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Breast tumor segmentation via deep correlation analysis of multi-sequence MRI

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Abstract

Precise segmentation of breast tumors from MRI is crucial for breast cancer diagnosis, as it allows for detailed calculation of tumor characteristics such as shape, size, and edges. Current segmentation methodologies face significant challenges in accurately modeling the complex interrelationships inherent in multi-sequence MRI data. This paper presents a hybrid deep network framework with three interconnected modules, aimed at efficiently integrating and exploiting the spatial-temporal features among multiple MRI sequences for breast tumor segmentation. The first module involves an advanced multi-sequence encoder with a densely connected architecture, separating the encoding pathway into multiple streams for individual MRI sequences. To harness the intricate correlations between different sequence features, we propose a sequence-awareness and temporal-awareness method that adeptly fuses spatial-temporal features of MRI in the second multi-scale feature embedding module. Finally, the decoder module engages in the upsampling of feature maps, meticulously refining the resolution to achieve highly precise segmentation of breast tumors. In contrast to other popular methods, the proposed method learns the interrelationships inherent in multi-sequence MRI. We justify the proposed method through extensive experiments. It achieves notable improvements in segmentation performance, with Dice Similarity Coefficient (DSC), Intersection over Union (IoU), and Positive Predictive Value (PPV) scores of 80.57%, 74.08%, and 84.74% respectively.

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Funding

This research was supported by the National Natural Science Foundation of China (No. 62001380, 62073260); Key & D projects in Shaanxi Province (No. 2023-YBSF-493, 2023-YBSF-455);Xi’an Meri-tocracy Plan innovative talent project in Shaanxi Province, China (No. XAYC210045); The Xi’an Science and Technology Plan Project (22YXYJ0127); and Xi’an University of Post & Telecommunications Science and Technology Project (No. 2023-B-51).

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Authors and Affiliations

  1. School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an, Shaanxi, 710121, China

    Hongyu Wang, Yanfang Hao & Songtao Ding

  2. Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi’an University of Posts and Telecommunications, Xi’an, Shaanxi, 710121, China

    Hongyu Wang, Yanfang Hao & Songtao Ding

  3. Xi’an Key Laboratory of Big Data and Intelligent Computing, Xi’an University of Posts and Telecommunications, Xi’an, Shaanxi, 710121, China

    Hongyu Wang, Yanfang Hao & Songtao Ding

  4. Department of Information Science and Technology, Northwest University, Xi’an, Shaanxi, 7101127, China

    Tonghui Wang & Jun Feng

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  1. Hongyu Wang
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Correspondence to Hongyu Wang or Jun Feng.

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This research was supported by the National Natural Science Foundation of China (No. 62001380, 62073260); Key & D projects in Shaanxi Province (No. 2023-YBSF-493, 2023-YBSF-455);Xi’an Meri-tocracy Plan innovative talent project in Shaanxi Province, China (No. XAYC210045); The Xi’an Science and Technology Plan Project (22YXYJ0127); and Xi’an University of Post & Telecommunications Science and Technology Project (No. 2023-B-51).

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Wang, H., Wang, T., Hao, Y. et al. Breast tumor segmentation via deep correlation analysis of multi-sequence MRI. Med Biol Eng Comput 62, 3801–3814 (2024). https://doi.org/10.1007/s11517-024-03166-0

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  • DOI: https://doi.org/10.1007/s11517-024-03166-0

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