Current Status and Future Perspectives of Artificial Intelligence in Magnetic Resonance Breast Imaging.

Anke Meyer-Bäse,Uwe Meyer-Bäse,Katja Pinker,Lia Morra

doi:10.1155/2020/6805710

Abstract

Recent advances in artificial intelligence (AI) and deep learning (DL) have impacted many scientific fields including biomedical maging. Magnetic resonance imaging (MRI) is a well-established method in breast imaging with several indications including screening, staging, and therapy monitoring. The rapid development and subsequent implementation of AI into clinical breast MRI has the potential to affect clinical decision-making, guide treatment selection, and improve patient outcomes. The goal of this review is to provide a comprehensive picture of the current status and future perspectives of AI in breast MRI. We will review DL applications and compare them to standard data-driven techniques. We will emphasize the important aspect of developing quantitative imaging biomarkers for precision medicine and the potential of breast MRI and DL in this context. Finally, we will discuss future challenges of DL applications for breast MRI and an AI-augmented clinical decision strategy.

Highlights

GOCS-DLP shape prior based on semantic segmentation based on deep learning (DL) 2D U-net applied slice-by-slice Hierarchical multistage U-net with dice loss Comparison of 2D U-net and 2D SegNet models with transfer learning from DCE-Magnetic resonance imaging (MRI) to diffusion-weighted imaging (DWI)
Evaluation of Computer-Aided Detection (CADe) systems is usually performed by free-response receiver operative curve (FROC) analysis [89]
The FROC curve plots the fraction of correctly localized lesions as a function of the average number of false positives (FPs) per image, where each point in the curve corresponds to a different threshold. e FROC curve is not bounded; a convenient summary measure like the area under the receiver operating curve (ROC) curve is not readily available

Summary

Introduction

Magnetic resonance imaging (MRI), in particular dynamic contrast-enhanced MRI (DCE-MRI), is a non-invasive, wellestablished breast imaging modality with several indications in oncology including screening of high-risk women, preoperative staging, and therapy monitoring [1]. Us, machine learning [3], considered as a subset of AI, has been considered to improve and streamline this process, determining relevant patterns in these data and supporting clinical decision-making [4]. In this context, there are an increasing number of clinical and biological features extracted from multiparametric breast imaging techniques that can potentially shed light into these important questions. Eir renaissance stems in particular from the increasing interest in radiomics In this discipline, “engineered” features describing the radiologic aspects of a tumor such as shape, intensity, and texture are extracted from regions of interest, usually segmented by an expert. AI and deep learning (DL) have the potential to overcome these challenges and can determine feature representations directly from the images without relying on a time-consuming manual segmentation step. We start by providing an overview of fundamental techniques in AI, highlighting differences between conventional ML and DL, and conclude by providing a future perspective on how AI, and DL in particular, will be leveraged for breast MRI in the future

Introduction to Data-Driven Approaches in Breast MRI

Perspectives of AI and Deep Learning in Breast MRI

Evaluation results

Deep Learning and Radiomics

Conclusion