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

人工智能 分割 计算机科学 模式识别(心理学) 乳腺肿瘤 深度学习 乳房磁振造影 乳腺癌 医学 乳腺摄影术 癌症 内科学
作者
Edson Damasceno Carvalho,Otílio Paulo da Silva Neto,Antônio Oseas de Carvalho Filho
出处
期刊:Biomedical Signal Processing and Control [Elsevier BV]
卷期号:93: 106199-106199 被引量:4
标识
DOI:10.1016/j.bspc.2024.106199
摘要

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. 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. 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%. 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.
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