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 101–200 of 284

A deep learning localization method for measuring abdominal muscle dimensions in ultrasound images
Saleh, A., Laradji, I., Lammie, C., Vázquez, D., Flavell, C. A., & Rahimi Azghadi, M. (2021) · ResearchOnline - JCU (James Cook University) · Cited by 15
BAGNet: Bidirectional Aware Guidance Network for Malignant Breast lesions Segmentation
Chen, G., Liu, Y., Dai, Y., Zhang, J., Cui, L., & Yin, X. (2022) · Asia-Pacific Conference on Intelligent Robot Systems (ACIRS) · doi:10.1109/acirs55390.2022.9845607 · Cited by 16
“These segmentation approaches can be classified into three types: manual, semi-automatic and automatic according to the degree of human intervention in the segmentation process [5].”
Method
CTG-Net: Cross-task guided network for breast ultrasound diagnosis
Yang, K., Suzuki, A., Ye, J., Nosato, H., Izumori, A., & Sakanashi, H. (2022) · PLoS ONE · doi:10.1371/journal.pone.0271106 · Cited by 16
“The literature [4] has reported three major difficulties in developing CAD systems for BUS images: 1) BUS images are commonly obscure because of speckle noise, low contrast, and artifacts; 2) the breast structure is complex owing to patient differences; and 3) tumors are diverse, leading to wide…”
Automatic segmentation of ultrasound images using SegNet and local Nakagami distribution fitting model
Cui, W., Meng, D., Lu, K., Wu, Y., Pan, Z., Li, X., & Sun, S. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104431 · Cited by 15
Automatic breast mass segmentation in ultrasound images with U-Net and resolution enhancement blocks
Rahmani, A. A., Shirazi, A. A. B., & Behnam, H. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106270 · Cited by 15
A Cross-scale Attention-Based U-Net for Breast Ultrasound Image Segmentation
Wang, T., Liu, J., & Tang, J. (2025) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-025-01392-y · Cited by 17
Cross-Tissue/Organ Transfer Learning for the Segmentation of Ultrasound Images Using Deep Residual U-Net
Huang, H., Chen, H., Xu, H., Chen, Y., Yu, Q., Cai, Y., & Zhang, Q. (2021) · Journal of Medical and Biological Engineering · doi:10.1007/s40846-020-00585-w · Cited by 15
Double level set segmentation model based on mutual exclusion of adjacent regions with application to brain MR images
Yang, Y., Xie, R., Jia, W., & Zhao, G. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107266 · Cited by 15
EMT-NET: Efficient Multitask Network for Computer-Aided Diagnosis of Breast Cancer
Shi, J., Vakanski, A., Xian, M., Ding, J., & Ning, C. (2022) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi52829.2022.9761438 · Cited by 15
“Due to the speckle noise, poor image quality, and variable tumor shapes and sizes, achieving accurate BUS image segmentation is challenging [7].”
BackgroundSelf-citation
Hybrid segmentation method based on multi‐scale Gaussian kernel fuzzy clustering with spatial bias correction and region‐scalable fitting for breast US images
Panigrahi, L., Verma, K., & Singh, B. K. (2018) · IET Computer Vision · doi:10.1049/iet-cvi.2018.5332 · Cited by 15
“Furthermore, Xian et al. [3] presented a review paper on breast US image segmentation based on basic ideas, pros and cons, principles and application issues of different breast US image segmentation approaches.”
BackgroundMethod
A review of deep learning models for medical diagnosis
Kunapuli, S. S., & Bhallamudi, P. C. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-821777-1.00007-0 · Cited by 16
Deep learning applied to breast imaging classification and segmentation with human expert intervention
Wilding, R., Sheraton, V. M., Soto, L., Chotai, N. C., & Tan, E. Y. (2022) · Journal of Ultrasound · doi:10.1007/s40477-021-00642-3 · Cited by 13
Densely connected convolutional networks for ultrasound image based lesion segmentation
Ma, J., Kong, D., Wu, F., Bao, L., Jing, Y., & Liu, Y. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107725 · Cited by 14
Comparative Analysis of Segmentation Techniques using Histopathological Images of Breast Cancer
Kaushal, C., Koundal, D., & Singla, A. (2019) · International Conference on Computing Methodologies and Communication (ICCMC) · doi:10.1109/iccmc.2019.8819659 · Cited by 14
“To overcome these factors, various segmentation techniques exist in the literature [4, 5].”
