Feasibility Study of Lesion Detection Using Deformable Part Models in Breast Ultrasound Images

2013 ◽  
pp. 269-276 ◽  
Author(s):  
Gerard Pons ◽  
Robert Martí ◽  
Sergi Ganau ◽  
Melcior Sentís ◽  
Joan Martí
Keyword(s):  
Feasibility Study ◽  
Breast Ultrasound ◽  
Lesion Detection ◽  
Ultrasound Images ◽  
Deformable Part Models

Automatic lesion detection and segmentation algorithm on 2D breast ultrasound images

2011 ◽  
Author(s):  
Donghoon Yu ◽  
Sooyeul Lee ◽  
Jeong Won Lee ◽  
Seunghwan Kim
Keyword(s):  
Breast Ultrasound ◽  
Lesion Detection ◽  
Segmentation Algorithm ◽  
Ultrasound Images

Nipple Localization in Automated Whole Breast Ultrasound Coronal Scans Using Ensemble Learning

2020 ◽  
Vol 43 (1) ◽  
pp. 29-45
Author(s):  
Alex Noel Joseph Raj ◽  
Ruban Nersisson ◽  
Vijayalakshmi G. V. Mahesh ◽  
Zhemin Zhuang
Keyword(s):  
Texture Features ◽  
Breast Ultrasound ◽  
Support Vector ◽  
Ultrasound Images ◽  
Shadow Area ◽  
Hu Moments ◽  
Artificial Neural Network Ann ◽  
Cade System ◽  
Occurrence Matrix ◽  
Shape Complexity

Nipple is a vital landmark in the breast lesion diagnosis. Although there are advanced computer-aided detection (CADe) systems for nipple detection in breast mediolateral oblique (MLO) views of mammogram images, few academic works address the coronal views of breast ultrasound (BUS) images. This paper addresses a novel CADe system to locate the Nipple Shadow Area (NSA) in ultrasound images. Here the Hu Moments and Gray-level Co-occurrence Matrix (GLCM) were calculated through an iterative sliding window for the extraction of shape and texture features. These features are then concatenated and fed into an Artificial Neural Network (ANN) to obtain probable NSA’s. Later, contour features, such as shape complexity through fractal dimension, edge distance from the periphery and contour area, were computed and passed into a Support Vector Machine (SVM) to identify the accurate NSA in each case. The coronal plane BUS dataset is built upon our own, which consists of 64 images from 13 patients. The test results show that the proposed CADe system achieves 91.99% accuracy, 97.55% specificity, 82.46% sensitivity and 88% F-score on our dataset.

The lesion detection efficacy of deep learning on automatic breast ultrasound and factors affecting its efficacy: a pilot study

2021 ◽  
Author(s):  
Xiao Luo PhD ◽  
Min Xu ◽  
Guoxue Tang ◽  
Yi Wang PhD ◽  
Na Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Breast Tissue ◽  
Test Group ◽  
Lesion Size ◽  
Breast Ultrasound ◽  
Lesion Detection ◽  
Detection Sensitivity ◽  
Control Group ◽  
Factors Affecting ◽  
Handheld Ultrasound

Objectives: The aim of this study was to investigate the detection efficacy of deep learning (DL) for automatic breast ultrasound (ABUS) and factors affecting its efficacy. Methods: Women who underwent ABUS and handheld ultrasound from May 2016 to June 2017 (N = 397) were enrolled and divided into training (n = 163 patients with breast cancer and 33 with benign lesions), test (n = 57) and control (n = 144) groups. A convolutional neural network was optimised to detect lesions in ABUS. The sensitivity and false positives (FPs) were evaluated and compared for different breast tissue compositions, lesion sizes, morphologies and echo patterns. Results: In the training set, with 688 lesion regions (LRs), the network achieved sensitivities of 93.8%, 97.2 and 100%, based on volume, lesion and patient, respectively, with 1.9 FPs per volume. In the test group with 247 LRs, the sensitivities were 92.7%, 94.5 and 96.5%, respectively, with 2.4 FPs per volume. The control group, with 900 volumes, showed 0.24 FPs per volume. The sensitivity was 98% for lesions > 1 cm3, but 87% for those ≤1 cm3 (p < 0.05). Similar sensitivities and FPs were observed for different breast tissue compositions (homogeneous, 97.5%, 2.1; heterogeneous, 93.6%, 2.1), lesion morphologies (mass, 96.3%, 2.1; non-mass, 95.8%, 2.0) and echo patterns (homogeneous, 96.1%, 2.1; heterogeneous 96.8%, 2.1). Conclusions: DL had high detection sensitivity with a low FP but was affected by lesion size. Advances in knowledge: DL is technically feasible for the automatic detection of lesions in ABUS.

Automatic superpixel-based segmentation method for breast ultrasound images

2019 ◽  
Vol 121 ◽  
pp. 78-96 ◽  
Author(s):  
Mohammad I. Daoud ◽  
Ayman A. Atallah ◽  
Falah Awwad ◽  
Mahasen Al-Najjar ◽  
Rami Alazrai
Keyword(s):  
Breast Ultrasound ◽  
Ultrasound Images ◽  
Segmentation Method

3D Tendon Strain Estimation Using High-frequency Volumetric Ultrasound Images: A Feasibility Study

2017 ◽  
Vol 40 (2) ◽  
pp. 67-83 ◽  
Author(s):  
Catarina Carvalho ◽  
Pieter Slagmolen ◽  
Stijn Bogaerts ◽  
Lennart Scheys ◽  
Jan D’hooge ◽  
...  
Keyword(s):  
Feasibility Study ◽  
High Frequency ◽  
Ultrasound Images ◽  
Strain Estimation ◽  
Tendon Strain ◽  
Volumetric Ultrasound
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