A novel method for breast mass segmentation: from superpixel to subpixel segmentation

Gu, Shenghua; Chen, Yi; Sheng, Fangqing; Zhan, Tianming; Chen, Yunjie

doi:10.1007/s00138-019-01020-0

Machine Vision and Applications

2019

DOI: 10.1007/s00138-019-01020-0

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A novel method for breast mass segmentation: from superpixel to subpixel segmentation

Shenghua Gu

Yi Chen

Fangqing Sheng

et al.

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Cited by 7 publications

(1 citation statement)

References 32 publications

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“…A new CAD system was implemented by Xie et al [15], in which the level set model was used for the segmentation of preprocessed images. Gu et al [16] achieved comparatively high precision on breast mass segmentation by means of a superpixel generation and curve evolution method. Given the region of interest selected by an expert, Saleck et al [17] developed a semi-automatic method of stepwise boundaries refinement in three stages for breast mass segmentation.…”

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confidence: 99%

A Novel Multi-Scale Adversarial Networks for Precise Segmentation of X-Ray Breast Mass

Chen

Wang

et al. 2020

IEEE Access

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With the constant changes of people's lifestyle and living environment, the morbidity of breast cancer is increasing year by year. It is highly imperative to develop an effective breast mass segmentation method for early breast cancer diagnosis. However, segmenting breast masses in mammograms is still one hot issue with enormous challenges because of masses' irregular shapes and various sizes. In this study, we propose multi-adversarial learning to capture multi-scale image information for accurate breast mass segmentation. To effectively reinforce higher-order consistency in the segmentation results, the proposed network introduces the idea of adversarial networks, mainly consisting of a segmentation network and a discrimination network. An improved U-Net is introduced as the segmentation network to generate masks of the suspicious regions, while the discrimination network combines three convolutional critic networks that operate at different scales to discriminate the input masks. To weaken the unbalanced class problem and produce fine-grained segmentation results, weighted cross entropy loss and Earth-Mover distance are jointly used as an integrated loss function to guide the optimization process. Furthermore, the spectral normalization is adopted to the critics to alleviate the instability of training. The effectiveness of the proposed method is evaluated on two public datasets (INbreast and CBIS-DDSM). Experimental results empirically demonstrate that our method outperforms FCNs-based methods and the state-of-the-art method with dice of 81.64% for INbreast and 82.16% for CBIS-DDSM.

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mentioning

confidence: 99%

A Novel Multi-Scale Adversarial Networks for Precise Segmentation of X-Ray Breast Mass

Chen

Wang

et al. 2020

IEEE Access

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TransNet: a comparative study on breast carcinoma diagnosis with classical machine learning and transfer learning paradigm

Chugh,

Kumar,

Singh

2023

Multimed Tools Appl

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Early anomaly prediction in breast thermogram by hybrid model consisting of superpixel segmentation, sparse feature descriptors and extreme learning machine classifier

Sharma

Maheshwari

et al. 2021

Biomedical Signal Processing and Control

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A novel method for breast mass segmentation: from superpixel to subpixel segmentation

Cited by 7 publications

References 32 publications

A Novel Multi-Scale Adversarial Networks for Precise Segmentation of X-Ray Breast Mass

A Novel Multi-Scale Adversarial Networks for Precise Segmentation of X-Ray Breast Mass

TransNet: a comparative study on breast carcinoma diagnosis with classical machine learning and transfer learning paradigm

Early anomaly prediction in breast thermogram by hybrid model consisting of superpixel segmentation, sparse feature descriptors and extreme learning machine classifier

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