Multi-channel pathological image segmentation with gated axial self-attention

doi:10.11772/j.issn.1001-9081.2022030333

Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (4): 1269-1277.DOI: 10.11772/j.issn.1001-9081.2022030333

• Multimedia computing and computer simulation • Previous Articles

Multi-channel pathological image segmentation with gated axial self-attention

Zhi CHEN¹, Xin LI¹, Liyan LIN², Jing ZHONG³, Peng SHI¹()

^1.College of Computer and Cyber Security，Fujian Normal University，Fuzhou Fujian 350117，China
^2.Department of Pathology，Fujian Cancer Hospital，Fujian Medical University Cancer Hospital，Fuzhou Fujian 350014，China
^3.Department of Radiology and Diagnosis，Fujian Cancer Hospital，Fujian Medical University Cancer Hospital，Fuzhou Fujian 350014，China

Received:2022-03-22 Revised:2022-07-29 Accepted:2022-08-15 Online:2023-01-11 Published:2023-04-10
Contact: Peng SHI
About author:CHEN Zhi， born in 1998， M. S. candidate. His research interests include deep learning， medical image processing.
LI Xin， born in 1998， M. S. candidate. His research interests include machine learning， medical image processing.
LIN Liyan， born in 1984， Ph. D.， deputy chief physician. Her research interests include pathological diagnosis of tumor.
ZHONG Jing， born in 1982， Ph. D.， deputy chief physician. Her research interests include imaging diagnosis， medical image processing.
Supported by:
Science and Technology Innovation Joint Fund of Fujian Province(2018Y9112);Medical Elite Cultivation Program of Fujian(2019-ZQN-17)

引入门控轴向自注意力的多通道病理图像分割

陈志¹, 李歆¹, 林丽燕², 钟婧³, 时鹏¹()

^1.福建师范大学计算机与网络空间安全学院, 福州 350117
^2.福建医科大学附属肿瘤医院, 福建省肿瘤医院病理科, 福州 350014
^3.福建医科大学附属肿瘤医院, 福建省肿瘤医院放射诊断科, 福州 350014

通讯作者: 时鹏
作者简介:陈志（1998—），男，江西九江人，硕士研究生，主要研究方向：深度学习、医学图像处理；
李歆（1998—），男，福建福州人，硕士研究生，主要研究方向：机器学习、医学图像处理；
林丽燕（1984—），女，福建龙岩人，副主任医师，博士，主要研究方向：肿瘤病理诊断；
钟婧（1982—），女（畲族），福建漳州人，副主任医师，博士，主要研究方向：影像诊断、医学图像处理；
基金资助:
福建省科技创新联合资金资助项目(2018Y9112);福建省卫生系统中青年骨干人才培养项目(2019?ZQN?17)

Abstract

Abstract:

In Hematoxylin-Eosin （HE）-stained pathological images， the uneven distribution of cell staining and the diversity of various tissue morphologies bring great challenges to automated segmentation. Traditional convolutions cannot capture the correlation features between pixels in a large neighborhood， making it difficult to further improve the segmentation performance. Therefore， a Multi-Channel Segmentation Network with gated axial self-attention （MCSegNet） model was proposed to achieve accurate segmentation of nuclei in pathological images. In the proposed model， a dual-encoder and decoder structure was adopted， in which the axial self-attention encoding channel was used to capture global features， while the convolutional encoding channel based on residual structure was used to obtain local fine features. The feature representation was enhanced by feature fusion at the end of the encoding channel， providing a good information base for the decoder. And in the decoder， segmentation results were gradually generated by cascading multiple upsampling modules. In addition， the improved hybrid loss function was used to alleviate the common problem of sample imbalance in pathological images effectively. Experimental results on MoNuSeg2020 public dataset show that the improved segmentation method is 2.66 percentage points and 2.77 percentage points higher than U-Net in terms of F1-score and Intersection over Union （IoU） indicators， respectively， and effectively improves the pathological image segmentation effect and the reliability of clinical diagnosis.

