Real-time automated segmentation of breast lesions using CNN-based deep learning paradigm: Investigation on mammogram and ultrasound
Kushangi Atrey
Department of Biomedical Engineering, National Institute of Technology Raipur, Raipur, India
Search for more papers by this authorCorresponding Author
Bikesh Kumar Singh
Department of Biomedical Engineering, National Institute of Technology Raipur, Raipur, India
Correspondence
Bikesh Kumar Singh, Department of Biomedical Engineering, National Institute of Technology Raipur, Raipur, Chhattisgarh 492010, India.
Email: [email protected]
Search for more papers by this authorAbhijit Roy
Department of Biomedical Engineering, National Institute of Technology Raipur, Raipur, India
Search for more papers by this authorNarendra Kuber Bodhey
Department of Radiodiagnosis, All India Institute of Medical Sciences Raipur, Raipur, India
Search for more papers by this authorKushangi Atrey
Department of Biomedical Engineering, National Institute of Technology Raipur, Raipur, India
Search for more papers by this authorCorresponding Author
Bikesh Kumar Singh
Department of Biomedical Engineering, National Institute of Technology Raipur, Raipur, India
Correspondence
Bikesh Kumar Singh, Department of Biomedical Engineering, National Institute of Technology Raipur, Raipur, Chhattisgarh 492010, India.
Email: [email protected]
Search for more papers by this authorAbhijit Roy
Department of Biomedical Engineering, National Institute of Technology Raipur, Raipur, India
Search for more papers by this authorNarendra Kuber Bodhey
Department of Radiodiagnosis, All India Institute of Medical Sciences Raipur, Raipur, India
Search for more papers by this authorAbstract
The existing studies involving single imaging modalities (i.e., mammogram (MG) or ultrasound (US)) to detect breast lesions have demonstrated limited clinical application because radiologists rarely interpret an MG without a corresponding US and vice-versa. Thus, this article aims to develop a Computer Aided Segmentation (CAS) system for detecting breast lesions in both MG and US. A customized convolutional neural network (CNN) is adopted for this purpose. A new real-time bi-modal database of MG and US is used for dual-modality evaluation. Twelve performance measures, five shape measurements, area under receiver operating characteristics (ROC), and paired T-test are used to assess the performance of proposed CAS system. A Dice Similarity Coefficient (DSC) of 0.64 (for MG) and 0.77 (for US) and Jaccard Index (JI) of 0.53 (for MG) and 0.64 (for US) indicate that the US can be used as an adjunct technique to MG in the segmenting breast lesions.
CONFLICT OF INTEREST
The authors declare no conflicts of interest.
Open Research
DATA AVAILABILITY STATEMENT
Data is not available with this manuscript but can be made available on reasonable request after permission of IEC.
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