Cited by 284Medical Image Segmentation TechniquesAI in cancer detectionBrain Tumor Detection and Classification

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Automatic breast ultrasound image segmentation: A survey

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Xian, Min, Zhang, Yingtao, Cheng, H.D., Xu, Fei, Zhang, Boyu, Ding, Jianrui·2018·Pattern Recognition

DOI 10.1016/j.patcog.2018.02.012

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Support-vector networks
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421 · cited 1×
“Let w be the normal vector to the hyperplane, and { = (0, 1 − ( ∙ + ))} be slack variables for the soft margins; then the problem can be formulated as a constrained quadratic optimization problem [97] 1 + ‖ ‖ (30)”
A Threshold Selection Method from Gray-Level Histograms
Otsu, N. (1979) · IEEE Transactions on Systems, Man and Cybernetics · doi:10.1109/tsmc.1979.4310076 · Cited by 44,708 · cited 1×
“[115] Global thresholding ROI generation Otsu’s algorithm [130] Depended on image preprocessing.”
Method
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834 · cited 1×
“Deep learning: in the last several years, deep learning has demonstrated impressive performance for many tasks such as object recognition [158], image classification [159], semantic segmentation [160], facial expression recognition [163], speech recognition [164], medical applications [161, 170], etc.”
Background
Applied logistic regression
(1990) · Choice Reviews Online · doi:10.5860/choice.27-3338 · Cited by 35,305
Support-Vector Networks
Cortes, C., & Vapnik, V. N. (1995) · Machine Learning · doi:10.1023/a:1022627411411 · Cited by 34,565
Statistical Learning Theory
Wu, Y., & Vapnik, V. N. (1999) · Technometrics · doi:10.2307/1271368 · Cited by 27,236
Book Review: Pattern Recognition and Machine Learning
Nasrabadi, N. M. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2819119 · Cited by 22,066 · cited 1×
“Graph-based approaches Graph-based approaches gain increasing popularity in BUS image segmentation because they offer several advantages: (1) they provide a simple way to organize task-related priors and image information in a unified framework; (2) they are flexible and suitable for expressing soft constraints betwee…”
Background
A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466
Applied Logistic Regression
Hosmer, D. W., & Lemeshow, S. (2000) · doi:10.1002/0471722146 · Cited by 20,302
Stochastic relaxation, gibbs distributions, and the bayesian restoration of images.
Geman, S., & Geman, D. (1984) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1984.4767596 · Cited by 17,949 · cited 1×
“Let = ( , ℰ ) be the subgraph corresponding to non-tumor regions, and = ( , ℰ ) be the subgraph corresponding to the tumor regions, where , ⊆ and ℰ , ℰ ⊆ ℰ, ∪ = , ∩ = ∅, and ℰ ∩ ℰ = ∅.”

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J. Shan, A Fully Automatic Segmentation Method for Breast Ultrasound Images, Dissertation, USU, 2011.
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Items 201–284 of 284

A windowing-based multi-view u-net for tumor segmentation in cone-beam breast CT
Peng, D., Su, T., Zheng, Y., Ouyang, Z., Chen, B., Chen, B., He, J., Ma, Q., Yang, L., Li, H., et al. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.107482 · Cited by 3
Hybrid adaptive attention deep supervision-guided U-Net for breast lesion segmentation in ultrasound computed tomography images
Liu, X., Zhou, L., Cai, M., Zheng, H., Zeng, S., Wang, X., Wang, Y., & Ding, M. (2025) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-025-03377-z · Cited by 3
“However, the segmentation of BUCT images can help the physician characterize and localize the breast tumors, which is one of the key steps in computer-aided diagnosis (CAD) [7] of breast cancer in breast ultrasound images.”
