多特征融合的产时超声胎方位识别模型

doi:10.12122/j.issn.1673-4254.2025.07.24

南方医科大学学报 ›› 2025, Vol. 45 ›› Issue (7): 1563-1570.doi: 10.12122/j.issn.1673-4254.2025.07.24

• • 上一篇

多特征融合的产时超声胎方位识别模型

郑子瑜¹(), 杨夏颖³(), 吴圣杰²(), 张诗婕³, 吕国荣³, 柳培忠²(), 王珺⁴(), 何韶铮³()

^1.华侨大学信息化建设与管理处，福建泉州 362021
^2.华侨大学工学院，福建泉州 362021
^3.福建医科大学附属第二医院超声科，福建泉州 362000
^4.泉州医学高等专科学校附属人民医院妇产科，福建泉州 362018

收稿日期:2025-03-28 出版日期:2025-07-20 发布日期:2025-07-17
通讯作者: 柳培忠,王珺,何韶铮 E-mail:zhzy@hqu.edu.cn;19942911457@163.com;1136132863@qq.com;pzliu@hqu.edu.cn;490089243@qq.com;1251282489@qq.com
作者简介:郑子瑜，工程师，E-mail: zhzy@hqu.edu.cn
杨夏颖，在读硕士研究生，E-mail: 19942911457@163.com
吴圣杰，在读硕士研究生，E-mail: 1136132863@qq.com
第一联系人：郑子瑜、杨夏颖、吴圣杰共同为第一作者
基金资助:
福建省科技创新联合资金项目资助(2024Y9392);福建省科技创新联合资金项目资助(2024Y9386);福建省科技创新联合资金项目资助(2024Y9435)

A multi-feature fusion-based model for fetal orientation classification from intrapartum ultrasound videos

Ziyu ZHENG¹(), Xiaying YANG³(), Shengjie WU²(), Shijie ZHANG³, Guorong LYU³, Peizhong LIU²(), Jun WANG⁴(), Shaozheng HE³()

^1.Department of Information Construction and Management
^2.College of Engineering, Huaqiao University, Quanzhou 362021, China
^3.Department of Ultrasound, Second Affiliated Hospital of Fujian Medical University, Quanzhou 362000, China
^4.Department of Obstetrics and Gynecology, Affiliated People's Hospital of Quanzhou Medical College, Quanzhou 362018, China

Received:2025-03-28 Online:2025-07-20 Published:2025-07-17
Contact: Peizhong LIU, Jun WANG, Shaozheng HE E-mail:zhzy@hqu.edu.cn;19942911457@163.com;1136132863@qq.com;pzliu@hqu.edu.cn;490089243@qq.com;1251282489@qq.com

摘要/Abstract

摘要：

目的探讨多特征融合的产时超声胎方位智能分析模型在提高分娩过程中胎方位分类准确性方面的应用效果。方法本研究提出模型由输入、骨干网络和分类头3部分组成。输入部分进行数据增强，以提高样本质量和模型的泛化能力；主干部分进行特征提取，在Yolov8的基础上结合了CBAM、ECA、PSA注意力机制和AIFI特征交互模块，以提升特征提取效率和模型性能；分类头由卷积层和softmax函数组成，输出最终各个类别的概率值。用医生对关键器官（眼睛，脸部，头部，丘脑，脊柱）进行画框标注后的图像用于训练，以提高对枕前、枕后和枕横方位的分类准确性。结果实验结果表明，本文提出的模型在胎方位分类任务中表现出色，准确率（ACC）达到了0.984，PR曲线下面积即平均精确度（PR-AUC）为0.993，特征曲线下面积（ROC-AUC）为0.984，kappa一致性检验分数为0.974，该深度学习模型表现出了非常好的性能，模型预测结果和实际类别一致。结论本研究提出的多特征融合模型能够高效、准确地分类产时超声图像中的胎方位，为临床提供可靠的辅助工具。

关键词: 产时超声, 胎方位, 深度学习, 注意力机制

Abstract:

Objective To construct an intelligent analysis model for classifying fetal orientation during intrapartum ultrasound videos based on multi-feature fusion. Methods The proposed model consists of the Input, Backbone Network and Classification Head modules. The Input module carries out data augmentation to improve the sample quality and generalization ability of the model. The Backbone Network was responsible for feature extraction based on Yolov8 combined with CBAM, ECA, PSA attention mechanism and AIFI feature interaction module. The Classification Head consists of a convolutional layer and a softmax function to output the final probability value of each class. The images of the key structures (the eyes, face, head, thalamus, and spine) were annotated with frames by physicians for model training to improve the classification accuracy of the anterior occipital, posterior occipital, and transverse occipital orientations. Results The experimental results showed that the proposed model had excellent performance in the tire orientation classification task with the classification accuracy reaching 0.984, an area under the PR curve (average accuracy) of 0.993, and area under the ROC curve of 0.984, and a kappa consistency test score of 0.974. The prediction results by the deep learning model were highly consistent with the actual classification results. Conclusion The multi-feature fusion model proposed in this study can efficiently and accurately classify fetal orientation in intrapartum ultrasound videos.

