Automatic Segmentation Model of Macular Edema Based on
Dilated U-Net and Conditional Random Fields
LI Jing1，ZHONG Yuanfu2，LI Xiaokai2，WANG Zhenhua2
1. Department of Discipline Inspection and Supervision, Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University,
Shanghai 201306, China丨 2. Department of Information, Shanghai Ocean University, Shanghai 201306, China
Abstract：Objective The automatic segmentation of macular edema (ME) region in optical coherence tomography (OCT) image can
assist clinical diagnosis and decision-making. In order to improve the accuracy and efficiency of me region segmentation in OCT
image, ME region automatic segmentation model of a combined Dilated U-net network and conditional random field (CRF) were
proposed in this paper. Methods The model includes two aspects. In view of the characteristics of simple structure and efficient
operation of U-net network, hole convolution was used to replace the traditional convolution to increase the network receptive field,
Dilated U-net, a network architecture for ME region segmentation of OCT image was established to realize the coarse segmentation
of retinal me region. Based on the coarse segmentation of ME region as the initial contour curve, the full CRF was used to optimize
the ME region with high precision. Results 200 OCT images were selected for experiments, and the segmentation accuracy was
analyzed through accuracy, recall and Dice similarity coefficient. The results showed that this model had higher segmentation
accuracy and Dice similarity coefficient than traditional segmentation models such as C-V and SBG, which were 95.94% and 95.52%
respectively丨 compared with network segmentation models such as FCN, PSPNet and Deeplab, it had the highest segmentation
efficiency, and the segmentation time of ME region in a single OCT image was reduced to 0.9 s. Conclusion The improved of ME
region automatic segmentation model combining dilated U-net and CRF can not only obtain the abstract features of ME region, but
also take into account the context information of the image, so as to make the segmentation result of ME region more accurate. This
model not only improves the accuracy of lesion region segmentation, but also reduces the segmentation time. It is suitable for highprecision
segmentation of retinal me region.
李净1，钟元芾2，李晓凯2，王振华2. 联合Dilated U-net和全连接条件随机场的黄斑水肿区域自动分割模型[J]. 中国医疗设备, 2021, 36(11): 46-50.
LI Jing1，ZHONG Yuanfu2，LI Xiaokai2，WANG Zhenhua2. Automatic Segmentation Model of Macular Edema Based on
Dilated U-Net and Conditional Random Fields. China Medical Devices, 2021, 36(11): 46-50.