Nipple Localization in Automated Whole Breast Ultrasound Coronal Scans Using Ensemble Learning
Raj, A. N. J., Nersisson, R., Mahesh, V. G. V., & Zhuang, Z. (2020) · Ultrasonic Imaging · doi:10.1177/0161734620974273 · Cited by 3
“Hu P h I i j P W a i j a HI i j a k a ( ) ( , ) , , , = ( ) ∈ ρ (9)”
Method
Advances in ultrasonography: image formation and quality assessment
Hasegawa, H. (2022) · Choonpa Igaku · doi:10.3179/jjmu.jjmu.r.210 · Cited by 3
CEmb-SAM: Segment Anything Model with Condition Embedding for Joint Learning from Heterogeneous Datasets
Shin, D., Kim, B. S., & Baek, S. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-47401-9_27 · Cited by 3
When deep learning is not enough: artificial life as a supplementary tool for segmentation of ultrasound images of breast cancer
Karunanayake, N., & Makhanov, S. S. (2024) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-024-03026-x · Cited by 3
“We refer the reader to the recent surveys [28, 29].”
Interlayer information fusion-based and dual-attention improved U-Net for ABVS image sequence intelligent tumor segmentation
Yang, X., Li, X., Qin, Y., Wang, H., Zhao, C., & Yin, Y. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106740 · Cited by 3
Multilevel perception boundary‐guided network for breast lesion segmentation in ultrasound images
Yang, X., Zhang, J., Ou, Y., Chen, Q., Wang, L., & Wang, L. (2025) · Medical Physics · doi:10.1002/mp.17647 · Cited by 5
Tumor segmentation and boundary fusion network via hierarchical cross-correlation learning
Ding, G., Yang, K., Sun, L., & Fang, Y. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108208 · Cited by 3
Detection of Unlabeled Micro- and Nanoplastics in Unstained Tissue with Optical Photothermal Infrared SpectroscopyPreprint
Duswald, K., Pichler, V., Kopatz, V., Limberger, T., Karl, V., Wimberger, D., Zimmerleiter, R., Wadsak, W., Hettich, M., Kenner, L., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.11.622943 · Cited by 3
Ultrasound Breast Image Classification Through Domain Knowledge Integration Into Deep Neural Networks
Nehary, E. A., & Rajan, S. (2024) · IEEE Access · doi:10.1109/access.2024.3442374 · Cited by 3
Semi-supervised breast ultrasound image segmentation via multi-paradigm synergy and cross-cyclic distillation
Yang, H., Yang, D., Huang, Q., & Jiang, L. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.130462 · Cited by 2
TRAM-UNet: Transformer and Region Attention Module based U-Net for Breast Ultrasound Image Segmentation
Zhou, J., & Sumi, C. (2025) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc58623.2025.11253082 · Cited by 2
“Recent advancements in automated breast ultrasound image segmentation present opportunities to enhance diagnostic accuracy [2].”
Improving the Quality of Multi-spectral Transmission Image Based on Modulation-frame Accumulation and Local Linear Embedding Algorithm
Liu, F., Li, G., & Lin, L. (2021) · IEEE International Conference on Signal and Image Processing (ICSIP) · doi:10.1109/icsip52628.2021.9688773 · Cited by 2
“At present, besides relying on clinicians’ hand-diagnosis, the detection of breast cancer mainly includes mammography[2,3], breast ultrasound[4,5], magnetic resonance imaging (MRI) [6,7], positron emission tomography computed tomography (PET-CT) [8] and histopathological examination.”
Background
Automated brain segmentation for guidance of ultrasonic transcranial tissue pulsatility image analysis
Leotta, D. F., Kucewicz, J. C., LaPiana, N., & Mourad, P. D. (2023) · Neuroscience Informatics · doi:10.1016/j.neuri.2023.100146 · Cited by 2
Distribution-Aware Contrastive Learning for Robust Medical Image Segmentation
Qin, Z., Xi, X., & Yin, Y. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10446000 · Cited by 2
“…analysis: (a) The first dataset, HMSS, comprises 2499 tumor images from 554 patients, (url: https://www.ultrasoundcases.info/), For quantitatively comparing different methods, we employ Dice Similarity Coefficient (DSC), Jaccard Index (JI), Recall, and Precision [21, 22] as segmentation metrics.”