Method
DFLNet: Disentangled feature learning network for breast cancer ultrasound image segmentation
Ye, Z., Huang, J., Zhang, Y., Deng, J., Zhang, J., Liu, S., Wang, D., Mei, L., & Lei, C. (2025) · Digital Signal Processing · doi:10.1016/j.dsp.2025.105331 · Cited by 15
Self-citation
MADR-Net: multi-level attention dilated residual neural network for segmentation of medical images
Keerthiveena, B., Ramteke, M., Mittal, S., Bhargava, R., & Rathore, A. S. (2024) · Scientific Reports · doi:10.1038/s41598-024-63538-2 · Cited by 14
An approach for classification of breast cancer using lightweight deep convolution neural network
Elaraby, A. E., Saad, A., Elmannai, H., Alabdulhafith, M. I., Hadjouni, M., & Hamdi, M. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e38524 · Cited by 13
A cGAN-based tumor segmentation method for breast ultrasound images
You, G., Qin, Y., Zhao, C., Zhao, Y., Zhu, K., Yang, X., & Li, Y. L. (2023) · Physics in Medicine and Biology · doi:10.1088/1361-6560/acdbb4 · Cited by 12
MFAR-Net: Multi-level feature interaction and Dual-Dimension adaptive reinforcement network for breast lesion segmentation in ultrasound images
Liu, G., Dong, S., Zhou, Y., Yao, S. Z., & Liu, D. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.126727 · Cited by 12
Automatic joint segmentation and classification of breast ultrasound images via multi-task learning with object contextual attention
Lu, Y., Sun, F., Wang, J., & Yu, K. M. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1567577 · Cited by 13
“ML methods have also shown great potential in the segmentation and classi ﬁ cation of breast ultrasound (BUS) images (14 – 16).”
Trustworthy Breast Ultrasound Image Semantic Segmentation Based on Fuzzy Uncertainty Reduction
Huang, K., Zhang, Y., Cheng, H.-D., & Xing, P. (2022) · Healthcare · doi:10.3390/healthcare10122480 · Cited by 13
Self-citation
A new two-stream network based on feature separation and complementation for ultrasound image segmentation
Zhu, Y., Li, C., Hu, K., Luo, H., Zhou, M., Li, X., & Gao, X. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104567 · Cited by 12
Deep Learning Methods for MRI Brain Tumor Segmentation: a comparative study
Brahim, I., Fourer, D., Vigneron, V., & Maaref, H. (2019) · International Conference on Image Processing Theory, Tools and Applications (IPTA) · doi:10.1109/ipta.2019.8936077 · Cited by 12
“Some of them include breast ultrasound image segmentation [8], lung segmentation for dynamic chest radiography [9] or detection of complete anterior cruciate ligament tear [10].”
Comparison of different CNNs for breast tumor classification from ultrasound imagesPreprint
Lazo, J. F., Moccia, S., Frontoni, E., & Momi, E. D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.14517 · Cited by 20
“Computer-aided diagnosis (CAD) systems for BUS image analysis have recently shown to be able to tackle the variability associated with both breast anatomy and BUS images, becoming a suitable tool to improve diagnosis accuracy [3].”
