# Parallel Network with Channel Attention and Post-Processing for Carotid Arteries Vulnerable Plaque Segmentation in Ultrasound Images

arXiv: https://arxiv.org/abs/2204.08127 · HTML: https://mlsanity.com/paper/2204.08127/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 46 references that are in the map.
- First posted on arXiv 18 Apr 2022.

## Builds on (most cited first, 12 of 12)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [One-Shot Learning for Semantic Segmentation](https://mlsanity.com/paper/1709.03410/) (2017)
- [Dilated Residual Networks](https://mlsanity.com/paper/1705.09914/) (2017)
- [Large Kernel Matters -- Improve Semantic Segmentation by Global Convolutional Network](https://mlsanity.com/paper/1703.02719/) (2017)
- [CRNet: Cross-Reference Networks for Few-Shot Segmentation](https://mlsanity.com/paper/2003.10658/) (2020)
- [Segmentation of common and internal carotid arteries from 3D ultrasound images using adaptive triple U-Net](https://mlsanity.com/paper/2101.11252/) (2021)

## Abstract (arXiv)

Carotid arteries vulnerable plaques are a crucial factor in the screening of atherosclerosis by ultrasound technique. However, the plaques are contaminated by various noises such as artifact, speckle noise, and manual segmentation may be time-consuming. This paper proposes an automatic convolutional neural network (CNN) method for plaque segmentation in carotid ultrasound images using a small dataset. First, a parallel network with three independent scale decoders is utilized as our base segmentation network, pyramid dilation convolutions are used to enlarge receptive fields in the three segmentation sub-networks. Subsequently, the three decoders are merged to be rectified in channels by SENet. Thirdly, in test stage, the initially segmented plaque is refined by the max contour morphology post-processing to obtain the final plaque. Moreover, three loss function Dice loss, SSIM loss and cross-entropy loss are compared to segment plaques. Test results show that the proposed method with dice loss function yields a Dice value of 0.820, an IoU of 0.701, Acc of 0.969, and modified Hausdorff distance (MHD) of 1.43 for 30 vulnerable cases of plaques, it outperforms some of the conventional CNN-based methods on these metrics. Additionally, we apply an ablation experiment to show the validity of each proposed module. Our study provides some reference for similar researches and may be useful in actual applications for plaque segmentation of ultrasound carotid arteries.