使MRInnU-Net:

Rohan Bareja1, Marwa Ismail1, Douglas Martin1

  • 1From the Department of Radiology, University of Wisconsin-Madison, Madison, Wis (R.B., M.I., I.Y.); University Hospitals, Cleveland, Ohio (D.M., A.N.); Departments of Biomedical Engineering (M.L., S.G., S.I.) and Neurosciences (P.D.), Case Western Reserve University, Cleveland, Ohio; Department of Radiology, Children's Hospital Los Angeles, Los Angeles, Calif (B.T.); Division of Hematology, Oncology & Bone Marrow Transplant, Nationwide Children's Hospital, Columbus, Ohio (R.S.); Department of Pediatrics, Keck School of Medicine of University of Southern California, Children's Hospital Los Angeles, Los Angeles, Calif (A.M.); Department of Pathology, Children's Hospital Los Angeles, Los Angeles, Calif (A.J.); Division of Oncology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio (P.d.B.); William S. Middleton Memorial Veterans Affairs (VA) Healthcare, Madison, Wis (P.T.); and Department of Radiology and Biomedical Engineering, University of Wisconsin-Madison, 750 Highland Ave, Madison, WI 53726 (P.T.).

概括

nnU-Net模型在多机构MRI扫描上准确地细分了儿科脑髓母细胞瘤. 转移学习和直接深度学习方法显示出稳健性,有助于放射治疗规划.

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