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.
Kupinski, 1998, Automated seeded lesion segmentation on digital mammograms, IEEE Trans. Med. Imaging, 17, 510, 10.1109/42.730396 · DOI
P.O. Glauner, Deep convolutional neural networks for smile recognition, arXiv preprint arXiv:1508.06535, 2015. · arXiv

Sorted by citations

Deep Learning in Medical Ultrasound Analysis: A Review
Liu, S., Wang, Y., Yang, X., Lei, B., Liu, L., Li, S. X., Ni, D., & Wang, T. (2019) · Engineering · doi:10.1016/j.eng.2018.11.020 · Cited by 798
“However, few focus on medical US analysis, aside from one or two papers that examine speciﬁc tasks such as breast US image segmentation [55].”
Background
AAU-Net: An Adaptive Attention U-Net for Breast Lesions Segmentation in Ultrasound Images
Chen, G., Li, L., Dai, Y., Zhang, J., & Yap, M. H. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3226268 · Cited by 402
“The segmentation of breast ultrasound (BUS) images can help us characterize and localize breast tumors, which is one of the key steps in computer-aided diagnosis (CAD) [4].”
Background
Breast mass segmentation in ultrasound with selective kernel U-Net convolutional neural network
Byra, M., Jarosik, P., Szubert, A., Galperin, M. Y., Ojeda‐Fournier, H., Olson, L. K., O'Boyle, M. K., Comstock, C., & André, M. P. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.102027 · Cited by 328
“However, automatic segmentation in US imaging is considered a challenging task due to relatively low US image contrast, speckle noise and large variations in breast mass sizes and shapes [3,4].”
Background
Deep learning in ultrasound imaging
van Sloun, R. J. G., Cohen, R., & Eldar, Y. C. (2020) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2932116 · Cited by 316
“Similar applications of deep learning include automated identification and segmentation of tumors in breast ultrasound [106], [107], [108], localization of clinically relevant B-line artifacts in lung ultrasonography [109], and”
Background
Segmentation of breast ultrasound image with semantic classification of superpixels
Huang, Q., Huang, Y., Luo, Y., Yuan, F., & Li, X. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101657 · Cited by 222
Attention-Enriched Deep Learning Model for Breast Tumor Segmentation in Ultrasound Images
Vakanski, A., Xian, M., & Freer, P. E. (2020) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2020.06.015 · Cited by 198
Self-citation
Rethinking the unpretentious U-net for medical ultrasound image segmentation
Chen, G., Li, L., Zhang, J., & Dai, Y. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109728 · Cited by 204
CAD and AI for breast cancer—recent development and challenges
Chan, H., Samala, R. K., & Hadjiiski, L. M. (2019) · British Journal of Radiology · doi:10.1259/bjr.20190580 · Cited by 183
SMU-Net: Saliency-Guided Morphology-Aware U-Net for Breast Lesion Segmentation in Ultrasound Image
Ning, Z., Zhong, S., Feng, Q., Chen, W., & Zhang, Y. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3116087 · Cited by 176
“Currently, ultrasound imaging is a well-established tool used for early evaluation and diagnosis of abnormal breast due to its noninvasive imaging procedure, high sensitivity and low cost [3], [4].”
BackgroundMethodSelf-citation
TTCNN: A Breast Cancer Detection and Classification towards Computer-Aided Diagnosis Using Digital Mammography in Early Stages
Maqsood, S., Damaševičius, R., & Maskeliūnas, R. (2022) · Applied Sciences · doi:10.3390/app12073273 · Cited by 165
“Breast cancer has been among the major cause of death among women worldwide [1,2].”
