Advanced Ultrasound in Diagnosis and Therapy ›› 2026, Vol. 10 ›› Issue (1): 29-41.doi: 10.26599/AUDT.2026.250056
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Fu Lijiaa, Li Nab, Liao Zilingc, Lin Yanpinga, Li Zhaojund, Li Fane,*(
)
Received:2025-12-08
Revised:2026-01-07
Accepted:2026-01-18
Online:2026-03-31
Published:2026-03-30
Contact:
Department of Ultrasound, Shanghai Chest Hospital of Shanghai Jiao Tong University School of Medicine, 241 West Huaihai Road, Shanghai, 200030, China; Shanghai Jiao Tong University School of Medicine, 227 South Chongqing Road, Shanghai 200025, China, e-mail: medicineli@163.com(F L),
Fu Lijia, Li Na, Liao Ziling, Lin Yanping, Li Zhaojun, Li Fan. Advances in Breast Ultrasound Segmentation and Classification. Advanced Ultrasound in Diagnosis and Therapy, 2026, 10(1): 29-41.
Figure 1
Schematic overview of the review structure. The framework connects the five primary ultrasound modalities (B-mode, Elastography, 3D Ultrasound, CEUS, and Color Doppler) to the downstream tasks of segmentation and classification, categorizing the key technological methodologies applied in recent CAD systems."
Figure 2
Three breast ultrasound images from the BUSI dataset [10]."
Figure 3
The upper image displays a SWE image indicating increased stiffness in the region surrounding the tumor, and the lower image is the corresponding B-mode ultrasound [11]."
Figure 4
Somo-V generated coronal view of a body mass in 9 consecutive slices. Red arrows indicate the location of the mass [14]."
Figure 5
Images of breast tumors. The left image represents the US image and the right image shows the corresponding CEUS image [16]."
Figure 6
Schematic diagram of GG-Net [28]."
Figure 7
Schematic representation of the framework for the segmentation method utilizing a self-attention mechanism. (A) Network structure; (B) SC module integrated with ResNet; (C) SC module integrated with ResNeXt [36]."
Table 1
Comparison of different breast ultrasound segmentation models."
| Author | Image type | Year | Method | Dataset | Performance (Dice) |
| Xue C et al. [ | B-mode | 2021 | GG-Net | BUSI/Private | 87.10%/82.10% |
| Ning Z et al. [ | B-mode | 2021 | SMU-Net | BUSI/UDIAT | 88.27%/87.03% |
| Chen G et al. [ | B-mode | 2022 | U-net + BAGNet + RFNet | BUSI | 79.35% |
| Zhai D et al. [ | B-mode | 2022 | ASSGAN | DBUI/SPDBUI/ADBUI | 86.90%/93.91%/76.44% |
| Chen G et al. [ | B-mode | 2022 | AAU-net | BUSI/UDIAT | 77.51%/78.14% |
| Huang R et al. [ | B-mode | 2022 | boundary-rendering | UDIAT | 89.40% |
| Li Y et al. [ | B-mode | 2022 | CAM-DLS | Private | 77.30% |
| Cho S W et al. [ | B-mode | 2022 | BTEC-Net + RFS-UNet | BUSI/UDIAT | 84.86%/85.37% |
| Lou M et al. [ | B-mode | 2022 | MCRNet | BUSI/UDIAT | 82.31%/90.05% |
| Iqbal A et al. [ | B-mode | 2022 | MDA-Net | UDIAT/BUSIS | 87.68%/91.85% |
| Wu H et al. [ | B-mode | 2023 | BUSSeg | BUSI/UDIAT | 85.77%/88.11% |
| Chen G et al. [ | B-mode | 2023 | NU-net | BUSI/UDIAT | 78.62%/80.80% |
| Lei B et al. [ | ABUS | 2020 | Self-attention + Res-SC + NCB | Private | 86.60% |
| Lei Y et al. [ | ABUS | 2021 | Mask scoring R-CNN | BUSI/UDIAT | 82.31%/90.05% |
| Meng Z et al. [ | US+CEUS | 2022 | CEUSegNet | Private | US: 91.05%, CEUS: 89.97% |
| Xie X et al. [ | US+CEUS | 2023 | IMAN | Private | US: 83.96%, CEUS: 81.16% |
Figure 8
General schematic of the BVA Net. The BVA network consists of two parts, a main architecture (RA net) that applies spatial residual attention, and a BI-RADS attention branch that applies channel smart attention using BI-RADS hierarchy [44]."
