DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Gihun | ko |
dc.contributor.author | Han, Minah | ko |
dc.contributor.author | Shim, Hyunjung | ko |
dc.contributor.author | Baek, Jongduk | ko |
dc.date.accessioned | 2022-07-04T06:00:15Z | - |
dc.date.available | 2022-07-04T06:00:15Z | - |
dc.date.created | 2022-07-04 | - |
dc.date.issued | 2020-04 | - |
dc.identifier.citation | MEDICAL PHYSICS, v.47, no.4, pp.1619 - 1632 | - |
dc.identifier.issn | 0094-2405 | - |
dc.identifier.uri | http://hdl.handle.net/10203/297172 | - |
dc.description.abstract | Purpose In this paper, we propose a convolutional neural network (CNN)-based efficient model observer for breast computed tomography (CT) images. Methods We first showed that the CNN-based model observer provided similar detection performance to the ideal observer (IO) for signal-known-exactly and background-known-exactly detection tasks with an uncorrelated Gaussian background noise image. We then demonstrated that a single-layer CNN without a nonlinear activation function provided similar detection performance in breast CT images to the Hotelling observer (HO). To train the CNN-based model observer, we generated simulated breast CT images to produce a training dataset in which different background noise structures were generated using filtered back projection with a ramp, or a Hanning weighted ramp, filter. Circular, elliptical, and spiculated signals were used for the detection tasks. The optimal depth and the number of channels for the CNN-based model observer were determined for each task. The detection performances of the HO and a channelized Hotelling observer (CHO) with Laguerre-Gauss (LG) and partial least squares (PLS) channels were also estimated for comparison. Results The results showed that the CNN-based model observer provided higher detection performance than the HO, LG-CHO, and PLS-CHO for all tasks. In addition, it was shown that the proposed CNN-based model observer provided higher detection performance than the HO using a smaller training dataset. Conclusions In the presence of nonlinearity in the CNN, the proposed CNN-based model observer showed better performance than other linear observers. | - |
dc.language | English | - |
dc.publisher | WILEY | - |
dc.title | A convolutional neural network-based model observer for breast CT images | - |
dc.type | Article | - |
dc.identifier.wosid | 000516984500001 | - |
dc.identifier.scopusid | 2-s2.0-85081041101 | - |
dc.type.rims | ART | - |
dc.citation.volume | 47 | - |
dc.citation.issue | 4 | - |
dc.citation.beginningpage | 1619 | - |
dc.citation.endingpage | 1632 | - |
dc.citation.publicationname | MEDICAL PHYSICS | - |
dc.identifier.doi | 10.1002/mp.14072 | - |
dc.contributor.localauthor | Shim, Hyunjung | - |
dc.contributor.nonIdAuthor | Kim, Gihun | - |
dc.contributor.nonIdAuthor | Han, Minah | - |
dc.contributor.nonIdAuthor | Baek, Jongduk | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | breast CT images | - |
dc.subject.keywordAuthor | convolutional neural network | - |
dc.subject.keywordAuthor | hotelling observer | - |
dc.subject.keywordAuthor | ideal observer | - |
dc.subject.keywordPlus | NOISE POWER SPECTRUM | - |
dc.subject.keywordPlus | COMPUTED-TOMOGRAPHY | - |
dc.subject.keywordPlus | DIGITAL MAMMOGRAPHY | - |
dc.subject.keywordPlus | LESION DETECTION | - |
dc.subject.keywordPlus | TOMOSYNTHESIS | - |
dc.subject.keywordPlus | QUALITY | - |
dc.subject.keywordPlus | STATISTICS | - |
dc.subject.keywordPlus | VISIBILITY | - |
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