Ultrasound image segmentation: a survey

By Alison Noble, Djamal Boukerroui. IEEE Transactions on Medical Imaging, 2006

1,241 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/af0eb96edf68ff8a1617170da61ddb435c60cdbd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Lin Shi
2. Yanhui Guo
3. Dong Ni
4. Bo Peng
5. Jianxun Zhang
6. Pierre-Marc Jodoin
7. Shawn Li
8. Yu Dai

In Computer Vision and Pattern Recognition

1. David Dapeng Zhang
2. Lei Zhang
3. Bo Peng
4. Yanhui Guo
5. Lin Shi
6. Heng-Da Cheng
7. Dong Ni
8. Jianxun Zhang

In Radiology, Nuclear Medicine and Imaging

1. Jianxun Zhang
2. Yu Dai
3. Gongping Chen
4. Moi Hoon Yap
5. Lei Zhang
6. Xiahai Zhuang
7. David Dapeng Zhang
8. Xin Yang

Citations in the corpus

• Automated breast cancer detection and classification using ultrasound images: A survey (2015, methodology): “The segmentation results can be further enhanced via the application of maximum a posteriori segmentation estimation scheme based on the Bayesian learning paradigm [102].”
• Multivariate Mixture Model for Myocardial Segmentation Combining Multi-Source Images (2016, background): “SEGMENTATION from medical images is an essential prerequisite in a number of imaging data assisted medical applications, such as in the localization and quantification of tissues and pathologies, and modeling of anatomical structures [1], [2], [3].”
• A survey of graph theoretical approaches to image segmentation (2013, background): “The reviews of various segmentation techniques can be found for image thresholding methods [24], medical image segmentation [25,26], statistical level set segmentation [27], 3D image segmentation [28], edge detection techniques [29] and so on.”
• Deep Learning for Cardiac Image Segmentation: A Review (2019, neutral): “While a number of traditional methods, such as active contours, level-sets and active shape models have been employed to automate the segmentation of anatomical structures in ultrasound images (212), the achieved accuracy is limited by various problems of ultrasound imaging, such as low signal-tonoi”
• Shadow Detection for Ultrasound Images Using Unlabeled Data and Synthetic Shadows (2019, background): “To alleviate this, there have been many attempts to support diagnosing by image recognition (Noble and Boukerroui, 2006; Cheng et al., 2010).”
• A survey of GPU-based medical image computing techniques. (2012, methodology): “The comprehensive reviews of this LSA and their associated numerical techniques were documented in the medical imaging literatures (29,30,33).”
• Deep Learning for Segmentation Using an Open Large-Scale Dataset in 2D Echocardiography (2019, background): “have been proposed, both in 2D [6], [7] and 3D [3], [8].”
• Deep Learning in Medical Ultrasound Analysis: A Review (2019, methodology): “In current clinical practice, medical US has been applied to specialties such as echocardiography, breast US, abdominal US, transrectal US, intravascular US, and prenatal diagnosis US, which is specially used in obstetrics and gynecology (OB-GYN) [2].”
• AAU-Net: An Adaptive Attention U-Net for Breast Lesions Segmentation in Ultrasound Images (2022, background): “Due to the flexibility and convenience of ultrasound imaging, it has become a convention modality for breast tumors screening [3].”