Deep-learning 2.5-dimensional single-shot detector improves the performance of automated detection of brain metastases on contrast-enhanced CT.
Purpose: This study aims to develop a 2.5-dimensional (2.5D) deep-learning, object detection model for the automated detection of brain metastases, into which three consecutive slices were fed as the input for the prediction in the central slice, and to compare its performance with that of an ordina...
| Publicado en: | Neuroradiology Vol. 64; no. 8; pp. 1511 - 1519 |
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| Autores principales: | , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2022
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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=157889445&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 157889445 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00283940 NYZ jtl: Neuroradiology issn: 00283940 maglogo: N pubinfo: dt: Aug2022 vid: 64 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 157889445 154794740 157889445 157889445 10.1007/s00234-022-02902-3 157889445 ppf: 1511 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep-learning 2.5-dimensional single-shot detector improves the performance of automated detection of brain metastases on contrast-enhanced CT. aug: au: Takao, Hidemasa Amemiya, Shiori Kato, Shimpei Yamashita, Hiroshi Sakamoto, Naoya Abe, Osamu affil: Department of Radiology, Graduate School of Medicine, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, 113-8655, Tokyo, Japan sug: subj: Brain Neoplasms Radiography Neoplasm Metastasis Radiography Tomography, X-Ray Computed Methods Image Processing, Computer Assisted Deep Learning Contrast Media Diagnostic Use Predictive Value of Tests Evaluation Human Predictive Validity Sensitivity and Specificity Descriptive Statistics Confidence Intervals T-Tests ab: Purpose: This study aims to develop a 2.5-dimensional (2.5D) deep-learning, object detection model for the automated detection of brain metastases, into which three consecutive slices were fed as the input for the prediction in the central slice, and to compare its performance with that of an ordinary 2-dimensional (2D) model. Methods: We analyzed 696 brain metastases on 127 contrast-enhanced computed tomography (CT) scans from 127 patients with brain metastases. The scans were randomly divided into training (n = 79), validation (n = 18), and test (n = 30) datasets. Single-shot detector (SSD) models with a feature fusion module were constructed, trained, and compared using the lesion-based sensitivity, positive predictive value (PPV), and the number of false positives per patient at a confidence threshold of 50%. Results: The 2.5D SSD model had a significantly higher PPV (t test, p < 0.001) and a significantly smaller number of false positives (t test, p < 0.001). The sensitivities of the 2D and 2.5D models were 88.1% (95% confidence interval [CI], 86.6–89.6%) and 88.7% (95% CI, 87.3–90.1%), respectively. The corresponding PPVs were 39.0% (95% CI, 36.5–41.4%) and 58.9% (95% CI, 55.2–62.7%), respectively. The numbers of false positives per patient were 11.9 (95% CI, 10.7–13.2) and 4.9 (95% CI, 4.2–5.7), respectively. Conclusion: Our results indicate that 2.5D deep-learning, object detection models, which use information about the continuity between adjacent slices, may reduce false positives and improve the performance of automated detection of brain metastases compared with ordinary 2D models. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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