MRI Quality Control Algorithm Based on Image Analysis Using Convolutional and Recurrent Neural Networks

被引：1

作者：

Shoroshov, Grigorii ^{[1
]}

Senyukova, Olga ^{[1
]}

Semenov, Dmitry ^{[2
]}

Sharova, Daria ^{[2
]}

机构：

[1] Lomonosov Moscow State Univ, Fac Computat Math & Cybernet, Moscow, Russia

[2] Ctr Diagnost & Telemed, Innovat Technol Dept, Moscow, Russia

来源：

2022 IEEE 35TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS) | 2022年

关键词：

MRI Quality Control; Deep Learning; Brain MRI; Convolutional Neural Networks; LSTM; ARTIFACTS;

D O I：

10.1109/CBMS55023.2022.00080

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

MRI quality control plays a significant role in ensuring safety and quality of examinations. Most of the work in the area is devoted to the development of no-reference quality metrics. Some recent works use 2D or 3D convolutional neural networks. For this study, we collected a dataset of 363 clinical MRI sequences with known results of quality control as well as 1295 clinical MRI sequences without known results of quality control. We propose a method based on neural networks that takes into account the three-dimensional context through the use of bidirectional LSTM, as well as a pre-training method based on a prediction of no-reference quality metrics using EfficientNet convolutional neural network that allows the use of unlabeled data. The proposed method makes it possible to predict the result of quality control with ROC-AUC of almost 0.94.

引用

页码：412 / 415

页数：4

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