DIAGNOSTIC IMAGE QUALITY ASSESSMENT AND CLASSIFICATION IN MEDICAL IMAGING: OPPORTUNITIES AND CHALLENGES

被引：0

作者：

Ma, Jeffrey J. ^{[1
,2
]}

Nakarmi, Ukash ^{[2
]}

Kin, Cedric Yue Sik ^{[2
]}

Sandino, Christopher M. ^{[3
]}

Cheng, Joseph Y. ^{[2
]}

Syed, Ali B. ^{[2
]}

Wei, Peter ^{[2
]}

Pauly, John M. ^{[3
]}

Vasanawala, Shreyas S. ^{[2
]}

机构：

[1] CALTECH, Dept Comp & Math Sci, Pasadena, CA 91125 USA

[2] Stanford Univ, Dept Radiol, Stanford, CA 94305 USA

[3] Stanford Univ, Dept Elect Engn, Stanford, CA 94305 USA

来源：

2020 IEEE 17TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2020) | 2020年

关键词：

Image quality; deep learning; medical imaging; MOTION ARTIFACTS; MRI;

D O I：

10.1109/isbi45749.2020.9098735

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

Magnetic Resonance Imaging (MRI) suffers from several artifacts, the most common of which are motion artifacts. These artifacts often yield images that are of non-diagnostic quality. To detect such artifacts, images are prospectively evaluated by experts for their diagnostic quality, which necessitates patient-revisits and rescans whenever non-diagnostic quality scans are encountered. This motivates the need to develop an automated framework capable of accessing medical image quality and detecting diagnostic and non-diagnostic images. In this paper, we explore several convolutional neural network-based frameworks for medical image quality assessment and investigate several challenges therein.

引用

页码：337 / 340

页数：4

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