基于高分辨率网络的视杯和视盘的联合分割

doi:10.13229/j.cnki.jdxbgxb.20211082

摘要/Abstract

摘要：

针对在使用视盘（OD）和视杯（OC）的分割测量杯盘比（CDR）来诊断青光眼的过程中，与视盘分割相比视杯分割仍存在分割精度上的困难，提出了一种深度学习体系结构MS-HRNET，用于视杯和视盘的联合分割。它是一种基于HRNET的改进架构。通过在HRNET中添加多尺度输入，可以弥补特征提取过程中的信息丢失。结合多尺度空间和通道注意机制，提取图像深层信息。通过添加侧输出层，指导网络的早期训练。实验表明：该算法在Drishti-GS1和REFUGE数据集上的分割效果优于现有的视杯和视盘分割方法。

关键词: 视杯视盘分割, 高分辨率网络, 多尺度输入, 注意力机制

Abstract:

Measuring cup to disk ratio（CDR） by segmenting optic disc（OD） and optic cup（OC） is an effective method for diagnosis of glaucoma. Compared with OD segmentation，OC segmentation still faces difficulties in segmentation accuracy. In this paper，a deep learning architecture MS-HRNET is proposed for joint segmentation of OC and OD. It is an improved architecture based on HRNET. By adding multi-scale input to HRNET，information loss during feature extraction can be compensated. Combined with multi-scale spatial and channel attention mechanism，deep image features are extracted. By adding a side output layer，the early training of the network is guided. Experimental results show that the proposed model performs better than the existing OC and OD segmentation methods on Drishti-GS1 and REFUGE datasets.

Key words: optic cup and optic disc segmentation, high-resolution network, multi-scale input, attention mechanism

中图分类号:

TP391.4

郭晓新,李佳慧,张宝亮. 基于高分辨率网络的视杯和视盘的联合分割[J]. 吉林大学学报(工学版), 2023, 53(8): 2350-2357.

Xiao-xin GUO,Jia-hui LI,Bao-liang ZHANG. Joint segmentation of optic cup and disc based on high resolution network[J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(8): 2350-2357.

图/表 16

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表9

各模型在REFUGE数据集上的分割结果比较"

方法	$J A C C O D$	$J A C C O C$	MIoU
U-Net	0.8750	0.7760	0.8516
M-Net	0.8826	0.7897	0.8605
SegNet	0.8816	0.7906	0.8597
cGANs	0.8843	0.8000	0.8655
CDED-Net	0.8837	0.8111	0.8705
本文	0.9757	0.8891	0.9463

表9

图5

表10

图6

参考文献 24

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方法	视杯		视盘		MIoU
方法	JACC	Dice	JACC	Dice	MIoU
网络（无多尺度输入）	0.8570	0.9218	0.9497	0.9738	0.9187
网络（多尺度输入）	0.8808	0.9358	0.9658	0.9825	0.9368

方法	视杯		视盘		MIoU
方法	JACC	Dice	JACC	Dice	MIoU
网络（无多尺度输入）	0.8686	0.9263	0.9752	0.9874	0.9340
网络（多尺度输入）	0.8757	0.9299	0.9759	0.9878	0.9374

方法	视杯		视盘		MIoU
方法	JACC	Dice	JACC	Dice	MIoU
网络（无MCCAM模块）	0.8570	0.9218	0.9497	0.9738	0.9187
网络（有MCCAM模块）	0.8818	0.9368	0.9668	0.9835	0.9378

方法	视杯		视盘		MIoU
方法	JACC	Dice	JACC	Dice	MIoU
网络（无MCCAM模块）	0.8686	0.9263	0.9752	0.9874	0.9340
网络（有MCCAM模块）	0.8758	0.9295	0.9720	0.9858	0.9326

方法	视杯		视盘		MIoU
方法	JACC	Dice	JACC	Dice	MIoU
网络（无MCCAM模块）	0.8714	0.9266	0.9754	0.9875	0.9352
网络（有MCCAM模块）	0.8812	0.9328	0.9773	0.9885	0.9398