Background
Joint Federated Learning Using Deep Segmentation and the Gaussian Mixture Model for Breast Cancer Tumors
Nguyen, T. Y., Lam, P. D., Tinh, V. P., Le, D.-D., Nguyen, H. N., & Khoa, T. A. (2024) · IEEE Access · doi:10.1109/access.2024.3424569 · Cited by 13
A Novel Fuzzy Relative-Position-Coding Transformer for Breast Cancer Diagnosis Using Ultrasonography
Guo, Y., Jiang, R., Gu, X., Cheng, H.-D., & Garg, H. (2023) · Healthcare · doi:10.3390/healthcare11182530 · Cited by 12
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Simultaneous segmentation and correction model for color medical and natural images with intensity inhomogeneity
Yang, Y., Jia, W., & Wu, B. (2019) · The Visual Computer · doi:10.1007/s00371-019-01651-4 · Cited by 11
Gabor-based anisotropic diffusion with lattice Boltzmann method for medical ultrasound despeckling
Xu, H., Gong, Y., Xia, X., Li, D., Yan, Z., Shi, J., & Zhang, Q. (2019) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2019379 · Cited by 11
Ultrasound image segmentation methods: A review
Bass, V., Mateos, J., Rosado-Méndez, I. M., & Márquez, J. (2021) · AIP conference proceedings · doi:10.1063/5.0051110 · Cited by 11
Highly sensitive surface-plasmon-resonance- based fiber optic breast cancer detection by shining a Bessel–Gauss beam: a wave-theory-based approach
Saha, B., Goswami, N., & Saha, A. (2021) · Applied Optics · doi:10.1364/ao.427733 · Cited by 11
Residual Swin Transformer Unet with Consistency Regularization for Automatic Breast Ultrasound Tumor Segmentation
Zhuang, X., Zhu, X., Hu, H. R., Yao, J., Li, W., Chen, Y., Wang, L., Feng, N., & Xu, D. M. (2022) · Proceedings - International Conference on Image Processing · doi:10.1109/icip46576.2022.9897941 · Cited by 11
“However, due to the limited and low-quality ultrasound images and large variations of breast structures that exist among patients [3], it has become a difficult challenge to segment breast lesions in ABUS images.”
Background
Dual-Stream CoAtNet models for accurate breast ultrasound image segmentation
Zaidkilani, N., García, M. Á., & Puig, D. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-09963-w · Cited by 11
A Cross-Attention and Multilevel Feature Fusion Network for Breast Lesion Segmentation in Ultrasound Images
Liu, G., Zhou, Y., Wang, J., Chen, Z., Liu, D., & Chang, B. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3418104 · Cited by 10
Cross-modality segmentation of ultrasound image with generative adversarial network and dual normalization network
Jiao, W., Han, H., Cai, Y., He, H., Chen, H., Ding, H., Wang, W., & Zhang, Q. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110953 · Cited by 10
GFA-Net: Global Feature Aggregation Network Based on Contrastive Learning for Breast Lesion Automated Segmentation in Ultrasound Images
Wang, T., Jin, C., Chen, Y., Zhou, G., Ge, R., Xue, C., Shi, B., Liu, T., Coatrieux, J.-L., & Feng, Q. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3497186 · Cited by 10
“The segmentation of breast US (BUS) images can help doctors to qualitatively locate benign and malignant breast tumors [9].”
A prior segmentation knowledge enhanced deep learning system for the classification of tumors in ultrasound image
Jiang, T., Guo, J. Y., Xing, W., Yu, M. Y., Li, Y., Zhang, B., Dong, Y. W., & Ta, D. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109926 · Cited by 10
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An automatic segmentation of breast ultrasound images using U-Net model
Radhi, E. A., & Kamil, M. Y. (2023) · Serbian Journal of Electrical Engineering · doi:10.2298/sjee2302191r · Cited by 10
CBAM-RIUnet: Breast Tumor Segmentation With Enhanced Breast Ultrasound and Test-Time Augmentation
Boro, L. O., & Nandi, G. (2024) · Ultrasonic Imaging · doi:10.1177/01617346241276411 · Cited by 9
“25,26 Shareef et al. 27 proposed a DL framework named STAN to enhance the accuracy of small tumor segmentation across various sizes.”
Bilateral-Aware and Multi-Scale Region Guided U-Net for precise breast lesion segmentation in ultrasound images
Li, Y., Hou, X., Hao, X., Shang, R., & Jiao, L. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.129775 · Cited by 9
A segmentation knowledge-based global-local attention network for tumor classification in breast ultrasound images
Jiang, T., Li, Y., Li, Y., Xing, W., Yu, M. Y., Xie, F., & Ta, D. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.112152 · Cited by 9
O^2PF: Oversampling via Optimum-Path Forest for Breast Cancer Detection
Júnior, L. A. P., Jodas, D. S., Ribeiro, L. C. F., Moreira, T. P., & Papa, J. P. (2020) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms49503.2020.00100 · Cited by 9
“, ranging from [5, 100], most of the time, the results outperform the other techniques, which assume a shorter interval between [5, 10].”
