Ultrasound image segmentation by using a FIR neural network

Torbati, Nima; Ayatollahi, Ahmad; Kermani, Ali

doi:10.1109/iraniancee.2013.6599759

Cited by 4 publications

(4 citation statements)

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“…Dataset A used in this study comprises 387 US images: 208 are benign lesions and 179 are malignant lesions. Compared with other datasets previously cited and used for breast lesion segmentation, 50 images [5], 112 images [6] and 30 images [8], dataset A is the larger one.…”

Section: Discussionmentioning

confidence: 99%

“…In this study, with CNN3 and dataset A, we obtained an accuracy of 0.950 ± 0.006 and a global accuracy of 0.956 ± 0.011. Torbati et al [8], as previously cited, obtained an IoU of 86.95% using a dataset with 30 images.…”

Section: Discussionmentioning

confidence: 99%

“…The authors obtained the following results: Accuracy = 87.5%, Sensitivity = 88.8% and Specificity = 84.4%. In [8], the dataset consists of only of 30 images and the authors used self-organized maps associated with finite impulse response filters for breast US images segmentation. The authors obtained the following results: True Positives (TP) = 93.24%, False Positives (FP) = 8.41% and Intersection over Union (IoU) = 86.95%.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating the performance of convolutional neural networks with direct acyclic graph architectures in automatic segmentation of breast lesion in US images

et al. 2019

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BackgroundOutlining lesion contours in Ultra Sound (US) breast images is an important step in breast cancer diagnosis. Malignant lesions infiltrate the surrounding tissue, generating irregular contours, with spiculation and angulated margins, whereas benign lesions produce contours with a smooth outline and elliptical shape. In breast imaging, the majority of the existing publications in the literature focus on using Convolutional Neural Networks (CNNs) for segmentation and classification of lesions in mammographic images. In this study our main objective is to assess the ability of CNNs in detecting contour irregularities in breast lesions in US images.MethodsIn this study we compare the performance of two CNNs with Direct Acyclic Graph (DAG) architecture and one CNN with a series architecture for breast lesion segmentation in US images. DAG and series architectures are both feedforward networks. The difference is that a DAG architecture could have more than one path between the first layer and end layer, whereas a series architecture has only one path from the beginning layer to the end layer. The CNN architectures were evaluated with two datasets.ResultsWith the more complex DAG architecture, the following mean values were obtained for the metrics used to evaluate the segmented contours: global accuracy: 0.956; IOU: 0.876; F measure: 68.77%; Dice coefficient: 0.892.ConclusionThe CNN DAG architecture shows the best metric values used for quantitatively evaluating the segmented contours compared with the gold-standard contours. The segmented contours obtained with this architecture also have more details and irregularities, like the gold-standard contours.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluating the performance of convolutional neural networks with direct acyclic graph architectures in automatic segmentation of breast lesion in US images

et al. 2019

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“…Studies [6], [7], [8], [9], [11], [12] focus on neural networks. According to Google Trends, the current trends which includes the last five years indicate an increase in the adoption of neural networks to perform medical image segmentation and analysis [13].…”

Section: Introductionmentioning

confidence: 99%

Automatic detection of lumen and media in the IVUS images using U-Net with VGG16 Encoder

Balakrishna,

Dadashzadeh,

Soltaninejad

2018

Preprint

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Coronary heart disease is one of the top rank leading cause of mortality in the world which can be because of plaque burden inside the arteries. Intravascular Ultrasound (IVUS) has been recognized as powerful imaging technology which captures the real time and high resolution images of the coronary arteries and can be used for the analysis of these plaques. The IVUS segmentation involves the extraction of two arterial walls components namely, lumen and media. In this paper, we investigate the effectiveness of Convolutional Neural Networks including U-Net to segment ultrasound scans of arteries. In particular, the proposed segmentation network was built based on the the U-Net with the VGG16 encoder. Experiments were done for evaluating the proposed segmentation architecture which show promising quantitative and qualitative results.

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Using Convolutional Neural Networks with Direct Acyclic Graph Architecture in Segmentation of Breast Lesions in US Images

et al. 2019

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Ultrasound image segmentation by using a FIR neural network

Cited by 4 publications

References 20 publications

Evaluating the performance of convolutional neural networks with direct acyclic graph architectures in automatic segmentation of breast lesion in US images

Evaluating the performance of convolutional neural networks with direct acyclic graph architectures in automatic segmentation of breast lesion in US images

Automatic detection of lumen and media in the IVUS images using U-Net with VGG16 Encoder

Using Convolutional Neural Networks with Direct Acyclic Graph Architecture in Segmentation of Breast Lesions in US Images

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