Method
Echocardiographic Image Segmentation with Vision Transformers: A Comparative Analysis of Different Loss Functions
Bosco, E., Magenes, G., & Matrone, G. C. (2024) · IEEE International Symposium on Medical Measurements and Applications (MeMeA) · doi:10.1109/memea60663.2024.10596874 · Cited by 3
“Deep learning has proven to be highly effective in segmentation tasks within cardiac ultrasound imaging, much like it has in various other imaging modalities [9], [10], [11], [12], [13], [14], [15].”
Background
ACL-DUNet: A tumor segmentation method based on multiple attention and densely connected breast ultrasound images
Zhang, H., He, L. J., Wenjia, G., Ma, J., Gang, S., & Ma, H. (2024) · PLoS ONE · doi:10.1371/journal.pone.0307916 · Cited by 3
“Currently, ultrasound imaging is a well-PLOS established tool for early assessment and diagnosis of abnormal breast conditions due to its non-invasive imaging procedure, high sensitivity, and cost-effectiveness [4, 5], Therefore, it is widely used for breast cancer screening.”
Background
EfficientNet-Based Attention Residual U-Net With Guided Loss for Breast Tumor Segmentation in Ultrasound Images
Jasrotia, H., Singh, C. K., & Kaur, S. (2025) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2025.03.009 · Cited by 3
Basketball Object Extraction Method Based on Image Segmentation Algorithm
Zhu, H., & Liu, L. (2022) · Security and Communication Networks · doi:10.1155/2022/3021682 · Cited by 2
“)e author uses the maximum likelihood to estimate the model parameters in the training process by increasing the gradient [25, 26].”
Method
Retracted
Breast Anatomy Enriched Tumor Saliency Estimation
Xu, F., Zhang, Y., Cheng, H.-D., Ding, J., Zhang, B., Ning, C., & Wang, Y. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412593 · Cited by 2
“In clinical routine, breast ultrasound (BUS) is one of the modalities for cancer screening [4, 5], and automatic BUS image segmentation methods are significant for cancer diagnosis and treatment planning.”
BackgroundSelf-citation
The Impact of Filtering for Breast Ultrasound Segmentation using A Visual Attention Model
Hardani, D. N. K., Nugroho, H. A., & Ardiyanto, I. (2022) · doi:10.1109/ibiomed56408.2022.9988361 · Cited by 2
“The segmentation results will determine quantitative tumor shape, size, and pattern characteristics [14].”
Background
Developing Large Pre-trained Model for Breast Tumor Segmentation from Ultrasound Images
Li, M., Sun, K., Gu, Y., Zhang, K., Sun, Y., Li, Z., & Shen, D. G. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43990-2_9 · Cited by 2
Texture image segmentation using Vonn mixtures-based hidden Markov tree model and relative phase
Niu, P., Wang, L., Shen, X., Wang, Q., & Wang, X. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09491-4 · Cited by 2
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Chen, G., Dai, Y., Cui, L., Yin, X., & Wang, R. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5152270 · Cited by 2
Segmentation of Breast Lesions in Ultrasound Images Based on Swin-Unet
Zhang, C., & Ren, H. (2024) · International Conference on Image Processing, Computer Vision and Machine Learning (ICICML) · doi:10.1109/icicml63543.2024.10958006 · Cited by 1
“The imaging examinations of breast cancer, particularly mammography (breast molybdenum target), ultrasound, and magnetic resonance imaging (MRI), play a vital role in the early detection and diagnosis of breast cancer [3].”
