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A dual-modality evaluation of computer-aided breast lesion segmentation in mammogram and ultrasound using customized transfer learning approach

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Abstract

Manual segmentation of breast lesions is a tedious and time-consuming task. The existing state-of-the-art studies are evaluated on single modalities, i.e., either on mammogram or ultrasound thereby having limited clinical application. The limitations stated above motivated us to develop a Computer-Aided Mammogram Segmentation (CAMS) system and Computer-Aided Ultrasound Segmentation (CAUS) by using a customized pre-trained AlexNet network to perform semi-automated segmentation of breast lesions in dual-modality. A new real-time dual-modality data of mammogram and ultrasound are developed for conducting this study. Data augmentation is applied to improve diversity of the data. The last layer of the pre-trained network is modified to perform pixel-wise classification of the input test image. The output binary image is obtained using the color map of each test image. Various performance measures and statistical correlation values of paired T test are used for evaluation of the proposed model. The proposed model achieves a Jaccard index of 0.53 for mammogram and 0.63 for ultrasound, respectively. Further, a Dice similarity coefficient of 0.66 for mammogram and 0.76 for ultrasound is achieved using the proposed CAMS and CAUS systems, respectively. Area under curve of the proposed CAUS system is found to be 0.81 which is very close to that of the CAMS, i.e., 0.82. It is concluded that the CAUS system can be useful in breast lesion detection along with CAMS in the routine clinical scenario. Ultrasound being a painless, ionizing radiation-free, and low-cost technology can be useful for breast cancer screening over traditional mammograms, particularly in low settings.

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Data are not available with this manuscript but can be made available on reasonable request after permission of IEC.

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This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Biomedical Engineering, National Institute of Technology Raipur, Raipur, Chhattisgarh, 492010, India

    Kushangi Atrey, Bikesh Kumar Singh & Abhijit Roy

  2. Department of Radiodiagnosis, All India Institute of Medical Sciences Raipur, Raipur, India

    Narendra Kuber Bodhey

Authors
  1. Kushangi Atrey
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  2. Bikesh Kumar Singh
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Contributions

KA and AR contributed to methodology, software, and writing—original draft. BKS contributed to conceptualization, methodology, and supervision. NKB contributed to conceptualization and supervision.

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Correspondence to Bikesh Kumar Singh.

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The study was approved by the Institutional ethics committee (IEC), NIT Raipur Letter No.: NITRR/IEC/2019/04.

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Atrey, K., Singh, B.K., Roy, A. et al. A dual-modality evaluation of computer-aided breast lesion segmentation in mammogram and ultrasound using customized transfer learning approach. SIViP 17, 1955–1963 (2023). https://doi.org/10.1007/s11760-022-02408-8

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  • DOI: https://doi.org/10.1007/s11760-022-02408-8

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