Physics in Medicine & Biology, 68, 055001(2023).

[4] Yang J, Tong L, Faraji M, Basu A, Berretti S et al. IVUS-Net: an intravascular ultrasound segmentation network[M]. Smart multimedia. Lecture notes in computer science, 11010, 367-377(2018).

[5] Yang J, Faraji M, Basu A. Robust segmentation of arterial walls in intravascular ultrasound images using Dual Path U-Net[J]. Ultrasonics, 96, 24-33(2019).

[6] Liu J Y, Hao W Y, Jia Z W et al. An intravascular ultrasound segmentation network based on multi-task learning[J]. Proceedings of SPIE, 12277, 122770D(2022).

[7] Yuan S F, Yang F. CSDN: combing shallow and deep networks for accurate real-time segmentation of high-definition intravascular ultrasound images[EB/OL]. https://arxiv.org/abs/2301.13648

[8] Kim S, Jang Y, Jeon B, Stoyanov D, Taylor Z, Balocco S et al. Fully automatic segmentation of coronary arteries based on deep neural network in intravascular ultrasound images[M]. Intravascular imaging and computer assisted stenting and large-scale annotation of biomedical data and expert label synthesis. Lecture notes in computer science, 11043, 161-168(2018).

[9] Blanco P J, Ziemer P G P, Bulant C A et al. Fully automated lumen and vessel contour segmentation in intravascular ultrasound datasets[J]. Medical Image Analysis, 75, 102262(2022).

[10] Bargsten L, Raschka S, Schlaefer A. Capsule networks for segmentation of small intravascular ultrasound image datasets[J]. International Journal of Computer Assisted Radiology and Surgery, 16, 1243-1254(2021).

[11] Zhang W Z, Cao Y K, Hu X F et al. IAT: a full-scale IVUS analysis toolbox based on deep learning algorithms for clinical diagnosis[J]. IEEE Transactions on Instrumentation and Measurement, 72, 2505315(2023).

[12] Cao H, Wang Y Y, Chen J, Karlinsky L, Michaeli T, Nishino K et al. Swin-UNet: unet-like pure transformer for medical image segmentation[M]. Computer vision-ECCV 2022 workshops. Lecture notes in computer science, 13803, 205-218(2023).

[13] Azad R, Heidari M, Shariatnia M, Rekik I, Adeli E, Park S H et al. TransDeepLab: convolution-free transformer-based DeepLabv3+ for medical image segmentation[M]. Predictive intelligence in medicine. Lecture notes in computer science, 13564, 91-102(2022).

[14] Xie Y T, Zhang J P, Shen C H, de Bruijne M, Cattin P C, Cotin S et al. CoTr: efficiently bridging CNN and transformer for 3D medical image segmentation[M]. Medical image computing and computer assisted intervention-MICCAI 2021. Lecture notes in computer science, 12903, 171-180(2021).

[15] Dosovitskiy A, Beyer L, Kolesnikov A et al. An image is worth[EB/OL], 16-16. https://arxiv.org/abs/2010.11929

[16] Wang W H, Xie E Z, Li X et al. Pyramid vision transformer: a versatile backbone for dense prediction without convolutions[C], 548-558(2022).

[17] Zheng S X, Lu J C, Zhao H S et al. Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers[C], 6877-6886(2021).

[18] Zhang Y D, Liu H Y, Hu Q, de Bruijne M, Cattin P C, Cotin S et al. TransFuse: fusing transformers and CNNs for medical image segmentation[M]. Medical image computing and computer assisted intervention-MICCAI 2021. Lecture notes in computer science, 12901, 14-24(2021).

[19] Chen J N, Lu Y Y, Yu Q H et al. TransUNet: transformers make strong encoders for medical image segmentation[EB/OL]. https://arxiv.org/abs/2102.04306

[20] Liu J Y, Cai H Y, Hao W Y et al. IVUS image segmentation based on polar coordinate modeling and neural network[J]. Opto-Electronic Engineering, 50, 220118(2023).

[21] Wyburd M K, Dinsdale N K, Namburete A I L, de Bruijne M, Cattin P C, Cotin S et al. TEDS-Net: enforcing diffeomorphisms in spatial transformers to guarantee topology preservation in segmentations[M]. Medical image computing and computer assisted intervention-MICCAI 2021. Lecture notes in computer science, 12901, 250-260(2021).

[22] Tomasi C, Manduchi R. Bilateral filtering for gray and color images[C], 839-846(2002).

[23] Huang H M, Xie S A, Lin L F et al. ScaleFormer: revisiting the transformer-based backbones from a scale-wise perspective for medical image segmentation[C], 964-971(2022).

[24] Wang H Y, Xie S A, Lin L F et al. Mixed transformer U-Net for medical image segmentation[C], 2390-2394(2022).

[25] Hatamizadeh A, Tang Y C, Nath V et al. UNETR: transformers for 3D medical image segmentation[C], 1748-1758(2022).

[26] Wang H Y, Zhu Y K, Green B, Vedaldi A, Bischof H, Brox T et al. Axial-DeepLab: stand-alone axial-attention for panoptic segmentation[M]. Computer vision-ECCV 2020. Lecture notes in computer science, 12349, 108-126(2020).

[27] Valanarasu J M J, Oza P, Hacihaliloglu I, de Bruijne M, Cattin P C, Cotin S et al. Medical transformer: gated axial-attention for medical image segmentation[M]. Medical image computing and computer assisted intervention-MICCAI 2021. Lecture notes in computer science, 12901, 36-46(2021).

[28] Huang X H, Deng Z F, Li D D et al. MISSFormer: an effective transformer for 2D medical image segmentation[J]. IEEE Transactions on Medical Imaging, 42, 1484-1494(2023).

[29] Balocco S, Gatta C, Ciompi F et al. Standardized evaluation methodology and reference database for evaluating IVUS image segmentation[J]. Computerized Medical Imaging and Graphics, 38, 70-90(2014).

[30] Chen L C, Zhu Y K, Papandreou G, Ferrari V, Hebert M, Sminchisescu C et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[M]. Computer vision-ECCV 2018. Lecture notes in computer science, 833-851(2018).

Tools

Get Citation

Copy Citation Text

Wenyue Hao, Huaiyu Cai, Tingtao Zuo, Zhongwei Jia, Yi Wang, Xiaodong Chen. Intravascular Ultrasound Image Segmentation Fusing Transformer Branch and Topology Enforcement[J]. Laser & Optoelectronics Progress, 2024, 61(12): 1237008

Download Citation

EndNote(RIS)BibTex Plain Text

Set citation alerts for article

Save article for my favorites

Paper Information

Category: Digital Image Processing

Received: Aug. 15, 2023

Accepted: Sep. 18, 2023

Published Online: Jun. 17, 2024

The Author Email: Huaiyu Cai (hycai@tju.edu.cn)

DOI:10.3788/LOP231918

CSTR:32186.14.LOP231918

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology