WDTISeg: One-Stage Interactive Segmentation for Breast Ultrasound Image Using Weighted Distance Transform and Shape-Aware Compound Loss

Accurate tumor segmentation is important for aided diagnosis using breast ultrasound. Interactive segmentation methods can obtain highly accurate results by continuously optimizing the segmentation result via user interactions. However, traditional interactive segmentation methods usually require a large number of interactions to make the result meet the requirements due to the performance limitations of the underlying model. With greater ability in extracting image information, convolutional neural network (CNN)-based interactive segmentation methods have been shown to effectively reduce the number of user interactions. In this paper, we proposed a one-stage interactive segmentation framework (interactive segmentation using weighted distance transform, WDTISeg) for breast ultrasound image using weighted distance transform and shape-aware compound loss. First, we used a pre-trained CNN to attain an initial automatic segmentation, based on which the user provided interaction points of mis-segmented areas. Then, we combined Euclidean distance transform and geodesic distance transform to convert interaction points into weighted distance maps to transfer segmentation guidance information to the model. The same CNN accepted the input image, the initial segmentation, and weighted distance maps as a concatenation input and provided a refined result, without another additional segmentation network. In addition, a shape-aware compound loss function using prior knowledge was designed to reduce the number of user interactions. In the testing phase on 200 cases, our method achieved a dice of 82.86 ± 16.22 (%) for automatic segmentation task and a dice of 94.45 ± 3.26 (%) for interactive segmentation task after 8 interactions. The results of comparative experiments proved that our method could obtain higher accuracy with fewer simple interactions than other interactive segmentation methods.

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Segmentation Methods in Ultrasound Images

Clinical Technologies ◽

10.4018/978-1-60960-561-2.ch210 ◽

2011 ◽

pp. 377-390

Author(s):

Farhang Sahba

Keyword(s):

Image Segmentation ◽

Automatic Segmentation ◽

Transrectal Ultrasound ◽

Ultrasound Image ◽

The Body ◽

Ultrasound Images ◽

Sound Waves ◽

Medical Test ◽

Segmentation Methods ◽

Segmentation Task

Ultrasound imaging now has widespread clinical use. It involves exposing a part of the body to highfrequency sound waves in order to generate images of the inside of the body. Because it is a real-time procedure, the ultrasound images show the movement of the body’s internal structure as well. It is usually a painless medical test and its procedures seem to be safe. Despite recent improvement in the quality of information from an ultrasound device, these images are still a challenging case for segmentation. Thus, there is much interest in understanding how to apply an image segmentation task to ultrasound data and any improvements in this regard are desirable. Many methods have been introduced in existing literature to facilitate more accurate automatic or semi-automatic segmentation of ultrasound images. This chapter is a basic review of the works on ultrasound image segmentation classified by application areas, including segmentation of prostate transrectal ultrasound (TRUS), breast ultrasound, and intravascular ultrasound (IVUS) images.

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Segmentation Methods in Ultrasound Images

Handbook of Research on Advanced Techniques in Diagnostic Imaging and Biomedical Applications ◽

10.4018/978-1-60566-314-2.ch030 ◽

2009 ◽

pp. 473-487

Author(s):

Farhang Sahba

Keyword(s):

Image Segmentation ◽

Automatic Segmentation ◽

Transrectal Ultrasound ◽

Ultrasound Image ◽

The Body ◽

Ultrasound Images ◽

Sound Waves ◽

Medical Test ◽

Segmentation Methods ◽

Segmentation Task

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Automatic Segmentation Method of Breast Tumor Ultrasonic Images Based on Attention-Enhancing Unet

Nanoscience and Nanotechnology Letters ◽

10.1166/nnl.2020.3201 ◽

2020 ◽

Vol 12 (8) ◽

pp. 996-1005

Author(s):

Yu Yan ◽

Xiaowei Cai ◽

Ge Fang ◽

Wei Zhu ◽

Jian Liu ◽

...

Keyword(s):

Loss Function ◽

Automatic Segmentation ◽

Ultrasound Image ◽

Breast Ultrasound ◽

Fine Tuning ◽

Segmentation Method ◽

Partial Loss ◽

Lesion Area ◽

Network Training ◽

Ultrasonic Images

In order to improve the accuracy of the segmentation of the breast ultrasound image lesion, Attention-Unet was improved, and an Attention-enhancing Unet (AE-Unet) model is proposed. First, the network loss function was improved. Based on the output value of the traditional network end, output weights of all attention gate were integrated. Compared with the standard lesion template, it was used to obtain accurate network loss values; Secondly, the network training method was improved, and the strategy of combining thickness and fineness was adopted. The overall loss function was used to train the overall network to make the network basically stable; then the partial loss function was used to alternately train the backbone network and the attention gate module in turn. Fine-tuning was used to further improve the accuracy of network parameters. The combination of the two greatly improves the accuracy of segmentation of the breast ultrasound lesion area. The experimental results on the breast ultrasound data actually collected in the hospital show that the proposed AE-Unet model has an M-IOU of 81.24%, precision of 85.88%, F1 of 80.58%, Acc of 93.85% and specificity of 97.48%, PPV is up to 85.88%, which has achieved better segmentation results than existing advanced algorithms.

