Breast Tumor Ultrasound Image Segmentation Method Based on Improved Residual U-Net Network

Zhao, Tianyu; Dai, Hang

doi:10.1155/2022/3905998

Cited by 12 publications

(7 citation statements)

References 25 publications

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“…However, the GG-Net and SegResNetVAE models performed better, achieving 82.56 % and 81.90 %, respectively. Furthermore, in the research work [40,41], additional residue and attention blocks were incorporated into the basic UNet architecture to enhance segmentation. The results showed that the Dice index value was 0.921, slightly lower than that of UNet with dense input, indicating its effectiveness and the promising use of dense and input blocks in this task.…”

Section: Discussion Of Study Results Of Cancer Segmentationmentioning

confidence: 99%

Comparison evaluation of unet-based models with noise augmentation for breast cancer segmentation on ultrasound image

Mukasheva,

Koishiyeva,

Suimenbayeva

et al. 2023

EEJET

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This paper investigates the application of convolutional neural networks (CNNs), particularly the UNet model architecture, to improve the accuracy of breast cancer tumor segmentation in ultrasound images. Accurate identification of breast cancer is essential for effective patient treatment. However, ultrasound images, often contain noise and artifacts, which can complicate the task of tumor segmentation. Therefore, to highlight the most robust architecture, modifications were made to the original set, including the addition of noise and fuzziness. In this study, a comparative study of five different variants of UNet models (UNet, Attention UNet, UNet++, Dense Inception UNet and Residual UNet) was conducted on a diverse set of ultrasound images with different breast tumors. Using consistent training methods and techniques of augmentation and adding noise to the data, an improvement in segmentation accuracy was highlighted when using the Dense Inception UNet architecture. The results have potential practical applications in the field of medical diagnosis and can assist medical professionals in automatic tumor segmentation in breast cancer ultrasound images. This study highlights the improvement of segmentation accuracy by introducing dense induction into the UNet architecture. Importantly, the Dice coefficient, a key segmentation metric, improved markedly, increasing from 0.973 to 0.976 after data augmentation. The results of the study offer promise to the medical community by offering a more accurate and reliable approach to segmenting breast cancer lesions on ultrasound images. The findings can be implemented in clinical practice to assist radiologists in early cancer diagnosis.

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Section: Discussion Of Study Results Of Cancer Segmentationmentioning

confidence: 99%

Comparison evaluation of unet-based models with noise augmentation for breast cancer segmentation on ultrasound image

Mukasheva,

Koishiyeva,

Suimenbayeva

et al. 2023

EEJET

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show abstract

“…U-Net is a CNN, which is basically an encoder–decoder architecture for feature extraction and localization [ 53 , 54 , 55 , 56 ]. Attention U-Net is another model that was used for segmentation purposes which introduces attention layers into the U-Net to identify and focus on relevant areas such as margins or salient features of the mass to efficiently extract features [ 57 , 58 ]. SegNet is another encoder–decoder-based architecture that can provide semantic segmentation by using skip connections and preserving contextual information, improving margin delineation capability [ 59 , 60 ].…”

Section: Discussionmentioning

confidence: 99%

Deep Learning in Different Ultrasound Methods for Breast Cancer, from Diagnosis to Prognosis: Current Trends, Challenges, and an Analysis

Afrin,

Larson,

Fatemi

et al. 2023

Cancers

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Breast cancer is the second-leading cause of mortality among women around the world. Ultrasound (US) is one of the noninvasive imaging modalities used to diagnose breast lesions and monitor the prognosis of cancer patients. It has the highest sensitivity for diagnosing breast masses, but it shows increased false negativity due to its high operator dependency. Underserved areas do not have sufficient US expertise to diagnose breast lesions, resulting in delayed management of breast lesions. Deep learning neural networks may have the potential to facilitate early decision-making by physicians by rapidly yet accurately diagnosing and monitoring their prognosis. This article reviews the recent research trends on neural networks for breast mass ultrasound, including and beyond diagnosis. We discussed original research recently conducted to analyze which modes of ultrasound and which models have been used for which purposes, and where they show the best performance. Our analysis reveals that lesion classification showed the highest performance compared to those used for other purposes. We also found that fewer studies were performed for prognosis than diagnosis. We also discussed the limitations and future directions of ongoing research on neural networks for breast ultrasound.

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“…This model enhances the attention U-net network framework by incorporating residual convolution and extended residual convolution modules in the encoding path. In their work [44], utilized a residual U-Net for breast tumor segmentation, incorporating a fusion attention mechanism that combines both spatial and channel attention. In another work [45], the authors presented the RDAU-NET (Residual-Dilated-Attention-Gate-UNet) model for tumor segmentation in breast ultrasound images.…”

Section: Plos Onementioning

confidence: 99%

DAU-Net: Dual attention-aided U-Net for segmenting tumor in breast ultrasound images

Pramanik,

Roy,

Cuevas

et al. 2024

PLoS ONE

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Breast cancer remains a critical global concern, underscoring the urgent need for early detection and accurate diagnosis to improve survival rates among women. Recent developments in deep learning have shown promising potential for computer-aided detection (CAD) systems to address this challenge. In this study, a novel segmentation method based on deep learning is designed to detect tumors in breast ultrasound images. Our proposed approach combines two powerful attention mechanisms: the novel Positional Convolutional Block Attention Module (PCBAM) and Shifted Window Attention (SWA), integrated into a Residual U-Net model. The PCBAM enhances the Convolutional Block Attention Module (CBAM) by incorporating the Positional Attention Module (PAM), thereby improving the contextual information captured by CBAM and enhancing the model’s ability to capture spatial relationships within local features. Additionally, we employ SWA within the bottleneck layer of the Residual U-Net to further enhance the model’s performance. To evaluate our approach, we perform experiments using two widely used datasets of breast ultrasound images and the obtained results demonstrate its capability in accurately detecting tumors. Our approach achieves state-of-the-art performance with dice score of 74.23% and 78.58% on BUSI and UDIAT datasets, respectively in segmenting the breast tumor region, showcasing its potential to help with precise tumor detection. By leveraging the power of deep learning and integrating innovative attention mechanisms, our study contributes to the ongoing efforts to improve breast cancer detection and ultimately enhance women’s survival rates. The source code of our work can be found here: https://github.com/AyushRoy2001/DAUNet.

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Breast Tumor Ultrasound Image Segmentation Method Based on Improved Residual U-Net Network

Cited by 12 publications

References 25 publications

Comparison evaluation of unet-based models with noise augmentation for breast cancer segmentation on ultrasound image

Comparison evaluation of unet-based models with noise augmentation for breast cancer segmentation on ultrasound image

Deep Learning in Different Ultrasound Methods for Breast Cancer, from Diagnosis to Prognosis: Current Trends, Challenges, and an Analysis

DAU-Net: Dual attention-aided U-Net for segmenting tumor in breast ultrasound images

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