Background
BUS‐BRA: A breast ultrasound dataset for assessing computer‐aided diagnosis systems
Gómez‐Flores, W., Calas, M. J. G., & Pereira, W. C. D. A. (2023) · Medical Physics · doi:10.1002/mp.16812 · Cited by 182
Sparse autoencoder for unsupervised nucleus detection and representation in histopathology images
Hou, L., Nguyen, V., Kanevsky, A. B., Samaras, D., Kurç, T., Zhao, T., Gupta, R., Gao, Y., Chen, W., Foran, D. J., et al. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.09.007 · Cited by 167
CSwin-PNet: A CNN-Swin Transformer combined pyramid network for breast lesion segmentation in ultrasound images
Yang, H., & Yang, D. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119024 · Cited by 163
Segmentation information with attention integration for classification of breast tumor in ultrasound image
Luo, Y., Huang, Q., & Li, X. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108427 · Cited by 149
A review on image-based approaches for breast cancer detection, segmentation, and classification
Rezaei, Z. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115204 · Cited by 137
Computer-aided detection of breast cancer on the Wisconsin dataset: An artificial neural networks approach
Alshayeji, M. H., Ellethy, H., Abed, S., & Gupta, R. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.103141 · Cited by 140
What is new in computer vision and artificial intelligence in medical image analysis applications
Olveres, J., González, G., Torres-Robles, F., Moreno-Tagle, J. C., Carbajal-Degante, E., Valencia-Rodríguez, A., Méndez‐Sánchez, N., & Ramírez, B. E. (2021) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-20-1151 · Cited by 134
“CAD systems using BUS images generally focus on solving three problems: lesion detection, lesion segmentation, and lesion classification.”
MethodInfluential
Fine-Tuning U-Net for Ultrasound Image Segmentation: Different Layers, Different Outcomes
Amiri, M., Brooks, R., & Rivaz, H. J. (2020) · IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · doi:10.1109/tuffc.2020.3015081 · Cited by 126
“watershed-based, graph-based, active contour model, Markov random field, and classic machine learning methods [8], [9].”
Method
Ultrasound Image-Based Diagnosis of Malignant Thyroid Nodule Using Artificial Intelligence
Nguyen, D. T., Kang, J. K., Pham, T. D., Batchuluun, G., & Park, K. R. (2020) · Sensors · doi:10.3390/s20071822 · Cited by 125
“Since each CNN model has its own architecture and characteristics of learning the characteristics of input images, the use of multiple CNN-based models can help to extract richer information compared to using an individual model.”
Method
Deep Learning to Obtain Simultaneous Image and Segmentation Outputs From a Single Input of Raw Ultrasound Channel Data
Nair, A. A., Washington, K., Tran, T. D., Reiter, A., & Bell, M. A. L. (2020) · IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · doi:10.1109/tuffc.2020.2993779 · Cited by 117
“Third, segmentation tasks are traditionally implemented after image formation [5], [6], which further increases the computational complexity of implementing segmentation algorithms to provide the desired segmentation result.”
BackgroundMethod
Advancements in Deep Learning for B-Mode Ultrasound Segmentation: A Comprehensive Review
Ansari, M. Y., Mangalote, I. A. C., Meher, P. K., Aboumarzouk, O. M., Al-Ansari, A. A., Halabi, O., & Dakua, S. P. (2024) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2024.3377676 · Cited by 117
“In recent years, some review papers on ML-based methods have been published for medical image analysis focusing on CT, MRI, or US [28], [29], [30], [31], [32], [33].”
Background
Breast tumor segmentation in 3D automatic breast ultrasound using Mask scoring R‐CNN
Lei, Y., He, X., Yao, J., Wang, T., Wang, L., Li, W., Curran, W. J., Liu, T., Xu, D. M., & Yang, X. (2020) · Medical Physics · doi:10.1002/mp.14569 · Cited by 114
Stan: Small Tumor-Aware Network for Breast Ultrasound Image Segmentation
Shareef, B., Xian, M., & Vakanski, A. (2020) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi45749.2020.9098691 · Cited by 111
“Refer to [20] for a detailed review of BUS segmentation approaches.”
BackgroundSelf-citation
Breast tumor localization and segmentation using machine learning techniques: Overview of datasets, findings, and methods
Ranjbarzadeh, R., Dorosti, S., Ghoushchi, S. J., Caputo, A., Tırkolaee, E. B., Ali, S. S., Arshadi, Z., & Bendechache, M. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106443 · Cited by 110
A comparative study of pre-trained convolutional neural networks for semantic segmentation of breast tumors in ultrasound
Gómez‐Flores, W., & Pereira, W. C. D. A. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.104036 · Cited by 108
Boundary-rendering network for breast lesion segmentation in ultrasound images
Huang, R., Lin, M., Dou, H., Lin, Z., Ying, Q., Jia, X., Xu, W., Mei, Z., Yang, X., Dong, Y., et al. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102478 · Cited by 102
ESKNet: An enhanced adaptive selection kernel convolution for ultrasound breast tumors segmentation
Chen, G., Zhou, L., Zhang, J., Yin, X., Cui, L., & Dai, Y. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123265 · Cited by 105
“Regular early screening is critical to develop the medical programme and reduce the mortality rate due to the strong concealment and many inducements of breast cancer (Xian et al., 2018).”
Background
BUSIS: A Benchmark for Breast Ultrasound Image Segmentation
Zhang, Y., Xian, M., Cheng, H.-D., Shareef, B., Ding, J., Xu, F., Huang, K., Zhang, B., Ning, C., & Wang, Y. (2022) · Healthcare · doi:10.3390/healthcare10040729 · Cited by 107
“Many semi-automatic segmentation approaches are utilized for BUS image segmentation [27].”
BackgroundMethodInfluentialSelf-citation
C-Net: Cascaded convolutional neural network with global guidance and refinement residuals for breast ultrasound images segmentation
Chen, G., Dai, Y., & Zhang, J. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.107086 · Cited by 97
Semi-supervised segmentation of lesion from breast ultrasound images with attentional generative adversarial network
Han, L., Huang, Y., Dou, H., Wang, S., Ahamad, S., Luo, H., Liu, Q., Fan, J., & Zhang, J. (2019) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2019.105275 · Cited by 95
Efficient Breast Cancer Diagnosis from Complex Mammographic Images Using Deep Convolutional Neural Network
Rahman, H., Bukht, T. F. N., Ahmad, R. B., Almadhor, A. S., & Javed, A. R. (2023) · Computational Intelligence and Neuroscience · doi:10.1155/2023/7717712 · Cited by 95
“Te predominant cause of cancer-related deaths among women globally is breast cancer [1, 2].”
Background
Artificial Intelligence-Based Thyroid Nodule Classification Using Information from Spatial and Frequency Domains
Nguyen, D. T., Pham, T. D., Batchuluun, G., Yoon, H. S., & Park, K. R. (2019) · Journal of Clinical Medicine · doi:10.3390/jcm8111976 · Cited by 93
A survey on image segmentation of blood and bone marrow smear images with emphasis to automated detection of Leukemia
Anilkumar, K., Manoj, V. J., & Sagi, T. M. (2020) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2020.08.010 · Cited by 93
Accurate segmentation of breast tumors using AE U-net with HDC model in ultrasound images
Yan, Y., Liu, Y., Wu, Y., Zhang, H., Zhang, Y., & Meng, L. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.103299 · Cited by 93
Self-citation
RCA-IUnet: a residual cross-spatial attention-guided inception U-Net model for tumor segmentation in breast ultrasound imaging
Punn, N. S., & Agarwal, S. (2022) · Machine Vision and Applications · doi:10.1007/s00138-022-01280-3 · Cited by 93
“Following this context, deep learning-based computer-aided diagnosis (CAD) systems are developed for the early detection of breast tumor for faster diagnosis and treatment [3].”
BackgroundInfluential
Ultrasound Medical Imaging Techniques
Avola, D., Cinque, L., Fagioli, A., Foresti, G. L., & Mecca, A. (2021) · ACM Computing Surveys · doi:10.1145/3447243 · Cited by 101
“In 2018, another valuable survey on breast segmentation has been also published in Reference [99].”
Background
HAU-Net: Hybrid CNN-transformer for breast ultrasound image segmentation
Zhang, H., Lian, J., Yi, Z., Wu, R., Lu, X., Ma, P., & Ma, Y. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105427 · Cited by 90
PDF-UNet: A semi-supervised method for segmentation of breast tumor images using a U-shaped pyramid-dilated network
Iqbal, A., & Sharif, M. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.119718 · Cited by 86
SHA-MTL: soft and hard attention multi-task learning for automated breast cancer ultrasound image segmentation and classification
Zhang, G., Zhao, K., Hong, Y., Qiu, X., Zhang, K., & Wei, B. (2021) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-021-02445-7 · Cited by 82
ESTAN: Enhanced Small Tumor-Aware Network for Breast Ultrasound Image Segmentation
Shareef, B., Vakanski, A., Freer, P. E., & Xian, M. (2022) · Healthcare · doi:10.3390/healthcare10112262 · Cited by 83
“We use three public BUS datasets: BUSIS [20,26,38,39], BUSI [40] and Dataset B [3].”
MethodInfluentialSelf-citation
Uncertainty-Aware Hierarchical Aggregation Network for Medical Image Segmentation
Zhou, T., Zhou, Y., Li, G., Chen, G., & Shen, J. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2024.3370685 · Cited by 74
“…[1], such as tumor segmentation in magnetic resonance (MR) images [2], polyp segmentation in colonoscopy images [3], breast lesion segmentation in ultrasound images [4], lung infection segmentation in computed tomography (CT) Images [5], nuclei segmentation in microscope cell images [6], and so on.”
Background
Multi-Level Fusion in Ultrasound for Cancer Detection based on Uniform LBP Features
Zeebaree, D. Q., Abdulazeez, A. M., Zebari, D. A., Haron, H., & Hamed, H. N. A. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2021.013314 · Cited by 73
MRFE-CNN: multi-route feature extraction model for breast tumor segmentation in Mammograms using a convolutional neural network
Ranjbarzadeh, R., Sarshar, N. T., Ghoushchi, S. J., Esfahani, M. S., Parhizkar, M., Pourasad, Y., Anari, S., & Bendechache, M. (2022) · Annals of Operations Research · doi:10.1007/s10479-022-04755-8 · Cited by 73
“Due to the importance of early cancer detection, designing computer-aided design (CAD) systems can reduce the risk of life-threatening parts of disease (Xian et al., 2018; Abdelhaﬁz et al., 2019).”
Background
MCRNet: Multi-level context refinement network for semantic segmentation in breast ultrasound imaging
Lou, M., Meng, J., Qi, Y., Li, X., & Ma, Y. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.10.102 · Cited by 72
A Regional-Attentive Multi-Task Learning Framework for Breast Ultrasound Image Segmentation and Classification
Xu, M., Huang, K., & Qi, X. (2023) · IEEE Access · doi:10.1109/access.2023.3236693 · Cited by 71
“We employ commonly-used BUS segmentation metrics [9], [15], [17], [18], [30], [31] including sensitivity (SEN), specificity (SPE), accuracy (ACC), dice similarity coefficient (DSC), and intersection over the union of tumor (tumor IoU) to quantitatively evaluate the segmentation performance.”
Method
A method for segmentation of tumors in breast ultrasound images using the variant enhanced deep learning
Ilesanmi, A. E., Chaumrattanakul, U., & Makhanov, S. S. (2021) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2021.05.007 · Cited by 66
Deep weakly-supervised breast tumor segmentation in ultrasound images with explicit anatomical constraints
Li, Y., Liu, Y., Huang, L., Wang, Z., & Luo, J. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102315 · Cited by 66
Automatic superpixel-based segmentation method for breast ultrasound images
Daoud, M. I., Atallah, A. A., Awwad, F. R., Al-Najjar, M., & Alazrai, R. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.11.024 · Cited by 65
Accurate and automatic tooth image segmentation model with deep convolutional neural networks and level set method
Yang, Y., Xie, R., Jia, W., Chen, Z., Yang, Y., Xie, L., & Jiang, B. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.07.110 · Cited by 65
DSEU-net: A novel deep supervision SEU-net for medical ultrasound image segmentation
Chen, G., Liu, Y., Jiang, Q., Zhang, J., Yin, X., Cui, L., & Dai, Y. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.119939 · Cited by 65
Ultrasound image segmentation using a novel multi-scale Gaussian kernel fuzzy clustering and multi-scale vector field convolution
Panigrahi, L., Verma, K., & Singh, B. K. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.08.013 · Cited by 64
RRCNet: Refinement residual convolutional network for breast ultrasound images segmentation
Chen, G., Dai, Y., & Zhang, J. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105601 · Cited by 64
Semantic segmentation of breast ultrasound image with fuzzy deep learning network and breast anatomy constraints
Huang, K., Zhang, Y., Cheng, H., Xing, P., & Zhang, B. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.04.012 · Cited by 73
Self-citation
MDF-Net: A Multi-Scale Dynamic Fusion Network for Breast Tumor Segmentation of Ultrasound Images
Qi, W., Wu, H. C., & Chan, S. C. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3304518 · Cited by 60
“However, low contrast and image intensity of BUS, intersubject variability, and speckle noise still present great challenge to cancer tissue segmentation [15], [16], [17] (c.”
Background
Weakly-supervised deep learning for ultrasound diagnosis of breast cancer
Kim, J., Kim, H. J., Kim, C., Lee, J. H., Kim, K. W., Park, Y. M., Ki, S. Y., Ki, S. Y., Kim, Y. M., & Kim, W. H. (2021) · Scientific Reports · doi:10.1038/s41598-021-03806-7 · Cited by 61
“Automated segmentation has been suggested in various techniques including graph-based approaches, deformable models, intensity-based thresh-olding, region growing, and watershed algorithm 28 .”
Method
BIRADS features-oriented semi-supervised deep learning for breast ultrasound computer-aided diagnosis
Zhang, E., Seiler, S. J., Chen, M., Lu, W., & Gu, X. (2020) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ab7e7d · Cited by 65
Identification of Breast Malignancy by Marker-Controlled Watershed Transformation and Hybrid Feature Set for Healthcare
Sadad, T., Hussain, A., Munir, A., Habib, M., Khan, S. A., Hussain, S., Yang, S., & Alawairdhi, M. (2020) · Applied Sciences · doi:10.3390/app10061900 · Cited by 57
“Global thresholding cannot perform well in noisy and low contrast images [10].”
BackgroundMethod
Improved U-net MALF model for lesion segmentation in breast ultrasound images
Tong, Y., Liu, Y., Zhao, M., Meng, L., & Zhang, J. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102721 · Cited by 58
A systematic survey of deep learning in breast cancer
Yu, X., Zhou, Q., Wang‎, S., & Zhang, Y. (2021) · International Journal of Intelligent Systems · doi:10.1002/int.22622 · Cited by 57
“In breast abnormality segmentation, numerous works deployed classical methods, but the exploration of utilizing deep learning models is quite limited.(167) Compared to traditional segmentation methods, CNN‐based segmentation methods, mainly FCNNs, turn out to 198 | YU ET AL.”
MethodSelf-citation
BT-Unet: A self-supervised learning framework for biomedical image segmentation using barlow twins with U-net models
Punn, N. S., & Agarwal, S. (2022) · Machine Learning · doi:10.1007/s10994-022-06219-3 · Cited by 61
Fully automatic tumor segmentation of breast ultrasound images with deep learning
Zhang, S., Liao, M., Wang, J., Zhu, Y., Zhang, Y., Zhang, J., Zheng, R., Lv, L., Zhu, D., Chen, H., et al. (2022) · Journal of Applied Clinical Medical Physics · doi:10.1002/acm2.13863 · Cited by 55
“6,7 Image segmentation aims to mark the abnormal regions from the background.”
BackgroundSelf-citation
Cross-Model Attention-Guided Tumor Segmentation for 3D Automated Breast Ultrasound (ABUS) Images
Zhou, Y. F., Chen, H., Li, Y., Cao, X., Wang, S., & Shen, D. G. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3081111 · Cited by 52
Breast ultrasound image segmentation: A coarse‐to‐fine fusion convolutional neural network
Wang, K., Liang, S., Zhong, S., Feng, Q., Ning, Z., & Zhang, Y. (2021) · Medical Physics · doi:10.1002/mp.15006 · Cited by 57
Self-citation
An attention‐supervised full‐resolution residual network for the segmentation of breast ultrasound images
Qu, X., Shi, Y., Hou, Y., & Jiang, J. (2020) · Medical Physics · doi:10.1002/mp.14470 · Cited by 42
“6,7 However, segmentation of BUS images has often presented difficulty because ultrasound images have strong speckle noise, low signal-to-noise ratio, intensity inhomogeneity, and other ultrasound artifacts.”
Background
Imbalanced Loss-Integrated Deep-Learning-Based Ultrasound Image Analysis for Diagnosis of Rotator-Cuff Tear
Lee, K., Kim, J.-Y., Lee, M. H., Choi, C.-H., & Hwang, J. Y. (2021) · Sensors · doi:10.3390/s21062214 · Cited by 42
“Conventional image processing algorithms based on morphological operations such as dilation and erosion have been applied to the diagnosis of locations of RCT, although their performance needs to be further improved [7,8,12].”
Method
Residual Feedback Network for Breast Lesion Segmentation in Ultrasound Image
Wang, K., Liang, S., & Zhang, Y. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87193-2_45 · Cited by 40
Self-citation
Medical Knowledge Constrained Semantic Breast Ultrasound Image Segmentation
Huang, K., Cheng, H.-D., Zhang, Y., Zhang, B., Xing, P., & Ning, C. (2018) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2018.8545272 · Cited by 36
“High-performance automatic segmentation approaches can be divided into semi-automatic and fullyautomatic segmentation approaches [3, 23].”
BackgroundSelf-citation
Swin-Net: A Swin-Transformer-Based Network Combing with Multi-Scale Features for Segmentation of Breast Tumor Ultrasound Images
Zhu, C., Chai, X., Xiao, Y., Liu, X., Zhang, R., Yang, Z., & Wang, Z. (2024) · Diagnostics · doi:10.3390/diagnostics14030269 · Cited by 38
“Traditional image segmentation methods mainly rely on low-level features such as texture and geometric features [5].”
Method
Feature Pyramid Nonlocal Network With Transform Modal Ensemble Learning for Breast Tumor Segmentation in Ultrasound Images
Tang, P., Yang, X., Yang, N., Xiang, S., & Liang, Q. (2021) · IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · doi:10.1109/tuffc.2021.3098308 · Cited by 37
A Benchmark for Breast Ultrasound Image Segmentation (BUSIS)Preprint
Xian, M., Zhang, Y., Cheng, H.-D., Xu, F., Huang, K., Zhang, B., Ding, J., Ning, C., & Wang, Y. (2018) · arXiv (Cornell University) · Cited by 36
Self-citation
Particle method for segmentation of breast tumors in ultrasound images
Karunanayake, N., Aimmanee, P., Lohitvisate, W., & Makhanov, S. S. (2019) · Mathematics and Computers in Simulation · doi:10.1016/j.matcom.2019.10.009 · Cited by 35
CGRNet: Contour-guided graph reasoning network for ambiguous biomedical image segmentation
Wang, K., Zhang, X., Lü, Y., Zhang, W., & Zhang, W. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.103621 · Cited by 34
Advances in ultrasonography: image formation and quality assessment
Hasegawa, H. (2021) · Journal of Medical Ultrasonics · doi:10.1007/s10396-021-01140-z · Cited by 33
“1 3 beamforming [100–104], ultrasound image processing such as speckle reduction [105, 106], and image segmentation [107, 108], etc.”
A dual-branch and dual attention transformer and CNN hybrid network for ultrasound image segmentation
Zhang, C., Wang, L., Wei, G., Kong, Z., & Qiu, M. (2024) · Frontiers in Physiology · doi:10.3389/fphys.2024.1432987 · Cited by 35
“These merits make it particularly suitable for various clinical needs (Huang et al., 2018), such as thyroid nodule detection (Bi et al., 2023), breast tumor screening (Xian et al., 2018), and gynecologic examinations (Drukker et al., 2020).”
Background
Multiscale superpixel method for segmentation of breast ultrasound
Ilesanmi, A. E., Idowu, O. P., & Makhanov, S. S. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103879 · Cited by 32
A hybrid enhanced attention transformer network for medical ultrasound image segmentation
Jiang, T., Xing, W., Yu, M. Y., & Ta, D. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105329 · Cited by 31
A Review on Region of Interest Segmentation Based on Clustering Techniques for Breast Cancer Ultrasound Images
Muhammad, M., Zeebaree, D. Q., Brifcani, A. M. A., Saeed, J. N., & Zebari, D. A. (2020) · Journal of Applied Science and Technology Trends · doi:10.38094/2020jastt1328 · Cited by 30
Multi-slice low-rank tensor decomposition based multi-atlas segmentation: Application to automatic pathological liver CT segmentation
Shi, C., Xian, M., Zhou, X., Wang, H., & Cheng, H.-D. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102152 · Cited by 29
“The general scheme is also applicable to other medical imaging modalities and organs ( Qin et al. (2019); Xian et al. (2018); Khaleel et al. (2018)) (Section 4.1).”
BackgroundInfluentialSelf-citation
DBL-Net: A dual-branch learning network with information from spatial and frequency domains for tumor segmentation and classification in breast ultrasound image
Zhu, C., Chai, X., Wang, Z., Xiao, Y., Zhang, R., Yang, Z., & Feng, J. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106221 · Cited by 29
Optimal segmentation of image datasets by genetic algorithms using color spaces
Cervantes, J., Cervantes, J., García‐Lamont, F., Yee-Rendón, A., Castilla, J. S. R., & Rodríguez-Mazahua, L. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121950 · Cited by 27
Enhanced Image-Based Endoscopic Pathological Site Classification Using an Ensemble of Deep Learning Models
Nguyen, D. T., Lee, M. B., Pham, T. D., Batchuluun, G., Arsalan, M., & Park, K. R. (2020) · Sensors · doi:10.3390/s20215982 · Cited by 29
“There are several types of cancer such as lung [2,3], breast [4,5], skin [6,7], stomach [8–10], colorectal (also known as colon cancer) [11–16], thyroid [17–19], and brain [20–22] cancers.”
Background
Segmentation and Quantitative Analysis of Photoacoustic Imaging: A Review
Le, T. D., Kwon, S. Y., & Lee, C. (2022) · Photonics · doi:10.3390/photonics9030176 · Cited by 27
“Based on conventional medical imaging modalities [60–62], (CT [63,64], X-ray [65,66], MRI [64,67–69], PET [70–72], and ultrasound [73–75]), segmentation and quantification have been proposed and challenged frequently in response to improvements in imaging techniques.”
Background
HCMNet: A Hybrid CNN-Mamba Network for Breast Ultrasound Segmentation for Consumer Assisted Diagnosis
Xiong, Y., Shu, X., Liu, Q., & Yuan, D. (2025) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2025.3593784 · Cited by 31
Detection of Unlabeled Polystyrene Micro- and Nanoplastics in Mammalian Tissue by Optical Photothermal Infrared Spectroscopy
Duswald, K., Pichler, V., Kopatz, V., Limberger, T., Karl, V., Hennerbichler, D., Zimmerleiter, R., Wadsak, W., Hettich, M., Gruber, E. S., et al. (2025) · Analytical Chemistry · doi:10.1021/acs.analchem.4c05400 · Cited by 25
An Adaptive Region Growing Based on Neutrosophic Set in Ultrasound Domain for Image Segmentation
Jiang, X., Guo, Y., Chen, H., Zhang, Y., & Lu, Y. (2019) · IEEE Access · doi:10.1109/access.2019.2911560 · Cited by 22
“graph-based approaches and learning-based approaches [1].”
BackgroundSelf-citation
Region of Interest Segmentation Based on Clustering Techniques for Breast Cancer Ultrasound Images: A Review
Muhammad, M., Zeebaree, D. Q., Abdulazeez, A. M., Saeed, J. N., & Zebari, D. A. (2020) · Journal of Applied Science and Technology Trends · doi:10.38094/jastt20201328 · Cited by 22
A SURVEY OF ULTRASONOGRAPHY BREAST CANCER IMAGE SEGMENTATION TECHNIQUES
Saeed, J. N. (2020) · Academic Journal of Nawroz University · doi:10.25007/ajnu.v9n1a523 · Cited by 21
“Specifically, techniques including unsupervised and supervised learning have been utilized to perform segmentation of BUS images [59].”