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Automatic segmentation algorithm of breast ultrasound image based on improved level set algorithm

2016 IEEE International Conference on Signal and Image Processing (ICSIP) ◽

10.1109/siprocess.2016.7888276 ◽

2016 ◽

Author(s):

Xilin Li ◽

Chunlan Yang ◽

Shuicai Wu

Keyword(s):

Level Set ◽

Automatic Segmentation ◽

Ultrasound Image ◽

Breast Ultrasound ◽

Segmentation Algorithm

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3‐D texture characterized by accessibility measurements, based on the grey weighted distance transform

Bioimaging ◽

10.1002/1361-6374(199403)2:1<1::aid-bio1>3.3.co;2-9 ◽

1994 ◽

Vol 2 (1) ◽

pp. 1-21 ◽

Cited By ~ 3

Author(s):

Karel C Strasters ◽

Arnold W M Smeulders ◽

Hans T M van der Voort

Keyword(s):

Distance Transform ◽

Weighted Distance

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Assessment of automatic segmentation accuracy with various point cloud density

Geodesy and Cartography ◽

10.22389/0016-7126-2020-961-7-47-55 ◽

2020 ◽

Vol 961 (7) ◽

pp. 47-55

Author(s):

A.G. Yunusov ◽

A.J. Jdeed ◽

N.S. Begliarov ◽

M.A. Elshewy

Keyword(s):

Point Cloud ◽

Laser Scanning ◽

Reference Data ◽

Automatic Segmentation ◽

Geometric Accuracy ◽

Segmentation Method ◽

Segmentation Methods ◽

Segmentation Accuracy ◽

Number Of Segments ◽

Cloud Density

Laser scanning is considered as one of the most useful and fast technologies for modelling. On the other hand, the size of scan results can vary from hundreds to several million points. As a result, the large volume of the obtained clouds leads to complication at processing the results and increases the time costs. One way to reduce the volume of a point cloud is segmentation, which reduces the amount of data from several million points to a limited number of segments. In this article, we evaluated effect on the performance, the accuracy of various segmentation methods and the geometric accuracy of the obtained models at density changes taking into account the processing time. The results of our experiment were compared with reference data in a form of comparative analysis. As a conclusion, some recommendations for choosing the best segmentation method were proposed.

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Revisiting the K-nn algorithm for obstetric image segmentation

International Journal of Hybrid Intelligent Systems ◽

10.3233/his-210001 ◽

2021 ◽

pp. 1-13

Author(s):

P. Salgado ◽

T.-P. Azevedo Perdicoúlis

Keyword(s):

Magnetic Resonance Image ◽

Medical Image ◽

Clustering Algorithm ◽

Ultrasound Image ◽

Well Being ◽

Health Conditions ◽

Medical Applications ◽

Training Set ◽

Segmentation Methods ◽

Segmentation Algorithms

Medical image techniques are used to examine and determine the well-being of the foetus during pregnancy. Digital image processing (DIP) is essential to extract valuable information embedded in most biomedical signals. After, intelligent segmentation methods, based on classifier algorithms, must be applied to identify structures and relevant features from previous data. The success of both is essential for helping doctors to identify adverse health conditions from the medical images. To obtain easy and reliable DIP methods for foetus images in real-time, at different gestational ages, aware pre-processing needs to be applied to the images. Thence, some data features are extracted that are meant to be used as input to the segmentation algorithms presented in this work. Due to the high dimension of the problems in question, assemblage of the data is also desired. The segmentation of the images is done by revisiting the K-nn algorithm that is a conventional nonparametric classifier. Besides its simplicity, its power to accomplish high classification results in medical applications has been demonstrated. In this work two versions of this algorithm are presented (i) an enhancement of the standard version by aggregating the data apriori and (ii) an iterative version of the same method where the training set (TS) is not static. The procedure is demonstrated in two experiments, where two images of different technologies were selected: a magnetic resonance image and an ultrasound image, respectively. The results were assessed by comparison with the K-means clustering algorithm, a well-known and robust method for this type of task. Both described versions showed results close to 100% matching with the ones obtained by the validation method, although the iterative version displays much higher reliability in the classification.

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