MethodResult
Automated fetal heart rate analysis for baseline determination using EMAU-Net
Liu, M., Zeng, R., Xiao, Y., Lu, Y., Wu, Y., Long, S., Liu, J., Zheng, Z., Wang, H., & Bai, J. (2023) · Information Sciences · doi:10.1016/j.ins.2023.119281 · Cited by 9
ETCNN: An ensemble transformer-convolutional neural network for automatic analysis of fetal heart rate
Wu, Q., Lu, Y., Kang, X., Wang, H., Zheng, Z., & Bai, J. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106629 · Cited by 10
Lesion Detection in Breast Ultrasound Images Using a Machine Learning Approach and Genetic Optimization
Torres-Robles, F., Ramírez, B. E., Olveres, J., & Yen, P. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-31332-6_26 · Cited by 9
Performance of the SegNet in the Segmentation of Breast Ultrasound Lesions
Vianna, P., Farias, R., & Pereira, W. C. D. A. (2021) · Global Medical Engineering Physics Exchanges/Pan American Health Care Exchanges (GMEPE/PAHCE) · doi:10.1109/gmepe/pahce50215.2021.9434877 · Cited by 9
“While conventional methods rely on handmade features extractors, which often are made by experts in the field and based on previous studies, convolutional neural networks (CNNs) can learn by itself the main features extractors present in the input image [7].”
Method
An Automated Approach to Detect Breast Cancer Tissue Using Ultrasound Images
Hoque, M. U., Sazzad, T. M. S., Farabi, A. K. M. A., Hosen, I., & Somi, M. A. (2019) · International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT) · doi:10.1109/icasert.2019.8934546 · Cited by 9
“Automatic breast ultrasound image segmentation is vital for most cancer diagnosis and arranging treatments [5].”
Background
DFA-UNet: dual-stream feature-fusion attention U-Net for lymph node segmentation in lung cancer diagnosis
Zhou, Q., Zhou, Y., Hou, N., Zhang, Y., Zhu, G., & Li, L. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1448294 · Cited by 8
“On the one hand, traditional ultrasound images suffer from low contrast and high noise, leading to blurred node edges and abnormal boundary changes (Xian et al., 2018; Liu et al., 2019; Chen et al., 2022).”
BackgroundSelf-citation
MAR-GAN: Multi attention residual generative adversarial network for tumor segmentation in breast ultrasounds
Haq, I. U., Ali, H., Li, Y., & Liu, Z. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.107171 · Cited by 8
Semi-dynamic Control of FCM Initialization for Automatic Extraction of Inflamed Appendix from Ultrasonography
Kim, K.-B., Park, H. J., & Song, D. H. (2018) · Current Medical Imaging Formerly Current Medical Imaging Reviews · doi:10.2174/1573405614666180719142536 · Cited by 8
“There have been several successful attempts of developing such automatic tools in soft tissue tumor extraction [13, 14], ganglion cyst detection [15], muscle extractions related to cervical vertebrae [16, 17], measuring carotid artery Intima-Media Thickness [18], and detecting breast cancer cells [19].”
Background
Improved Inception V3 method and its effect on radiologists’ performance of tumor classification with automated breast ultrasound system
Zhang, P., Ma, Z., Zhang, Y., Chen, X., & Wang, G. (2021) · Gland Surgery · doi:10.21037/gs-21-328 · Cited by 8
“w 0 x b → → • + = [26] The hypothesis model of logistic regression was as follows, where X is the sample input, hӨ(X)is the output of the model, and Ө is the model parameter of the required solution.”
BackgroundSelf-citation
Tumor saliency estimation for breast ultrasound images via breast anatomy modeling
Xu, F., Zhang, Y., Cheng, H., Zhang, B., Ding, J., Ning, C., & Wang, Y. (2021) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2021.102155 · Cited by 9
“In clinical routine, breast ultrasound (BUS) is a primary modality for cancer screening [4, 5], and automatic BUS image segmentation methods are essential for cancer diagnosis and treatment planning.”
BackgroundSelf-citation
Proposal for the detection of breast cancer at its early stage using wave-theory-based analysis in a tapered fiber structure illuminated by the Bessel–Gauss beam for enhanced sensitivity
Saha, B., Goswami, N., & Saha, A. (2023) · Journal of the Optical Society of America B · doi:10.1364/josab.479172 · Cited by 8
“Visual Affluence” in Social Photography: Applicability of Image Segmentation as a Visually Oriented Approach to Study Instagram Hashtags
Rathnayake, C., & Ntalla, I. (2020) · Social Media + Society · doi:10.1177/2056305120924758 · Cited by 8
“…image segmentation can be used to examine a variety of images, such as lung images (Skourt et al., 2018), catenary images (Wu et al., 2018), sonar images (Song et al., 2019), and breast ultrasound images (Xian et al., 2018; Xu et al., 2019) and help detect critical problems (e.g., breast cancer).”
Background
Multi-task interaction learning for accurate segmentation and classification of breast tumors in ultrasound images
Zheng, S., Li, J., Qiao, L., & Gao, X. (2025) · Physics in Medicine and Biology · doi:10.1088/1361-6560/adae4d · Cited by 7
“Breast ultrasound is a non-invasive examination method that does not cause harm or discomfort to the body (Xian et al 2018).”
Ultrasound image segmentation: A systematic review of deformable models from classical techniques to intelligent advancements
Sharma, P., Kumar, A., & Jain, S. K. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103768 · Cited by 7
Deep learning-based segmentation of breast masses using convolutional neural networks
Nastase, I.-N. A., Moldovanu, S., & Moraru, L. (2024) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2701/1/012005 · Cited by 7
“Nowadays, artificial intelligence (AI) is the leader in clinical practice, as it saves time and preforms tedious activities much faster, diminishes radiologist overload and helps less experiences practicians, in some cases [4-17].”
Background
Collaborative boundary-aware context encoding networks for error map prediction
Zhang, Z., Tian, C., Gao, X., Li, J., Jiao, Z., Wang, C., & Zhong, Z. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108515 · Cited by 7
FPGA-accelerated anisotropic diffusion filter based on SW/HW-codesign for medical images
Fredj, A. H., & Malek, J. (2021) · Journal of Real-Time Image Processing · doi:10.1007/s11554-021-01100-3 · Cited by 7
Sequential Active Contour Based on Morphological-Driven Thresholding for Ultrasound Image Segmentation of Ascites
Fallahdizcheh, A., Laroia, S., & Wang, C. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3286869 · Cited by 7
“However, these methods fail to provide accurate segmentation due to the dynamically changing shapes and locations of ascites in different patients [14],”
Method
Combinatorial active contour bilateral filter for ultrasound image segmentation
Nugroho, A., Hidayat, R., Nugroho, H. A., & Debayle, J. (2020) · Journal of medical imaging · doi:10.1117/1.jmi.7.5.057003 · Cited by 7
“8,9 13 They are self adapting to various object shapes, 13 have high potential for automatic 11,12 and adaptive adjustment, 14 and can be flexibly integrated into any framework.”
Background
Fuzzy Semantic Segmentation of Breast Ultrasound Image with Breast Anatomy ConstraintsPreprint
Huang, K., Zhang, Y., Cheng, H.-D., Xing, P., & Zhang, B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.06645 · Cited by 7
“BUS image segmentation consists of semi-automatic and fully-automatic methods [5], [6]; and it can be thresholding methods [7], [8], clustering-based methods [9], [10], watershed-based methods, graph-based methods [11], active-contour model [12], Markov model [13], neural network [14], deep…”
MethodSelf-citation
Syn-Net: A Synchronous Frequency-Perception Fusion Network for Breast Tumor Segmentation in Ultrasound Images
Zhao, G., Zhu, X., Wang, X., Yan, F., & Guo, M. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3514134 · Cited by 6
BreastCare application: Moroccan Breast cancer diagnosis through deep learning-based image segmentation and classification
Erragzi, N., Zrira, N., Lanjeri, S., Omor, Y., Jimi, A., Benmiloud, I., Sebihi, R., Latib, R., Ngote, N., Khan, H. A., et al. (2025) · Intelligence-Based Medicine · doi:10.1016/j.ibmed.2025.100254 · Cited by 6
EGTFormer: edge guided transformer for breast cancer ultrasound image segmentation
Pi, S., Ye, Z., Huang, J., Wu, Q., & Mei, L. (2025) · Signal Image and Video Processing · doi:10.1007/s11760-025-04750-z · Cited by 6
Multi-Task Breast Ultrasound Image Segmentation and Classification Using Convolutional Neural Network and Transformer
Loja, J., Mendez, A., & Huang, K. (2023) · IEEE MIT Undergraduate Research Technology Conference (URTC) · doi:10.1109/urtc60662.2023.10534928 · Cited by 6
“For instance, Xian et al. [14] discussed how radiologists’ input can aid in segmenting complex BUS cases.”
Background
CMAUNeXt: An Efficient Neural Network Based on Large Kernel and Multi-Dimensional Attention Module for Breast Tumor Segmentation
Prasetyo, H., Ashidiqy, R. B., & Salamah, U. (2024) · International Conference on Smart Mechatronics (ICSMech) · doi:10.1109/icsmech62936.2024.10812276 · Cited by 6
MulFF-Net: A Domain-Aware Multiscale Feature Fusion Network for Breast Ultrasound Image Segmentation With Radiomic Applications
Halder, A., & Mahadevappa, M. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3566814 · Cited by 7
“Traditionally, ultrasound image segmentation relied on conventional computer vision methods [6] such as deformable models [7], thresholding [8], and region growing.”
Weakly-Supervised Ultrasound Video Segmentation with Minimal Annotations
Chang, R., Wang, D., Guo, H., Ding, J., & Wang, L. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87237-3_62 · Cited by 6
Self-citation
Deep Semantic Segmentation of Echocardiographic Images using Vision Transformers
Bosco, E., Casula, F., Cotogni, M., Cusano, C., & Matrone, G. C. (2023) · IEEE International Ultrasonics Symposium · doi:10.1109/ius51837.2023.10306917 · Cited by 8
“An extensive overview of applications on ultrasound images can be found in [12], [13] and some of these focused on exploring DL segmentation methods for different anatomical regions [11], [14]–[16].”
Background
US-Net: A Breast Ultrasound Image Segmentation using Deep Learning
Erragzi, N., Zrira, N., Jimi, A., Benmiloud, I., Sebihi, R., & Ngote, N. (2023) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1145/3625007.3627304 · Cited by 6
Recognition of Heterogeneous Edges in Multiwavelength Transmission Images Based on the Weighted Constraint Decision Method
Liu, F., Li, G., Yang, S., Yan, W., He, G., & Lin, L. (2020) · Applied Spectroscopy · doi:10.1177/0003702820908951 · Cited by 6
The Classification and Segmentation of Fetal Anatomies Ultrasound Image: A Survey
Song, C., Gao, T., Wang, H., Sudirman, S., Zhang, W., & Zhu, H. (2020) · Journal of Medical Imaging and Health Informatics · doi:10.1166/jmihi.2021.3616 · Cited by 6
Automatic construction of filter tree by genetic programming for ultrasound guidance image segmentation
Yuan, D., Zhang, D., Yang, Y. Z., & Yang, S. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.103641 · Cited by 6
A Novel Lesion Segmentation Method based on Breast Ultrasound Images
Shen, X., Liu, J., Li, H., Sun, H., & Ma, H. (2019) · International Conference on Biomedical Imaging, Signal Processing (ICBSP) · doi:10.1145/3366174.3366176 · Cited by 6
Image Segmentation Based on Velocity Feature Vector for Moving Target Extraction
Hu, Y. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.2974314 · Cited by 6
“He believes that three separate levels are needed to express the processing of visual information [9].”
Background
DC-Net: Decomposing and coupling saliency map for lesion segmentation in ultrasound images
Ning, Z., Mao, Y., Xu, X., Feng, Q., Zhong, S., & Zhang, Y. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110355 · Cited by 6
Self-citation
A two-stage progressive deep segmentation network for tumor detection in breast ultrasound images
Zaidkilani, N., Abdel‐Nasser, M., García, M. Á., & Puig, D. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-20465-8 · Cited by 5
Weakly-supervised deep learning for breast tumor segmentation in ultrasound images
Li, Y., Liu, Y., Wang, Z., & Luo, J. (2021) · IEEE International Ultrasonics Symposium · doi:10.1109/ius52206.2021.9593692 · Cited by 5
“Many methods have been proposed for breast tumor segmentation on breast ultrasound (BUS) images [1] and deep learning-based methods have achieved state-of-the-art performance in recent years [2].”
Background
Deep learning for ultrasound medical images: artificial life variant
Karunanayake, N., & Makhanov, S. S. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-09910-9 · Cited by 5
“We refer the reader to the recent works [38–41] and the surveys [42, 43].”
Automatic Characterizations of Lumbar Multifidus Muscle and Intramuscular Fat with Fuzzy C-means based Quantization from Ultrasound Images
Kim, K.-B., Park, H. J., & Song, D. H. (2018) · Current Medical Imaging Formerly Current Medical Imaging Reviews · doi:10.2174/1573405615666181224141358 · Cited by 5
A Fusion Method in Frequency Domain for Multi-Wavelength Transmission Image
Liu, F., Li, G., Yan, W., He, G., Yang, S., & Lin, L. (2019) · IEEE Access · doi:10.1109/access.2019.2954694 · Cited by 5
“The imaging techniques currently used for breast cancer detection mainly include ultrasound [4]–[6], X-ray [7]–[9],computed tomography (CT) [10], magnetic resonance image (MRI) [11], [12] and near infrared transmission imaging [13]–[15].”
Method
Multi-atlas based segmentation of human cerebellum
Daly, A., Yazid, H., Solaiman, B., & Amara, N. E. B. (2020) · International Multi-Conference on Systems, Signals & Devices (SSD) · doi:10.1109/ssd49366.2020.9364095 · Cited by 5
Group Shift Pointwise Convolution for Volumetric Medical Image Segmentation
He, J., Ye, J., Li, C., Song, D., Chen, W., Wang, S., Gu, L., & Qiao, Y. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87199-4_5 · Cited by 6
Wave-theory-based analysis of a fiber optic bio-sensor illuminated by radially polarized Bessel–Gauss beam: an approach for early diagnosis of breast cancer with a high-resolution wavelength-interrogation technique
Saha, B., Goswami, N., & Saha, A. (2022) · Applied Optics · doi:10.1364/ao.471283 · Cited by 5
CAD System for Breast US Images with Speckle Noise Reduction and Bio-Inspired Segmentation
Rodrigues, P. S., Wachs-Lopes, G. A., Giraldi, G. A., Barcelos, C. A. Z., Vieira, L. E. S. R., Guliato, D., & Singh, B. K. (2019) · SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) · doi:10.1109/sibgrapi.2019.00018 · Cited by 5
“In this section, we analyze related methods, supported by recent surveys [12], [13], highlighting their strengths, weaknesses and significant observations.”
Method
A Framework to Figure Out Breast Cancer Cells Using Ultrasound Images
Sultana, B., Rahman, F., Nesar, H. A., & Sazzad, T. M. S. (2020) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt49239.2020.9225640 · Cited by 5
“8: (a) Test Images, (b) Proposed by [17], (c) This study”
BackgroundMethod
Effective diagnostic model construction based on discriminative breast ultrasound image regions using deep feature extraction
Yu, H., Sun, H., Li, J., Shi, L., Bao, N., Li, H., Qian, W., & Zhou, S. (2021) · Medical Physics · doi:10.1002/mp.14832 · Cited by 5
Edge-Driven Multi-Agent Reinforcement Learning: A Novel Approach to Ultrasound Breast Tumor Segmentation
Karunanayake, N., Moodleah, S., & Makhanov, S. S. (2023) · Diagnostics · doi:10.3390/diagnostics13243611 · Cited by 4
“The latest survey [11] considers the deformable models of the most frequently used approach for segmentation of the US images.”
