Deep learning-based tumor segmentation and classification in breast MRI with 3TP method

Abstract

Background and Objective:Timely diagnosis of early breast cancer plays a critical role in improving patient outcome and increasing treatment effectiveness. Dynamic contrast-enhancing magnetic resonance imaging (DCE-MRI) is a minimally invasive test widely used in the analysis of breast cancer. Manual analysis of DCE-MRI images by the specialist is extremely complex, exhaustive, and can lead to misunderstandings. Thus, the development of automated methods for analyzing DCE-MRI images of the breast is increasing. In this research, we propose an automatic methodology capable of detecting tumors and classifying their malignancy in a DCE-MRI breast image. Methodology:The proposed method consists of the use of two deep learning architectures, that is, SegNet and UNet, for breast tumor segmentation and the three-time-point (3TP) method for classifying the malignancy of segmented tumors. Results:The proposed methodology was tested on the public Quantitative Imaging Network (QIN) Breast DCE-MRI image set, and the best result in segmentation was a Dice of 0.9332 and IoU of 0.9799. For the classification of tumor malignancy, the methodology presented an accuracy of 100%. Conclusions:In our research, we demonstrate that the problem of mammary tumor segmentation in DCE-MRI images can be efficiently solved using deep learning architectures, and tumor malignancy classification can be done through the three-time method. The method can be integrated as a support system for the specialist in treating patients with breast cancer.