UANeT: An uncertainty-aware network for feature calibration and boundary refinement in medical image segmentation
Zhang, Q., Wang, J., Wu, W., & Zheng, Y. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108413 · Cited by 1
LRA-UNet: Large receptive-field attention-enhanced U-Net for breast lesion segmentation in ultrasound images
Tang, L., Zhou, Q., Zhang, Y., Hou, N., & Zhu, G. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.114890 · Cited by 1
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F-Net: breast cancerous lesion region segmentation based on improved U-Net
Deng, X., Pan, L., & Dang, Z. (2025) · Optoelectronics Letters · doi:10.1007/s11801-025-4182-x · Cited by 1
Structural consistency alignment U-Net for breast tumors ultrasound segmentation
Zhang, J., Yu, Y., Liu, Y., Zhang, Y., & Pan, B. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108763 · Cited by 1
Self-citation
ASFCP-Net: Axial Spatial-Frequency Domain Collaborative Perception Dual-Branch Network for breast lesion segmentation in ultrasound images
Dong, S., Liu, G., Liu, C., & Liu, D. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.109124 · Cited by 1
DAUS-Net: Toward Ultrasound Scanner-Agnostic Domain Generalized Robust and Accurate Segmentation
Lee, S., Jung, D., Guezzi, N., Nam, S., & Yu, J. (2025) · Ultrasonic Imaging · doi:10.1177/01617346251388454 · Cited by 1
“3.”
SFDDNet: A Spatial-Frequency Dual-Decoder Network for Breast Ultrasound Segmentation
Shan, X., Peng, Y., Jiang, Y., & Jin, L. (2026) · IEEE Signal Processing Letters · doi:10.1109/lsp.2026.3655340 · Cited by 1
UA-MAE: An Uncertainty-Aware Masked Autoencoder for Breast Lesion Segmentation in Ultrasound Images
Xiong, X., Sun, Y., Huang, J., Huang, D., Chen, S., Zhang, Z., & Tan, T. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11356317 · Cited by 1
“Segmenting breast lesions in ultrasound images is essential for lesion localization and characterization, aiding treatment planning and improving survival rates [2].”
MSA-Net: Masked Separable Attention Network for Breast Ultrasound Tumor Segmentation
Wang, C., Zhu, Y., Li, Q., Zhang, S., & Liu, W. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11356822 · Cited by 1
“1, research indicates that automatic segmentation algorithms for breast tumors, as the core module of CAD systems, can effectively optimize lesion quantification efficiency [6].”
Hybrid Loss-Driven Attention Residual U-Net with Multiscale Cascaded CBAM for Breast Cancer Segmentation
Zafar, M., Amin, J., Sharif, M., & Alnfiai, M. M. (2026) · Cognitive Computation · doi:10.1007/s12559-026-10554-1 · Cited by 2
Learning Conceptual Text Prompts from Visual Regions of Interest for Medical Image Segmentation
He, Z., Zhang, H., Zhang, W., Zhao, S., Liu, Q., Wu, X., & Lao, Q. (2026) · Engineering · doi:10.1016/j.eng.2026.04.006 · Cited by 1
RA-BUSSeg: Relation-Aware Semi-Supervised Breast Ultrasound Image Segmentation via Adjacent Propagation and Cross-Layer Alignment
Zhang, W., Ding, Z., Chen, G., Wu, H., & Qin, J. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.02014 · Cited by 1
“To the end, many recent studies [4, 16, 29, 34] are devoted to developing deep learning models for automatic segmentation of breast lesions from BUS images to facilitate the construction of these systems.”
LKCAU-Net: A Large Kernel Coordinated Attention U-Net for Breast Tumors Segmentation in Ultrasound Images
Jiang, L., Zheng, Y., & Huang, A. (2026) · Ultrasonic Imaging · doi:10.1177/01617346261463796 · Cited by 1
A Superpixel-Classification-Based Method for Breast Ultrasound Images
Huang, Y., & Huang, Q. (2018) · International Conference on Systems and Informatics (ICSAI) · doi:10.1109/icsai.2018.8599423 · Cited by 1
“Automatic BUS segmentation has been studied extensively [24] in the last several years and it has become a research hotspot [5-7, 22, 23].”