Key words: intrapartum ultrasound, fetal orientation, deep learning, attention mechanism

郑子瑜, 杨夏颖, 吴圣杰, 张诗婕, 吕国荣, 柳培忠, 王珺, 何韶铮. 多特征融合的产时超声胎方位识别模型[J]. 南方医科大学学报, 2025, 45(7): 1563-1570.

Ziyu ZHENG, Xiaying YANG, Shengjie WU, Shijie ZHANG, Guorong LYU, Peizhong LIU, Jun WANG, Shaozheng HE. A multi-feature fusion-based model for fetal orientation classification from intrapartum ultrasound videos[J]. Journal of Southern Medical University, 2025, 45(7): 1563-1570.

图/表 9

表1 数据分布

Tab.1 Data distribution

Classes	Train (Number of images)	Val (Number of images)	Video Test (number of video/number of frames)
OA	1006	125	3/482
OP	1708	213	3/491
OT	1270	158	2/346
Total	3984	496	8/1319

图1 模型结构图

Fig.1 Model structure diagram.

表2 模型评价指标定义

Tab.2 Definition of the model evaluation metrics

Model evaluation metrics	Definition
ACC	$T P + T N T P + T N + F P + F N$
Precision (P)	$T P T P + F P$
Recall (R)	$T P T P + F N$
PR-AUC	The average precision, which is the average of the precision over different recall points, is shown as the area under the P-R curve on the P-R curve plot
kappa	$A c c u r a c y - p e 1 - p e p e = ∑ i (S u m o f e l e m e n t s i n r o w i * S u m o f e l e m e n t s i n c o l u m n i) T o t a l s u m o f a l l e l e m e n t s i n t h e m a t r i x 2$
ROC-AUC	The area under the ROC curve

表2 模型评价指标定义

Tab.2 Definition of the model evaluation metrics

Model evaluation metrics	Definition
ACC	$T P + T N T P + T N + F P + F N$
Precision (P)	$T P T P + F P$
Recall (R)	$T P T P + F N$
PR-AUC	The average precision, which is the average of the precision over different recall points, is shown as the area under the P-R curve on the P-R curve plot
kappa	$A c c u r a c y - p e 1 - p e p e = ∑ i (S u m o f e l e m e n t s i n r o w i * S u m o f e l e m e n t s i n c o l u m n i) T o t a l s u m o f a l l e l e m e n t s i n t h e m a t r i x 2$
ROC-AUC	The area under the ROC curve

图2 模型在验证集上的测试结果

Fig.2 Test results of the model on the validation dataset.

图3 预测统计的方法

Fig.3 Methods for predictive statistics.

表3 不同模型对视频的分类结果

Tab.3 Classification results of videos by different models

Label	Ours	EfficientNet_b2	DenseNet169	ResNet50	Yolo8s_cls	ConvNeXt	MPViT	Swim transformer
OA1	OA	OA	OP	OA	OA	OP	OA	OT
OP1	OP	OP	OP	OP	OP	OP	OA	OP
OT1	OT	OT	OT	OT	OT	OT	OA	OT
OA2	OA	OA	OA	OA	OA	OA	OA	OT
OP2	OP	OP	OP	OP	OP	OP	OA	OP
OA3	OA	OA	OA	OA	OA	OA	OA	OT
OP3	OP	OP	OP	OP	OP	OP	OA	OT
OT3	OT	OT	OT	OT	OT	OT	OA	OT

表4 不同模型结果的比较

Tab.4 Comparison of results from different models

Model	PR-AUC	ROC-AUC	ACC	kappa
EfficientNet_b2	0.92545	0.96126	0.91431	0.86892
DenseNet169	0.92836	0.96201	0.91519	0.86879
ResNet50	0.92621	0.95507	0.92307	0.88284
Yolo8s_cls	0.87686	0.94205	0.87456	0.80322
ConvNeXt	0.91083	0.94177	0.91145	0.86351
MPViT	0.47209	0.57401	0.37571	0.00162
Swim transformer	0.49753	0.62991	0.48685	0.22014
Yolov8+CBAM	0.88311	0.94350	0.90500	0.85025
Yolov8+CSAM	0.55933	0.75915	0.64312	0.42443
Ours	0.91545	0.95944	0.92499	0.88553

图4 不同模型在测试集上的PR曲线和ROC曲线

Fig.4 PR curves and ROC curves of different models on the test set.

