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首页> Academic radiology> Fully Automated Postlumpectomy Breast Margin Assessment Utilizing Convolutional Neural Network Based Optical Coherence Tomography Image Classification Method.

Fully Automated Postlumpectomy Breast Margin Assessment Utilizing Convolutional Neural Network Based Optical Coherence Tomography Image Classification Method.

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作者： Diana,Mojahed ^[1] ; Richard S,Ha ^[2] ; Peter,Chang ^[3] ; Yu,Gan ^[4] ; Xinwen,Yao ^[4] ; Brigid,Angelini ^[4] ; Hanina,Hibshoosh ^[5] ; Bret,Taback ^[6] ; Christine P,Hendon ^[4]

作者单位： Department of Biomedical Engineering, Columbia University, New York, New York; Department of Electrical Engineering, Columbia University, New York, New York. ^[1] Department of Radiology, Columbia University Medical Center, 622 W 168th St, PB-1-301, New York, New York 10032. Electronic address: rh2616@columbia.edu. ^[2] Department of Radiological Sciences, University of California Irvine Medical Center, Orange, California. ^[3] Department of Electrical Engineering, Columbia University, New York, New York. ^[4] Department of Pathology and Cell Biology, Columbia University Medical Center, New York, New York. ^[5] Department of Surgery, Columbia University Medical Center, New York, New York. ^[6]

关键词 CNN Lumpectomy OCT

主题词算法(Algorithms);乳房(Breast);乳腺肿瘤(Breast Neoplasms);女(雌)性(Female);人类(Humans);图像处理, 计算机辅助(Image Processing, Computer-Assisted);磁共振成像(Magnetic Resonance Imaging);乳房切除术, 区段(Mastectomy, Segmental);手术后期间(Postoperative Period);体层摄影术, 光学相干(Tomography, Optical Coherence)

DOI 10.1016/j.acra.2019.06.018

PMID 31324579

发布时间 2020-10-22

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Academic radiology

Academic radiology

2020年27卷5期

e81-e86页

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