Key words: pathological image, nuclear segmentation, axial self-attention, residual structure, hybrid loss function

摘要：

在苏木精?伊红（HE）染色病理图像中，细胞染色分布的不均匀和各类组织形态的多样性给自动化分割带来了极大挑战。针对传统卷积无法捕获大邻域范围内像素间的关联特征，导致分割效果难以进一步提升的问题，提出引入门控轴向自注意力的多通道分割网络（MCSegNet）模型，以实现病理图像细胞核的精准分割。所提模型采用双编码器和解码器结构，在其中使用轴向自注意力编码通道捕获全局特征，并使用基于残差结构的卷积编码通道获取局部精细特征；在编码通道末端，通过特征融合增强特征表示，从而为解码器提供良好的信息基础；而解码器通过级联多个上采样模块逐步生成分割结果。此外，使用改进的混合损失函数有效解决了病理图像中普遍存在的样本不均衡问题。在MoNuSeg2020公开数据集上的实验结果表明，改进的分割方法比U-Net在F1、交并比（IoU）指标上分别提升了2.66个百分点、2.77个百分点，有效改善了病理图像的分割效果，提高了临床诊断的可靠性。

关键词: 病理图像, 细胞核分割, 轴向自注意力, 残差结构, 混合损失函数

CLC Number:

TP183

Zhi CHEN, Xin LI, Liyan LIN, Jing ZHONG, Peng SHI. Multi-channel pathological image segmentation with gated axial self-attention[J]. Journal of Computer Applications, 2023, 43(4): 1269-1277.

陈志, 李歆, 林丽燕, 钟婧, 时鹏. 引入门控轴向自注意力的多通道病理图像分割[J]. 《计算机应用》唯一官方网站, 2023, 43(4): 1269-1277.

Figures/Tables 13

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数据集	细胞核总数	图像数
数据集	细胞核总数	乳腺	肝脏	肾脏	前列腺	膀胱	结肠	胃	肺	大脑
总计	28 846	8	6	9	8	4	3	2	2	2
训练集	21 623	6	6	6	6	2	2	2	―	―
测试集	7 223	2	―	3	2	2	1	―	2	2

数据集	细胞核总数	图像数
数据集	细胞核总数	乳腺	肝脏	肾脏	前列腺	膀胱	结肠	胃	肺	大脑
总计	28 846	8	6	9	8	4	3	2	2	2
训练集	21 623	6	6	6	6	2	2	2	―	―
测试集	7 223	2	―	3	2	2	1	―	2	2

ResBlock	轴向自注意力	DiceBCELoss	准确率	精确度	召回率	特异度	F1	JS	DC
			91.75	78.85	81.17	94.26	79.44	66.04	79.44
√			91.60	76.61	85.34	93.00	80.10	66.96	80.10
√	√		91.95	77.83	84.53	93.64	80.63	67.67	80.63
√	√	√	92.00	78.30	85.13	93.61	80.87	68.03	80.87

ResBlock	轴向自注意力	DiceBCELoss	准确率	精确度	召回率	特异度	F1	JS	DC
			91.75	78.85	81.17	94.26	79.44	66.04	79.44
√			91.60	76.61	85.34	93.00	80.10	66.96	80.10
√	√		91.95	77.83	84.53	93.64	80.63	67.67	80.63
√	√	√	92.00	78.30	85.13	93.61	80.87	68.03	80.87

模型类型	模型	F1	IoU
基于卷积	U-Net^［9］	76.45±2.62	62.86±3.00
	UNet++^［10］	77.01±2.01	63.04±2.54
	Attention U-Net^［27］	76.67±1.06	63.47±1.16
基于Transformer	MedT^［19］	77.46±2.38	63.37±3.11
	TransUNet^［18］	78.53±1.06	65.05±1.28
	Swin-Unet^［21］	77.69±0.94	63.77±1.15
	UCTransNet^［28］	79.08±0.67	65.50±0.91
本文模型		79.11±0.99	65.63±1.33

Multi-channel pathological image segmentation with gated axial self-attention

引入门控轴向自注意力的多通道病理图像分割

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Figures/Tables 13

References 29

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Metrics

模型	参数量/10⁶	计算量/GFLOPS
U-Net^［9］	31.04	437.71
UNet++^［10］	36.63	1 108.74
Attention U-Net^［27］	34.88	533.93
MedT^［19］	14.18	1.37
TransUNet^［18］	66.78	260.39
Swin-Unet^［21］	27.12	61.21
UCTransNet^［28］	66.22	343.46
本文模型	58.01	622.97

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