Background
Breast tumor segmentation in ultrasound images: comparing U-net and U-net + +
de Oliveira, C. E. G., Vieira, S. L., Paranaiba, C. F. B., & Itikawa, E. N. (2025) · Research on Biomedical Engineering · doi:10.1007/s42600-025-00402-w · Cited by 5
“However, considering only US images, there is difficulty in accurately delineating the region containing tumors due to image quality (Xian et al. 2018).”
MOM-BUS: a multi-output framework for precise breast lesion segmentation in ultrasound images
Wang, X., Monkam, P., Qi, S., Liu, C., Zhao, D., Yu, T., & Qian, W. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/add28a · Cited by 4
“Pioneering segmentation techniques include threshold segmentation [15] and region growing [16] approaches.”
Hybrid Between Ontology and Quantum Particle Swarm Optimization for Segmenting Noisy Plant Disease Image
Elsayed, E. K., & Aly, M. (2020) · International Journal of Systems Applications Engineering & Development · doi:10.46300/91015.2020.14.11 · Cited by 4
TS-GCN: A novel tumor segmentation method integrating transformer and GCN
Song, H., Liu, C., Li, S., & Zhang, P. (2023) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2023807 · Cited by 4
A Secured and Continuously Developing Methodology for Breast Cancer Image Segmentation via U-Net Based Architecture and Distributed Data Training
Aoyon, R. S., Hossain, I., Abdullah-Al-Wadud, M., & Uddin, J. (2025) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2025.060917 · Cited by 4
CSC-PA: Cross-image Semantic Correlation via Prototype Attentions for Single-network Semi-supervised Breast Tumor Segmentation
Ding, Z., Chen, G., Zhang, Q., Wu, H., & Qin, J. (2025) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52734.2025.01457 · Cited by 4
“Early detection and diagnosis play a crucial role in improving patients’ quality of life, extending their lifespan, and reducing the mortality rate [8, 32].”
A Multi-model Organ Segmentation Method Based on Abdominal Ultrasound Image
Li, D., Huanhuan, M., Jiang, Y., & Shen, Y. (2020) · International Conference on Signal Processing · doi:10.1109/icsp48669.2020.9320910 · Cited by 4
“In the field of ultrasonic image segmentation, researchers have introduced neural network, mathematical morphology and other theories, and achieved a series of results in the segmentation of different objects[4-6].”
Method
Gallbladder CT Image Segmentation by Integrating Rough Entropy Thresholding with Contours
Jitani, N., Singha, B. J., Barman, G. G., Talukdar, A., Choudhury, B. K., Sarmah, R., & Bhattacharyya, D. K. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-981-16-4369-9_62 · Cited by 4
Robust Denoising Technique for Ultrasound Images Based on Weighted Nuclear Norm Minimization
Basha, S. M., & Jinaga, B. C. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-1286-5_66 · Cited by 4
A novel approach for breast tumor segmentation using multi-resolution analysis through wavelet transform
Jasrotia, H., Singh, C., & Kaur, S. (2026) · Soft Computing · doi:10.1007/s00500-025-11060-6 · Cited by 3
Breast Ultrasound Image Segmentation Algorithm Using Adaptive Region Growing and Variation Level Sets
Wang, Y., Ye, J., Wang, T., Liu, J., Dong, H., & Qiao, X. (2022) · Mathematical Problems in Engineering · doi:10.1155/2022/1752390 · Cited by 3
Enhancement of Ultrasound Imaging: A Novel Approach Using Image Processing Algorithms
Parmar, M., & Patel, H. K. (2024) · International Conference on Control, Automation and Information Sciences (ICCAIS) · doi:10.1109/iccais63750.2024.10814256 · Cited by 3
“But distinguishing between the foreground and background areas with enough accuracy is a crucial challenge, particularly when working with complex imagery like markers on ultrasound images, and it is used for the morphological gradient to highlight the edges around the ultrasound images [20].”

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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