Automatic Segmentation of Diffuse White Matter Abnormality on T2-weighted Brain MR Images Using Deep Learning in Very Preterm Infants.

التفاصيل البيبلوغرافية
العنوان:	Automatic Segmentation of Diffuse White Matter Abnormality on T2-weighted Brain MR Images Using Deep Learning in Very Preterm Infants.
المؤلفون:	Li H; Imaging Research Center, Department of Radiology (H.L., L.H.), and Perinatal Institute (H.L., V.S.P.I., N.A.P., L.H.), Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, MLC 5033, Cincinnati, OH 45229; Department of Electronic Engineering and Computing Systems, University of Cincinnati, Cincinnati, Ohio (M.C.); Deep MRI Imaging, Lewes, Del (J.W.); and Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio (N.A.P., L.H.)., Chen M; Imaging Research Center, Department of Radiology (H.L., L.H.), and Perinatal Institute (H.L., V.S.P.I., N.A.P., L.H.), Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, MLC 5033, Cincinnati, OH 45229; Department of Electronic Engineering and Computing Systems, University of Cincinnati, Cincinnati, Ohio (M.C.); Deep MRI Imaging, Lewes, Del (J.W.); and Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio (N.A.P., L.H.)., Wang J; Imaging Research Center, Department of Radiology (H.L., L.H.), and Perinatal Institute (H.L., V.S.P.I., N.A.P., L.H.), Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, MLC 5033, Cincinnati, OH 45229; Department of Electronic Engineering and Computing Systems, University of Cincinnati, Cincinnati, Ohio (M.C.); Deep MRI Imaging, Lewes, Del (J.W.); and Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio (N.A.P., L.H.)., Illapani VSP; Imaging Research Center, Department of Radiology (H.L., L.H.), and Perinatal Institute (H.L., V.S.P.I., N.A.P., L.H.), Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, MLC 5033, Cincinnati, OH 45229; Department of Electronic Engineering and Computing Systems, University of Cincinnati, Cincinnati, Ohio (M.C.); Deep MRI Imaging, Lewes, Del (J.W.); and Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio (N.A.P., L.H.)., Parikh NA; Imaging Research Center, Department of Radiology (H.L., L.H.), and Perinatal Institute (H.L., V.S.P.I., N.A.P., L.H.), Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, MLC 5033, Cincinnati, OH 45229; Department of Electronic Engineering and Computing Systems, University of Cincinnati, Cincinnati, Ohio (M.C.); Deep MRI Imaging, Lewes, Del (J.W.); and Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio (N.A.P., L.H.)., He L; Imaging Research Center, Department of Radiology (H.L., L.H.), and Perinatal Institute (H.L., V.S.P.I., N.A.P., L.H.), Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, MLC 5033, Cincinnati, OH 45229; Department of Electronic Engineering and Computing Systems, University of Cincinnati, Cincinnati, Ohio (M.C.); Deep MRI Imaging, Lewes, Del (J.W.); and Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio (N.A.P., L.H.).
المصدر:	Radiology. Artificial intelligence [Radiol Artif Intell] 2021 Feb 03; Vol. 3 (3), pp. e200166. Date of Electronic Publication: 2021 Feb 03 (Print Publication: 2021).
نوع المنشور:	Journal Article
اللغة:	English
بيانات الدورية:	Publisher: Radiological Society of North America, Inc Country of Publication: United States NLM ID: 101746556 Publication Model: eCollection Cited Medium: Internet ISSN: 2638-6100 (Electronic) Linking ISSN: 26386100 NLM ISO Abbreviation: Radiol Artif Intell Subsets: PubMed not MEDLINE
أسماء مطبوعة:	Original Publication: Oak Brook, IL : Radiological Society of North America, Inc., [2019]-
مستخلص:	About 50%-80% of very preterm infants (VPIs) (≤ 32 weeks gestational age) exhibit diffuse white matter abnormality (DWMA) on their MR images at term-equivalent age. It remains unknown if DWMA is associated with developmental impairments, and further study is warranted. To aid in the assessment of DWMA, a deep learning model for DWMA quantification on T2-weighted MR images was developed. This secondary analysis of prospective data was performed with an internal cohort of 98 VPIs (data collected from December 2014 to April 2016) and an external cohort of 28 VPIs (data collected from January 2012 to August 2014) who had already undergone MRI at term-equivalent age. Ground truth DWMA regions were manually annotated by two human experts with the guidance of a prior published semiautomated algorithm. In a twofold cross-validation experiment using the internal cohort of 98 infants, the three-dimensional (3D) ResU-Net model accurately segmented DWMA with a Dice similarity coefficient of 0.907 ± 0.041 (standard deviation) and balanced accuracy of 96.0% ± 2.1, outperforming multiple peer deep learning models. The 3D ResU-Net model that was trained with the whole internal cohort ( n = 98) was further tested on an independent external test cohort ( n = 28) and achieved a Dice similarity coefficient of 0.877 ± 0.059 and balanced accuracy of 92.3% ± 3.9. The externally validated 3D ResU-Net deep learning model for accurately segmenting DWMA may facilitate the clinical diagnosis of DWMA in VPIs. Supplemental material is available for this article. Keywords: Brain/Brain Stem, Convolutional Neural Network (CNN), MR-Imaging, Pediatrics, Segmentation, Supervised learning © RSNA, 2021. Competing Interests: Disclosures of Conflicts of Interest: H.L. Activities related to the present article: institution and work supported by the National Institutes of Health (NIH) grants R01-EB029944, R21-HD094085, R01-NS094200, and R01-NS096037. Activities not related to the present article: disclosed no relevant relationships. Other relationships: disclosed no relevant relationships. M.C. disclosed no relevant relationships. J.W. disclosed no relevant relationships. V.S.P.I. disclosed no relevant relationships. N.A.P. Activities related to the present article: institution received grant from NIH. Activities not related to the present article: disclosed no relevant relationships. Other relationships: disclosed no relevant relationships. L.H. Activities related to the present article: institution received grant from NIH (R01-EB029944, R21-HD094085, R01-NS094200, and R01-NS096037). Activities not related to the present article: disclosed no relevant relationships. Other relationships: disclosed no relevant relationships. (2021 by the Radiological Society of North America, Inc.)
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معلومات مُعتمدة:	R01 EB029944 United States EB NIBIB NIH HHS; R01 NS094200 United States NS NINDS NIH HHS; R01 NS096037 United States NS NINDS NIH HHS; R21 HD094085 United States HD NICHD NIH HHS
تواريخ الأحداث:	Date Created: 20210618 Latest Revision: 20230209
رمز التحديث:	20231215
مُعرف محوري في PubMed:	PMC8166113
DOI:	10.1148/ryai.2021200166
PMID:	34142089
قاعدة البيانات:	MEDLINE

الوصف
تدمد:	2638-6100
DOI:	10.1148/ryai.2021200166