Automatic classification of ultrasound breast lesions using a deep convolutional neural network mimicking human decision-making.
Objectives: To evaluate a deep convolutional neural network (dCNN) for detection, highlighting, and classification of ultrasound (US) breast lesions mimicking human decision-making according to the Breast Imaging Reporting and Data System (BI-RADS).Methods and Materials: One thousand nineteen breast...
| Publicado en: | European Radiology Vol. 29; no. 10; pp. 5458 - 5469 |
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| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Oct2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=138395950&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 138395950 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Oct2019 vid: 29 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 138395950 138395950 144029210 NLM30927100 138395950 10.1007/s00330-019-06118-7 NLM30927100 138395950 ppf: 5458 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automatic classification of ultrasound breast lesions using a deep convolutional neural network mimicking human decision-making. aug: au: Ciritsis, Alexander Rossi, Cristina Eberhard, Matthias Marcon, Magda Becker, Anton S. Boss, Andreas affil: Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, Rämistrasse 100, 8091, Zurich, Switzerland sug: subj: Breast Neoplasms Image Interpretation, Computer Assisted Methods Ultrasonography Methods Aged Female Retrospective Design Middle Age ROC Curve Adult Algorithms Human Observer Bias Validation Studies Comparative Studies Evaluation Research Multicenter Studies Aged: 65+ years Middle Aged: 45-64 years Adult: 19-44 years Female ab: Objectives: To evaluate a deep convolutional neural network (dCNN) for detection, highlighting, and classification of ultrasound (US) breast lesions mimicking human decision-making according to the Breast Imaging Reporting and Data System (BI-RADS).Methods and Materials: One thousand nineteen breast ultrasound images from 582 patients (age 56.3 ± 11.5 years) were linked to the corresponding radiological report. Lesions were categorized into the following classes: no tissue, normal breast tissue, BI-RADS 2 (cysts, lymph nodes), BI-RADS 3 (non-cystic mass), and BI-RADS 4-5 (suspicious). To test the accuracy of the dCNN, one internal dataset (101 images) and one external test dataset (43 images) were evaluated by the dCNN and two independent readers. Radiological reports, histopathological results, and follow-up examinations served as reference. The performances of the dCNN and the humans were quantified in terms of classification accuracies and receiver operating characteristic (ROC) curves.Results: In the internal test dataset, the classification accuracy of the dCNN differentiating BI-RADS 2 from BI-RADS 3-5 lesions was 87.1% (external 93.0%) compared with that of human readers with 79.2 ± 1.9% (external 95.3 ± 2.3%). For the classification of BI-RADS 2-3 versus BI-RADS 4-5, the dCNN reached a classification accuracy of 93.1% (external 95.3%), whereas the classification accuracy of humans yielded 91.6 ± 5.4% (external 94.1 ± 1.2%). The AUC on the internal dataset was 83.8 (external 96.7) for the dCNN and 84.6 ± 2.3 (external 90.9 ± 2.9) for the humans.Conclusion: dCNNs may be used to mimic human decision-making in the evaluation of single US images of breast lesion according to the BI-RADS catalog. The technique reaches high accuracies and may serve for standardization of highly observer-dependent US assessment.Key Points: • Deep convolutional neural networks could be used to classify US breast lesions. • The implemented dCNN with its sliding window approach reaches high accuracies in the classification of US breast lesions. • Deep convolutional neural networks may serve for standardization in US BI-RADS classification. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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