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一种使用术中受激拉曼组织学和深度学习的自动化组织诊断流程。

An automated tissue-to-diagnosis pipeline using intraoperative stimulated Raman histology and deep learning.

作者信息

Hollon Todd C, Orringer Daniel A

机构信息

Department of Neurosurgery, University of Michigan Medical School, Ann Arbor, MI, USA.

Department of Neurosurgery, New York University, New York, NY, USA.

出版信息

Mol Cell Oncol. 2020 Apr 1;7(3):1736742. doi: 10.1080/23723556.2020.1736742. eCollection 2020.

Abstract

We recently developed and validated a bedside tissue-to-diagnosis pipeline using stimulated Raman histology (SRH), a label-free optical imaging method, and deep convolutional neural networks (CNN) in prospective clinical trial. Our CNN learned a hierarchy of interpretable histologic features found in the most common brain tumors and was able to accurately segment cancerous regions in SRH images.

摘要

我们最近在一项前瞻性临床试验中,利用受激拉曼组织学(SRH,一种无标记光学成像方法)和深度卷积神经网络(CNN)开发并验证了一种床边组织到诊断的流程。我们的CNN学习了在最常见脑肿瘤中发现的可解释组织学特征层次结构,并能够在SRH图像中准确分割癌性区域。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8480/7199763/5e48a0b947f1/kmco-07-03-1736742-g001.jpg

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