Vessel segmentation is crucial for assisting in the diagnosis and treatment of a range of diseases, such as retinal diseases and coronary artery diseases. However, current vessel segmentation methods often face the problem of poor vessel boundary segmentation, making the vessel segmentation task still challenging. This study aims to alleviate this problem, thereby improving the vessel segmentation results. By introducing a boundary detection auxiliary task to the vessel segmentation main task, we propose a boundary perception enhancement network (BPENet) for retinal vessel and coronary angiogram segmentation. Among them, to enhance vessel feature extraction capability, BPENet introduces the feature extraction enhancement (FE2) module. To enhance vessel boundary perception capability, BPENet introduces the boundary enhancement (BE) module. In addition, to fully leverage feature information from different layers, BPENet introduces the deep feature aggregation (DFA) module. Experimental results on retinal vessel datasets (DRIVE, CHASE-DB1, and STARE) and coronary angiogram datasets (DCA1 and XCAD) show that BPENet outperforms the existing mainstream segmentation methods. BPENet is expected to provide a reliable vessel segmentation result for doctors during the diagnosis and treatment of related diseases.
CamasãoDMantovaniD. The mechanical characterization of blood vessels and their substitutes in the continuous quest for physiological-relevant performances. a critical review. Materials Today Bio2021; 10: 100106.
2.
FrazMRemagninoPHoppeA, et al.Blood vessel segmentation methodologies in retinal images – a survey. Comput Methods Programs Biomed2012; 108: 407–433.
3.
KerkeniABenabdallahAManzaneraA, et al.A coronary artery segmentation method based on multiscale analysis and region growing. Comput Med Imaging Graph2016; 48: 49–61.
4.
WanTFengHTongC, et al.Automated identification and grading of coronary artery stenoses with x-ray angiography. Comput Methods Programs Biomed2018; 167: 13–22.
5.
GuoXChenCLuY, et al.Retinal vessel segmentation combined with generative adversarial networks and dense unet. IEEE Access2020; 8: 194551–194560.
6.
JunTJKweonJKimYH, et al.T-net: nested encoder–decoder architecture for the main vessel segmentation in coronary angiography. Neural Netw2020; 128: 216–233.
7.
BakkouriIAfdelK. Multi-scale CNN based on region proposals for efficient breast abnormality recognition. Multimed Tools Appl2019; 78: 12939–12960.
8.
BakkouriIAfdelK. Computer-aided diagnosis (cad) system based on multi-layer feature fusion network for skin lesion recognition in dermoscopy images. Multimed Tools Appl2020; 79: 20483–20518.
9.
SahliHBen SlamaALabidiS. U-net: a valuable encoder-decoder architecture for liver tumors segmentation in ct images. J Xray Sci Technol2022; 30: 45–56.
10.
BakkouriIAfdelKBenois-PineauJ, et al.Bg-3dm2f: bidirectional gated 3d multi-scale feature fusion for Alzheimer’s disease diagnosis. Multimed Tools Appl2022; 81: 10743–10776.
11.
BakkouriIAfdelK. Mlca2f: multi-level context attentional feature fusion for COVID-19 lesion segmentation from CT scans. Signal Image Video Process2023; 17: 1181–1188.
12.
LongJShelhamerEDarrellT. Fully convolutional networks for semantic segmentation. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR), pp.3431–3440.
13.
RonnebergerOFischerPBroxT. U-net: convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells WM, et al. (eds) Medical image computing and computer-assisted intervention – MICCAI 2015. Cham: Springer International Publishing, 2015, pp.234–241. ISBN 978-3-319-24574-4.
14.
LaibacherTWeydeTJalaliS. M2u-net: effective and efficient retinal vessel segmentation for real-world applications. In: 2019 IEEE/CVF conference on computer vision and pattern recognition workshops (CVPRW), pp.115–124.
15.
YangQMaBCuiH, et al.Amf-net: attention-aware multi-scale fusion network for retinal vessel segmentation. In: 2021 43rd Annual international conference of the IEEE engineering in medicine & biology society (EMBC), pp.3277–3280.
16.
MaWYuSMaK, et al.Multi-task neural networks with spatial activation for retinal vessel segmentation and artery/vein classification. In: Shen D, Liu T and Peters TM, et al. (eds) Medical image computing and computer assisted intervention – MICCA 2019. Cham: Springer International Publishing, 2019, pp.769–778. ISBN 978-3-030-32239-7.
17.
ZhangHZhangDGaoZ, et al.Joint segmentation and quantification of main coronary vessels using dual-branch multi-scale attention network. In: de Bruijne M, Cattin PC and Cotin S, et al. (eds) Medical image computing and computer assisted intervention – MICCAI 2021. Cham: Springer International Publishing, 2021, pp.369–378. ISBN 978-3-030-87193-2.
18.
XuHZhuPLuoX, et al.Extracting buildings from remote sensing images using a multitask encoder-decoder network with boundary refinement. Remote Sens (Basel)2022; 14: 564.
19.
LiuJXiaYFengJ, et al.A novel building extraction network via multi-scale foreground modeling and gated boundary refinement. Remote Sens (Basel)2023; 15: 5638.
20.
WangBQiuSHeH. Dual encoding u-net for retinal vessel segmentation. In: Shen D, Liu T, Peters TM, et al. (eds) Medical image computing and computer assisted intervention – MICCAI 2019. Cham: Springer International Publishing, 2019, pp.84–92. ISBN 978-3-030-32239-7.