Background
A Breast Lesion Segmentation Method Based on Radio Frequency Ultrasound Signals
Zhang, S., & Han, S. (2023) · International Symposium on Computer Technology and Information Science (ISCTIS) · doi:10.1109/isctis58954.2023.10213002 · Cited by 1
Beamforming methods in the field of medical ultrasonics
Hasegawa, H. (2024) · IEICE ESS FUNDAMENTALS REVIEW · doi:10.1587/essfr.18.1_56 · Cited by 1
SGF-SCA: A Spatial Gated Framework with Shared Channel Attention for Breast Ultrasound Image Segmentation
Xu, T., Li, Y., Song, Z., Xu, Y., Wu, Y., & He, X. (2024) · International Conference on Video, Signal and Image Processing (VSIP) · doi:10.1145/3708568.3708579 · Cited by 1
Extraction of Features from Breast Cancer Mammogram Image Using Some Image Processing Techniques
Pati, D. P., & Panda, S. (2018) · International Conference on Parallel, Distributed and Grid Computing (PDGC) · doi:10.1109/pdgc.2018.8745891 · Cited by 1
Is denoising necessary for ultrasound image segmentation deep learning: review and benchmarkPreprint
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Is despeckling necessary for deep learning based ultrasound image segmentation?Preprint
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Medical-Network (Med-Net): A Neural Network for Breast Cancer Segmentation in Ultrasound Image
Alzahrani, Y., & Boufama, B. (2023) · Smart innovation, systems and technologies · doi:10.1007/978-981-19-7742-8_12 · Cited by 1
Early Diagnosis of Rheumatoid Arthritis of the Wrist Using Power Doppler Ultrasound: A Review
Asha, D. P. S., & Hemalatha, R. J. (2023) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-35078-8_27
A Dual-branch Framework Based on Implicit Continuous Representation for Tumor Image SegmentationPreprint
Wang, J., Zheng, Y., Wang, J., Xiao, X., & Sun, J. (2023) · Research Square · doi:10.21203/rs.3.rs-3548540/v1
Is denoising necessary for ultrasound image segmentation deep learning: review and benchmarkPreprint
Liu, F., Chen, L., Qin, P., Xu, S., Dong, Z., Zhao, X., Wan, X., Chen, X., & Qin, B. (2023) · doi:10.36227/techrxiv.22186456.v3
Performance Evaluation of Different Deep Learning Models for Breast Cancer Detection in Mammograms
Das, J. K., Pramanik, S., & Bhattacharjee, D. (2023) · Algorithms for intelligent systems · doi:10.1007/978-981-99-1509-5_30
Caam: Medical Ultrasound Image Robust Segmentation with a Concurrent Adaptive Attention ModulePreprint
Chen, G., Yin, X., Cui, L., & Chen, L. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4934025
Segmentasi Objek Citra Ultrasonografi Terotomatisasi Menggunakan Metode Aktif Kontur Kombinatorial
Nugroho, A., Sunarko, B., Wibawanto, H., Mulwinda, A., Fauzi, A., Oktaviyanti, D., & Savitri, D. W. (2024) · Jurnal Teknologi dan Sistem Komputer · doi:10.14710/jtsiskom.2023.14712
MICN: Multi-level Induced Cross-Attention Network for Breast Lesion Segmentation
Ye, X., Qu, X., Xu, Z., & Kang, Y. (2024) · International Conference on Advanced Robotics and Mechatronics (ICARM) · doi:10.1109/icarm62033.2024.10715865
Breast Cancer Segmentation Using UNet and Global Convolutional Networks
Thyagachandran, A., & Ahmed, Y. A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-78201-5_8
A Progressive Soft Erase and Multi-Scale Feature Fusion Method for Medical Education Management of Breast Cancer
Liu, Y., Li, J., Liu, X., Xu, X., Ding, S., Zhao, L., & Liu, Z. (2024) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc54092.2024.10832038
“However, they are greatly affected by the results of feature extraction, which limits their generalization ability and makes it challenging to apply them to the segmentation lesions on BUS in complex environments [15].”