Method
A robust semantic lung segmentation study for CNN-based COVID-19 diagnosis
Aslan, M. F. (2022) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2022.104695 · Cited by 22
“Among these, the medical field that includes applications such as brain tumor segmentation [32], liver segmentation [33], breast image segmentation [34], etc. stands out.”
Effective hybrid attention network based on pseudo-color enhancement in ultrasound image segmentation
Huang, X., Wang, Q., Chen, J., Chen, L., & Chen, Z. (2023) · Image and Vision Computing · doi:10.1016/j.imavis.2023.104742 · Cited by 20
A dual-stage transformer and MLP-based network for breast ultrasound image segmentation
Lin, G., Chen, M., Tan, M., Chen, L., & Chen, J. (2023) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2023.09.001 · Cited by 21
Saliency-guided automatic detection and segmentation of tumor in breast ultrasound images
Ramadan, H., Lachqar, C., & Tairi, H. (2020) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2020.101945 · Cited by 20
A Review of Segmentation Algorithms Applied to B-Mode Breast Ultrasound Images: A Characterization Approach
Kriti, N. A., Virmani, J., & Agarwal, R. (2020) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-020-09469-3 · Cited by 19
“From Table 8, it can also be noted that studies in [248, 249] have been carried out in the past that focus solely on the segmentation of breast tumors from ultrasound images while others describe segmentation as one of the steps among others for diagnosis of breast tumors.”
BackgroundMethod
Supervised and Semi-supervised Methods for Abdominal Organ Segmentation: A Review
Senkyire, I. B., & Liu, Z. (2021) · Machine Intelligence Research · doi:10.1007/s11633-021-1313-0 · Cited by 19
Artificial life for segmentation of fusion ultrasound images of breast abnormalities
Karunanayake, N., Lohitvisate, W., & Makhanov, S. S. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108838 · Cited by 19
Multimodal image feature fusion for improving medical ultrasound image segmentation
Chen, J., & Chen, J. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105705 · Cited by 19
EU2-Net: A Parameter Efficient Ensemble Model With Attention-Aided Triple Feature Fusion for Tumor Segmentation in Breast Ultrasound Images
Roy, A., Pramanik, P., & Sarkar, R. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3421436 · Cited by 19
“The segmentation of breast ultrasound (BUS) images plays a crucial role in characterizing and localizing breast tumors, representing a critical step in computer-aided diagnosis (CAD) [2].”
Background
Wavelet-based computationally-efficient computer-aided characterization of liver steatosis using conventional B-mode ultrasound images
Amin, M. N., Rushdi, M. A., Marzaban, R. N., Yosry, A., Kim, K., & Mahmoud, A. M. (2019) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2019.03.010 · Cited by 19
“…were lost because of unavailability of accurate and low-cost fatty liver detection techniques and devices; high performance approaches consume much less resources than traditional approaches, and is vitally important to provide an affordable means for early detection of fatty liver disease [57].”
Background
A novel breast ultrasound image automated segmentation algorithm based on seeded region growing integrating gradual equipartition threshold
Fan, H., Meng, F., Liu, Y., Kong, F., Ma, J., & Lv, Z. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-07884-8 · Cited by 18
“It is very difficult to identify threshold from ultrasound images by using histogram [13].”
BackgroundMethod
Syntactic pattern recognition-based diagnostics of fetal palates
Jurek, J., Wójtowicz, W., & Wójtowicz, A. (2020) · Pattern Recognition Letters · doi:10.1016/j.patrec.2020.02.023 · Cited by 17
Classification of Heterogeneity on Multi-Spectral Transmission Image Based on Modulation-Demodulation-Frame Accumulation and Pattern Recognition
Liu, F., Li, G., Yan, W., He, G., Yang, S., & Lin, L. (2019) · IEEE Access · doi:10.1109/access.2019.2930296 · Cited by 16
“Currently, imaging techniques for detecting breast cancer mainly include x-ray [4]–[6], ultrasound [7]–[9], magnetic”
Method

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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