21.
LiYGuS. Retinal vascular segmentation algorithm based on atrous attention u-net. In: 2022 International conference on image processing, computer vision and machine learning (ICICML), pp.517–521.
22.
WangZZouYLiuPX. Hybrid dilation and attention residual u-net for medical image segmentation. Comput Biol Med2021; 134: 104449.
23.
FuZLiJHuaZ, et al.Deep supervision feature refinement attention network for medical image segmentation. Eng Appl Artif Intell2023; 125: 106666.
24.
StaalJAbramoffMNiemeijerM, et al.Ridge-based vessel segmentation in color images of the retina. IEEE Trans Med Imaging2004; 23: 501–509.
25.
OwenCGRudnickaARMullenR, et al.Measuring retinal vessel tortuosity in 10-year-old children: validation of the computer-assisted image analysis of the retina (CAIAR) Program.Investigative Ophthalmology & Visual Science2009; 50: 2004–2010.
26.
HooverAKouznetsovaVGoldbaumM. Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans Med Imaging2000; 19: 203–210.
27.
Cervantes-SanchezFCruz-AcevesIHernandez-AguirreA, et al.Automatic segmentation of coronary arteries in x-ray angiograms using multiscale analysis and artificial neural networks. Appl Sci2019; 9: 5507.
28.
MaYHuaYDengH, et al.Self-supervised vessel segmentation via adversarial learning. In: 2021 IEEE/CVF international conference on computer vision (ICCV), pp.7516–7525.
29.
WangQWuBZhuP, et al.Eca-net: efficient channel attention for deep convolutional neural networks. In: 2020 IEEE/CVF conference on computer vision and pattern recognition (CVPR), pp.11531–11539.
30.
ZhouZRahman SiddiqueeMMTajbakhshN, et al.Unet++: a nested u-net architecture for medical image segmentation. In: Stoyanov D, Taylor Z, Carneiro G, et al. (eds) Deep learning in medical image analysis and multimodal learning for clinical decision support. Cham: Springer International Publishing, 2018, pp.3–11. ISBN 978-3-030-00889-5.
31.
ZhangZLiuQWangY. Road extraction by deep residual u-net. IEEE Geosci Remote Sens Lett2018; 15: 749–753.
32.
WangWZhongJWuH, et al.Rvseg-net: an efficient feature pyramid cascade network for retinal vessel segmentation. In: Martel AL, Abolmaesumi P, Stoyanov D, et al. (eds) Medical image computing and computer assisted intervention – MICCAI 2020. Cham: Springer International Publishing, 2020, pp.796–805. ISBN 978-3-030-59722-1.
33.
JhaDRieglerMAJohansenD, et al.Doubleu-net: a deep convolutional neural network for medical image segmentation. In: 2020 IEEE 33rd international symposium on computer-based medical systems (CBMS), pp.558–564.
34.
ZhangJZhangYXuX. Pyramid u-net for retinal vessel segmentation. In: ICASSP 2021 - 2021 IEEE international conference on acoustics, speech and signal processing (ICASSP), pp.1125–1129.
35.
SuRZhangDLiuJ, et al.Msu-net: Multi-scale u-net for 2d medical image segmentation. Front Genet2021; 12: 639930.
36.
BeecheCSinghJPLeaderJK, et al.Super u-net: a modularized generalizable architecture. Pattern Recognit2022; 128: 108669.
37.
WangJTianSYuL, et al.Sbdf-net: a versatile dual-branch fusion network for medical image segmentation. Biomed Signal Process Control2022; 78: 103928.
38.
XieXZhangWPanX, et al.Canet: context aware network with dual-stream pyramid for medical image segmentation. Biomed Signal Process Control2023; 81: 104437.
39.
IbtehazNKiharaD. Acc-unet: a completely convolutional unet model for the 2020s. In: Greenspan H, Madabhushi A, Mousavi P, et al. (eds) Medical image computing and computer assisted intervention – MICCAI 2023. Cham: Springer Nature Switzerland, 2023, pp.692–702. ISBN 978-3-031-43898-1.
40.
LiuQZhouFShenJ, et al.A fundus vessel segmentation method based on double skip connections combined with deep supervision. Front Cell Dev Biol2024; 12: 1477819.
41.
NiJMuWPanA, et al.Fse-net: rethinking the up-sampling operation in encoder-decoder structure for retinal vessel segmentation. Biomed Signal Process Control2024; 90: 105861.
42.
KhanTMNaqviSSMeijeringE. Esdmr-net: a lightweight network with expand-squeeze and dual multiscale residual connections for medical image segmentation. Eng Appl Artif Intell2024; 133: 107995.
43.
PengYChenDZSonkaM. U-net v2: rethinking the skip connections of u-net for medical image segmentation. In: 2025 IEEE 22nd International symposium on biomedical imaging (ISBI), pp.1–5.
44.
WangBWeiZJuM, et al.Efficient hierarchical multiscale convolutional attention for accurate medical image segmentation. Vis Comput2025; 41: 10805–10826.
45.
LiHWangYWanC, et al.Mau-net: a retinal vessels segmentation method. In: 2020 42nd annual international conference of the IEEE engineering in medicine & biology society (EMBC), pp.1958–1961.
46.
ZhangHNiWLuoY, et al.Tunet-lbf: retinal fundus image fine segmentation model based on transformer unet network and lbf. Comput Biol Med2023; 159: 106937.
