Convolutional neural network -based phantom image scoring for mammography quality control

BMC Medical Imaging

Research

Veli-Matti Sundell¹  Teemu Mäkelä¹  Touko Kaasalainen²  Anne-Mari Vitikainen² 

[1] Department of Physics, University of Helsinki, P.O. Box 64, 00014, Helsinki, Finland;HUS Diagnostic Center, Radiology, University of Helsinki and Helsinki University Hospital, P.O. Box 340, Haartmaninkatu 4, 00290, Helsinki, Finland;HUS Diagnostic Center, Radiology, University of Helsinki and Helsinki University Hospital, P.O. Box 340, Haartmaninkatu 4, 00290, Helsinki, Finland;

关键词: Mammography;    Quality control;    Convolutional neural network;   

DOI  :  10.1186/s12880-022-00944-w

 received in 2022-06-21, accepted in 2022-11-28,  发布年份 2022

来源: Springer

【 摘 要 】

BackgroundVisual evaluation of phantom images is an important, but time-consuming part of mammography quality control (QC). Consistent scoring of phantom images over the device’s lifetime is highly desirable. Recently, convolutional neural networks (CNNs) have been applied to a wide range of image classification problems, performing with a high accuracy. The purpose of this study was to automate mammography QC phantom scoring task by training CNN models to mimic a human reviewer.MethodsEight CNN variations consisting of three to ten convolutional layers were trained for detecting targets (fibres, microcalcifications and masses) in American College of Radiology (ACR) accreditation phantom images and the results were compared with human scoring. Regular and artificially degraded/improved QC phantom images from eight mammography devices were visually evaluated by one reviewer. These images were used in training the CNN models. A separate test set consisted of daily QC images from the eight devices and separately acquired images with varying dose levels. These were scored by four reviewers and considered the ground truth for CNN performance testing.ResultsAlthough hyper-parameter search space was limited, an optimal network depth after which additional layers resulted in decreased accuracy was identified. The highest scoring accuracy (95%) was achieved with the CNN consisting of six convolutional layers. The highest deviation between the CNN and the reviewers was found at lowest dose levels. No significant difference emerged between the visual reviews and CNN results except in case of smallest masses.ConclusionA CNN-based automatic mammography QC phantom scoring system can score phantom images in a good agreement with human reviewers, and can therefore be of benefit in mammography QC.

【 授权许可】

CC BY   
© The Author(s) 2022

【 图 表 】

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