DRUNET: a dilated-residual U-Net deep learning network to segment optic nerve head tissues in optical coherence tomography images
DRUNET: a dilated-residual U-Net deep learning network to segment optic nerve head tissues in optical coherence tomography images
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DOI：
10.1364/boe.9.003244
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发表时间：
2018-07-01
影响因子：
3.4
通讯作者：
Girard, Michael J. A.
中科院分区：
文献类型：
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作者：
Devalla, Sripad Krishna;Renukanand, Prajwal K.;Girard, Michael J. A.
鉴于随着青光眼的发展，视神经头(ONH)的神经和结缔组织呈现出复杂的形态变化，从光学相干断层扫描(OCT)图像中同时分离它们对于青光眼的临床诊断和治疗具有重要意义。设计并训练了一种深度学习算法(自定义U-Net)，通过捕捉局部(组织纹理)和上下文(组织的空间排列)信息来分割6个组织层。总体Dice系数(所有组织的平均值)为0.91+/-0.05，与专家观察者进行的手动分割相比。此外，我们从分割的组织中自动提取了六个与临床相关的神经和结缔组织结构参数。我们在这里提供了一个健壮的分割框架，该框架也可以扩展到ONH组织的3D分割。(C)OSA开放获取出版协议条款下的2018年美国光学学会。
Given that the neural and connective tissues of the optic nerve head (ONH) exhibit complex morphological changes with the development and progression of glaucoma, their simultaneous isolation from optical coherence tomography (OCT) images may be of great interest for the clinical diagnosis and management of this pathology. A deep learning algorithm (custom U-NET) was designed and trained to segment 6 ONH tissue layers by capturing both the local (tissue texture) and contextual information (spatial arrangement of tissues). The overall Dice coefficient (mean of all tissues) was 0.91 +/- 0.05 when assessed against manual segmentations performed by an expert observer. Further, we automatically extracted six clinically relevant neural and connective tissue structural parameters from the segmented tissues. We offer here a robust segmentation framework that could also be extended to the 3D segmentation of the ONH tissues. (C) 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.