Label-free nano- and microplastics detection in mammalian tissue by photothermal infrared spectroscopy
Karl, V., Duswald, K., Pichler, V., Limberger, T., Kopatz, V., Pacher-Deutsch, C., Stadlbauer, V., Horvath, A., Gruber, E. S., Kenner, L., et al. (2025) · Optical Biopsy: Toward Real-Time Spectroscopic Imaging and Diagnosis · doi:10.1117/12.3049173
“For further details the reader is referred to [9], [12].”
A superpixel based self-attention network for uterine fibroid segmentation in high intensity focused ultrasound guidance images
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A Brief Perspective on Deep Learning Approaches for 2D Semantic Segmentation
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Ultrasound Breast Tumor Segmentation Using Deep Learning Models
Manasa, C. M., Sapna, R., & Pavithra, N. (2025) · IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS) · doi:10.1109/i2cacis65476.2025.11100680
“This emphasizes the necessity of scalable and effective solutions [3].”
FEQIN: A feature-enhanced query interaction network for breast ultrasound segmentation
Luo, X., & Ou-Yang, L. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.112168
Segmentation and Identification of Breast Nodules in Ultrasound Images
Souza, W. A., Ambrósio, P. E., & Iglesias, S. M. (2025) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-031-94934-0_21
Pre-trained Encoder-Decoder Architecture for Breast Ultrasound Image Segmentation
Taljeh, M. Z., & Shekar, B. H. (2025) · Lecture notes in electrical engineering · doi:10.1007/978-981-96-9967-4_4
Segnet Deep Architecture for Breast Tumor Segmentation
Taljeh, M. Z., & Shekar, B. H. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-8441-0_14
ETRT-Net: Efficient Non-Local Transformer and Residual Triplet Attention for Breast Lesion Segmentation
Wang, C., Cao, J., Wang, J., & Liu, S. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11228056
“The segmentation of breast ultrasound (BUS) images is essential for accurately identifying and localizing breast tumors, serving as a foundational component in computer-aided diagnosis (CAD) [1].”
Auto-BUSAM: Auto-segmentation of attention-diverted low-contrast breast ultrasound images
Liu, X., Hussain, M., Huang, J. W., Li, Q., Khan, M. T., & Wu, H. (2025) · Displays · doi:10.1016/j.displa.2025.103314
Uncertainty-weighted feature alignment and information interaction network for multi-scale medical image segmentation
Zhang, M., Wang, J., Wang, J., & Zheng, Y. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.109399
Future Technological & Research Perspectives
Cammarasana, S., & Patanè, G. (2026) · Synthesis lectures on visual computing · doi:10.1007/978-3-032-05805-8_8
Research on breast ultrasound images lesion localization and diagnosis based on knowledge-driven and data-driven methods
Li, J., Song, L., Liu, X., Liu, Y., Ma, T., Bai, J., Zhao, Q., & Xu, X. (2026) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2026.111465 · Cited by 1
A Novel Attention-Driven Framework with Hierarchical Interaction and Bi-Path Attention for Breast Lesion Segmentation
Cao, J., Shi, J., & Wang, J. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11357129
“Accurately partitioning ultrasound images of the breast is essential for identifying and pinpointing tumors, serving as a cornerstone for computer-aided diagnostic systems [1], [2].”
