Segmentation of the Common Carotid Intima-Media Complex in Ultrasound Images Using 2-D Continuous Max-Flow and Stacked Sparse Auto-encoder

Qian, Chunjun; Su, Enjie; Yang, Xiaoping

doi:10.1016/j.ultrasmedbio.2020.07.021

Cited by 13 publications

(6 citation statements)

References 42 publications

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“…Similarly to many state-of-the-art methods [7], [10], [14], [16], the far wall is first detected to drive the subsequent actual IMC segmentation. The corresponding U-net, referred to as Θ F W , uses patches of full image height and 128-pixel width.…”

Section: A Detection Of the Far Wallmentioning

confidence: 99%

“…This task is most frequently addressed by contour-based approaches [1], [3], [11], [16], which exploit intensity changes caused by the echoes at acoustic interfaces. Region-based segmentation methods based on texture, adaptive thresholding [9], [14] learning (DL) has been successfully used in vascular USimage segmentation to enhance the structures of interest prior to the actual delineation by more conventional contour-based methods [7], [10], [13]. The drawback of these approaches is the necessity to combine a learnable pre-processing operation with an analytic segmentation task.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Segmenting the carotid-artery wall in ultrasound image sequences with a dual-resolution U-net

Lainé

Zahnd

Liebgott

et al. 2022

2022 IEEE International Ultrasonics Symposium (IUS)

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Thickening of intima-media complex in the common carotid artery is a biomarker of atherosclerosis. To automatically measure this thickness, we propose a region-based segmentation method, involving a supervised deep-learning approach based on the dilated U-net architecture, named caroSegDeep. It was trained and evaluated using 5-fold cross-validation on two open-access databases containing a total of 2676 annotated images. Compared with the methods already evaluated on these databases, caroSegDeep established a new benchmark and achieved a mean absolute error twice smaller than the interobserver variability.

show abstract

Section: A Detection Of the Far Wallmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Segmenting the carotid-artery wall in ultrasound image sequences with a dual-resolution U-net

Lainé

Zahnd

Liebgott

et al. 2022

2022 IEEE International Ultrasonics Symposium (IUS)

View full text Add to dashboard Cite

show abstract

“…Like in many state-of-the-art methods [6,9,10,11], the far wall is first detected and is considered as the initialization step. Here, the patches are of full image height and 128-pixel width, and the corresponding U-net will be referred to as Θ F W .…”

Section: Detection Of the Far Wallmentioning

confidence: 99%

“…Recently, deep-learning (DL) has been successfully used in vascular US-image segmentation to enhance the structures of interest prior to the actual delineation by more conventional contour-based methods [10,11,12]. The drawback of these approaches is the necessity to combine a learnable preprocessing operation with an analytic segmentation task.…”

Section: Introductionmentioning

confidence: 99%

Carotid artery wall segmentation in ultrasound image sequences using a deep convolutional neural network

Lainé,

Zahnd,

Liebgott

et al. 2022

Preprint

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The objective of this study is the segmentation of the intimamedia complex of the common carotid artery, on longitudinal ultrasound images, to measure its thickness. We propose a fully automatic region-based segmentation method, involving a supervised deep-learning approach based on a dilated U-net network. It was trained and evaluated using a 5-fold cross-validation on a multicenter database composed of 2176 images annotated by two experts. The resulting mean absolute difference (< 120 µm) compared to reference annotations was less than the inter-observer variability (≈ 180 µm). With a 98.7% success rate, i.e., only 1.3% cases requiring manual correction, the proposed method has been shown to be robust and thus may be recommended for use in clinical practice.

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“…Recent applications of deep learning in medical US analysis have involved various tasks (Liu et al 2019), such as multi-needle detection for US-guided prostate brachytherapy (Zhang et al 2020c), segmentation of common carotid intima-media complex in US images (Qian et al 2020), and differentiating benign and malignant lesions for detecting lung cancer (Chen et al 2019). However, the classification of breast tumors based on ultrasonography images faces several challenges.…”

Section: Introductionmentioning

confidence: 99%

A novel approach with dual-sampling convolutional neural network for ultrasound image classification of breast tumors

Xie

Song

et al. 2020

Phys. Med. Biol.

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Breast cancer is one of the leading causes of female cancer deaths. Early diagnosis with prophylactic may improve the patients’ prognosis. So far ultrasound (US) imaging has been a popular method in breast cancer diagnosis. However, its accuracy is bounded to traditional handcrafted feature methods and expertise. A novel method, named dual-sampling convolutional neural networks (DSCNNs), was proposed in this paper for the differential diagnosis of breast tumors based on US images. Combining traditional convolutional and residual networks, DSCNN prevented gradient disappearance and degradation. The prediction accuracy was increased by the parallel dual-sampling structure, which can effectively extract potential features from US images. Compared with other advanced deep learning methods and traditional handcrafted feature methods, DSCNN reached the best performance with an accuracy of 91.67% and an area under curve of 0.939. The robustness of the proposed method was also verified by using a public dataset. Moreover, DSCNN was compared with evaluation from three radiologists utilizing US-BI-RADS lexicon categories for overall breast tumors assessment. The result demonstrated that the prediction sensitivity, specificity and accuracy of the DSCNN were higher than those of the radiologist with 10 year experience, suggesting that the DSCNN has the potential to help doctors make judgements in clinic.

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Segmentation of the Common Carotid Intima-Media Complex in Ultrasound Images Using 2-D Continuous Max-Flow and Stacked Sparse Auto-encoder

Cited by 13 publications

References 42 publications

Segmenting the carotid-artery wall in ultrasound image sequences with a dual-resolution U-net

Segmenting the carotid-artery wall in ultrasound image sequences with a dual-resolution U-net

Carotid artery wall segmentation in ultrasound image sequences using a deep convolutional neural network

A novel approach with dual-sampling convolutional neural network for ultrasound image classification of breast tumors

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