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

共 50 条

[1] ROC image: software for the diagnostic assessment of medical imaging quality
Adame Brooks, David
Ramirez Aguilera, Andres
Miller, Rafael A.
IMAGEN DIAGNOSTICA, 2012, 3 (01): : 6 - 10
[2] Metrological assessment of image quality in ultrasonic medical diagnostic equipment
Souza R.M.
Alvarenga A.V.
Petrella L.I.
Costa-Felix R.P.B.
Research on Biomedical Engineering, 2020, 36 (04) : 379 - 397
[3] Medical Malpractice and Diagnostic Radiology: Challenges and Opportunities
Sumner, Christina
Kietzman, Alexander
Kadom, Nadja
Frigini, Alexandre
Makary, Mina S.
Martin, Ardenne
Mcknight, Colin
Retrouvey, Michele
Spieler, Bradley
Griffith, Brent
ACADEMIC RADIOLOGY, 2024, 31 (01) : 233 - 241
[4] Challenges and opportunities in diagnostic imaging: a Canadian perspective
Stolberg, HO
Nahmias, C
CANADIAN ASSOCIATION OF RADIOLOGISTS JOURNAL-JOURNAL DE L ASSOCIATION CANADIENNE DES RADIOLOGISTES, 2002, 53 (03): : 130 - 132
[5] Medical Imaging Image Quality Assessment with Monte Carlo Methods
Michail, C. M.
Karpetas, G. E.
Fountos, G. P.
Kalyvas, N. I.
Martini, Niki
Koukou, Vaia
Valais, I. G.
Kandarakis, I. S.
4TH INTERNATIONAL CONFERENCE ON MATHEMATICAL MODELING IN PHYSICAL SCIENCES (IC-MSQUARE2015), 2015, 633
[6] Sleep Quality Assessment Challenges and opportunities
Penzel, Thomas
IEEE PULSE, 2016, 7 (04) : 3 - 3
[7] Machine learning in medical imaging: challenges and opportunities
De Bruijne, M.
RADIOTHERAPY AND ONCOLOGY, 2018, 127 : S9 - S9
[8] On the Opportunities and Challenges in Microwave Medical Sensing and Imaging
Chandra, Rohit
Zhou, Huiyuan
Balasingham, Ilangko
Narayanan, Ram M.
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2015, 62 (07) : 1667 - 1682
[9] CLASSIFICATION OF IMAGE DISTORTIONS FOR IMAGE QUALITY ASSESSMENT
Alaql, Omar
Ghazinour, Kambiz
Lu, Cheng Chang
2016 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE & COMPUTATIONAL INTELLIGENCE (CSCI), 2016, : 653 - 658
[10] Machine Learning and Bias in Medical Imaging: Opportunities and Challenges
Vrudhula, Amey
Kwan, Alan C.
Ouyang, David
Cheng, Susan
CIRCULATION-CARDIOVASCULAR IMAGING, 2024, 17 (02)

← 1 2 3 4 5 →