A Transform-Based UNet Architecture for Breast Tumor Segmentation
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Generational shift in computer vision for fully automated petrographic image analysisPreprint
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Predicting the outcome of intracytoplasmic sperm injection in women using machine learning combining Doppler and clinical data : SPIRL
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GroupD-MixNet: Accurate ultrasound lesion segmentation with multi-scale dynamic kernels
Wang, Y., Zhang, W., ZHANG, C., & Lu, F. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.110248
USMamba: A hybrid Mamba-CNN network for breast tumor segmentation in ultrasound images with Frequency-Graph Perception Fusion
Zhu, X., Bai, L., Zhang, C., Liu, H., & Zhang, J. W. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.110519
MBUSNet: Multimodal breast ultrasound segmentation with attention-guided rectification and boundary enhancement
Yin, H., Zhao, Y., Tao, L., Lv, X., Hu, Y., & Lu, X. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.110746
Eel and Grouper Lyrebird Optimizer-Based Fractal Deep Spiking Residual Network for Breast Cancer Detection Using Mammogram Images
Urabinahatti, S. A., Jayadevappa, D., & Kruthi, T. S. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-19321-6_7
RTCMUNet: retinex-inspired feature modulation for breast ultrasound lesion segmentation
Ma, L., & Cai, W. (2026) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ae97d2
“Ultrasound imaging is widely used in clinical breast screening because it is real-time, non-ionising, and portable (Huang et al 2018, Xian et al 2018).”
ROD-Net: a residual orthogonal decoupling network with semantic-guided interaction for breast ultrasound analysis
Li, X., Zhang, Y., Shen, Z., Chen, Z., & Li, X. (2026) · Multimedia Systems · doi:10.1007/s00530-026-02614-1
Self-citation
Intelligent ultrasound image segmentation using adaptive coupled fractional deformable model
Kumar, A., & Jain, S. K. (2026) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2026.109019
SFCT-Net: A spatial–frequency collaborative transformer for robust breast ultrasound tumor segmentation
Chen, J., Zhuang, S., Zhuang, Z., Wang, H., Guo, J., & Zeng, H. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.111355
RDPA-UNet: Differential-Path Feature Learning and Multi-Scale Context Aggregation for Ultrasound Lesion Segmentation
Jin, J., Zhang, H., & Wu, K. (2026) · Mathematics · doi:10.3390/math14173232
Latent-CURE: Interpretable Breast Cancer Diagnosis via Dual-Asymmetric Chain-of-Thought
Zhao, W., Tan, X., Gan, L., Liu, L., & Qiu, X. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38098-2_35
From static wavelets to learnable spectral modulation after wavelet decomposition: a guided region-contour consistency network for breast ultrasound segmentation
Jiang, Z., & Wang, R. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-01168-6
“Among the available imaging modalities, breast ultrasound (BUS) has gained widespread clinical acceptance owing to its noninvasive nature, absence of ionizing radiation, cost-effectiveness, and real-time imaging capability (Xian et al. 2018; Sood et al. 2019).”

Authors: Xian, M., Zhang, Y., Cheng, H. D., Xu, F., Zhang, B., & Ding, J.
Affiliations: University of Idaho (US)
Harbin Institute of Technology (CN)
Utah State University (US)
Corresponding author: Cheng, H.D.
ORCID: Xian, Min
Zhang, Yingtao
Cheng, H.D.
Xu, Fei
Zhang, Boyu
Ding, Jianrui
Venue: Pattern Recognition
Publisher: Elsevier BV
Published: 2018-07
Volume: 79
Pages: 340-355
ISSN: 0031-3203 (ISSN-L)
DOI: 10.1016/j.patcog.2018.02.012
OpenAlex: W2963104294
Cited by: 284
References: 186
Citations by year: 2026: 20 · 2025: 47 · 2024: 41 · 2023: 37 · 2022: 31 · 2021: 42 · 2020: 36 · 2019: 20 · 2018: 10
FWCI: 13.51
Type: journal article
Language: en
Open access: green
arxiv.org
Licence: public-domain
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: Pattern Recognition (publishedVersion)
arXiv (Cornell University) (submittedVersion, public-domain)
arXiv (Cornell University)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Brain Tumor Detection and Classification
Keywords: breast ultrasound, image segmentation, breast cancer, automatic segmentation, medical image analysis

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Automatic breast ultrasound image segmentation: A survey

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