Figure 9
Schematic diagram of the architecture of the CEUS video-based diagnostic model by Chen et al. [53]."
Table 2
Comparison of breast ultrasound classification models."
| Author | Image type | Year | Method | Dataset | Performance |
| Ciritsis A et al. [ | B-mode US | 2019 | dCNN | Private | AUC: 0.838 (Internal)/ 0.967 (External) |
| Al-Dhabyani W et al. [ | B-mode US | 2019 | NASNet + DAGAN | BUSI; UDIAT | ACC: 94% (BUSI); 99% (Combined) |
| Fujioka T et al. [ | SWE | 2020 | DenseNet169 | Private | AUC: 0.898; Sens: 85.7% |
| Li Y et al. [ | B-mode + SWE + CEUS | 2020 | RAB (Radiomics + Attribute Bagging) | Private | ACC: 84.1%; AUC: 0.919 |
| Moon W K et al. [ | B-mode US | 2020 | Ensemble CNN (VGG, ResNet, DenseNet) | Private; BUSI | ACC: 94.6%; (BUSI); AUC: 0.97 |
| Qian X et al. [ | B-mode + Color Doppler | 2020 | CNN | Private | AUC: 0.982; Spec: 88.7% |
| Wang J et al. [ | B-mode + Doppler + SWE + SE | 2020 | ResNet-18 | Private | ACC: 95.4%; Sens: 96.1% |
| Yang Z et al. [ | B-mode + CEUS | 2020 | TSDBN | Private | ACC: 90.2%; F1: 93.2% |
| Chen C et al. [ | CEUS | 2021 | DKG-C3D (3D-CNN + Attention) | Private | ACC: 86.3%; Sens: 97.2% |
| Xing J et al. [ | B-mode US | 2021 | BVA Net (ResNet-50 + Attention) | Private; UDIAT; BUSI | AUC: 0.91 (Private); AUC: 0.89 (BUSI) |
| Zhou Y et al. [ | 3D ABUS | 2021 | Faster R-CNN + Multi-view Analysis | Private | Sens: 95.1% |
| Bourouis S et al. [ | B-mode US | 2022 | GWO-WNN | Public | ACC: 98.0%; Sens: 98.8% |
| Gong X et al. [ | B-mode + CEUS | 2022 | BUS-Net (ResNet/R(2+1)D) | Private | ACC: 89.7%; AUC: 0.93 |
| Hejduk P et al. [ | 3D ABUS | 2022 | dCNN + Sliding Window | Private | ACC: 90.9% |
| Ragab M et al. [ | B-mode US | 2022 | Ensemble DL + CSO-MLP | BUSI | ACC: 97.1%; Spec: 97.7% |
| Shao Y et al. [ | S-WAVE (RF Data) | 2022 | SVM/Random Forest | Private | AUC: 95%; Sens: 95% |
| Xie L et al. [ | US + Peritumoral SWE | 2023 | EfficientNet-B0 | Private | AUC: 0.93; ACC: 91% |
| Yao Z et al. [ | Virtual EUS | 2023 | GAN (U-Net + Discriminator) | Private | V-EUS AUC: 0.75 |
| Atrey K et al. [ | US + Mammogram | 2024 | SVM (Cubic kernel) | Private | ACC: 98.8%; Spec: 99.3% |
| Guo D et al. [ | B-mode + CEUS | 2024 | KAMnet (Knowledge-Augmented) | Private | ACC: 88.2%; Sens: 90.9% |
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