表5 消融实验

Tab.5 Ablation experiment

Modules				PR-AUC	ROC-AUC	ACC	kappa
CBAM	PSA	ECA	AIFI	PR-AUC	ROC-AUC	ACC	kappa
0	0	0	0	0.87686	0.94205	0.87456	0.80322
1	0	0	0	0.88311	0.94350	0.90500	0.85025
0	1	0	0	0.91193	0.94745	0.89150	0.83678
0	0	1	0	0.89821	0.94370	0.89865	0.84478
0	0	0	1	0.86844	0.93363	0.86554	0.79148
1	1	1	1	0.91545	0.95944	0.92499	0.88553

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多特征融合的产时超声胎方位识别模型

A multi-feature fusion-based model for fetal orientation classification from intrapartum ultrasound videos

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参考文献 33

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Metrics

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Label	Ours	EfficientNet_b2	DenseNet169	ResNet50	Yolo8s_cls	ConvNeXt	MPViT	Swim transformer
OA1	OA	OA	OP	OA	OA	OP	OA	OT
OP1	OP	OP	OP	OP	OP	OP	OA	OP
OT1	OT	OT	OT	OT	OT	OT	OA	OT
OA2	OA	OA	OA	OA	OA	OA	OA	OT
OP2	OP	OP	OP	OP	OP	OP	OA	OP
OA3	OA	OA	OA	OA	OA	OA	OA	OT
OP3	OP	OP	OP	OP	OP	OP	OA	OT
OT3	OT	OT	OT	OT	OT	OT	OA	OT

Label	Ours	EfficientNet_b2	DenseNet169	ResNet50	Yolo8s_cls	ConvNeXt	MPViT	Swim transformer
OA1	OA	OA	OP	OA	OA	OP	OA	OT
OP1	OP	OP	OP	OP	OP	OP	OA	OP
OT1	OT	OT	OT	OT	OT	OT	OA	OT
OA2	OA	OA	OA	OA	OA	OA	OA	OT
OP2	OP	OP	OP	OP	OP	OP	OA	OP
OA3	OA	OA	OA	OA	OA	OA	OA	OT
OP3	OP	OP	OP	OP	OP	OP	OA	OT
OT3	OT	OT	OT	OT	OT	OT	OA	OT

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Label	Ours	EfficientNet_b2	DenseNet169	ResNet50	Yolo8s_cls	ConvNeXt	MPViT	Swim transformer
OA1	OA	OA	OP	OA	OA	OP	OA	OT
OP1	OP	OP	OP	OP	OP	OP	OA	OP
OT1	OT	OT	OT	OT	OT	OT	OA	OT
OA2	OA	OA	OA	OA	OA	OA	OA	OT
OP2	OP	OP	OP	OP	OP	OP	OA	OP
OA3	OA	OA	OA	OA	OA	OA	OA	OT
OP3	OP	OP	OP	OP	OP	OP	OA	OT
OT3	OT	OT	OT	OT	OT	OT	OA	OT

Label	Ours	EfficientNet_b2	DenseNet169	ResNet50	Yolo8s_cls	ConvNeXt	MPViT	Swim transformer
OA1	OA	OA	OP	OA	OA	OP	OA	OT
OP1	OP	OP	OP	OP	OP	OP	OA	OP
OT1	OT	OT	OT	OT	OT	OT	OA	OT
OA2	OA	OA	OA	OA	OA	OA	OA	OT
OP2	OP	OP	OP	OP	OP	OP	OA	OP
OA3	OA	OA	OA	OA	OA	OA	OA	OT
OP3	OP	OP	OP	OP	OP	OP	OA	OT
OT3	OT	OT	OT	OT	OT	OT	OA	OT

Label	Ours	EfficientNet_b2	DenseNet169	ResNet50	Yolo8s_cls	ConvNeXt	MPViT	Swim transformer
OA1	OA	OA	OP	OA	OA	OP	OA	OT
OP1	OP	OP	OP	OP	OP	OP	OA	OP
OT1	OT	OT	OT	OT	OT	OT	OA	OT
OA2	OA	OA	OA	OA	OA	OA	OA	OT
OP2	OP	OP	OP	OP	OP	OP	OA	OP
OA3	OA	OA	OA	OA	OA	OA	OA	OT
OP3	OP	OP	OP	OP	OP	OP	OA	OT
OT3	OT	OT	OT	OT	OT	OT	OA	OT

Label	Ours	EfficientNet_b2	DenseNet169	ResNet50	Yolo8s_cls	ConvNeXt	MPViT	Swim transformer
OA1	OA	OA	OP	OA	OA	OP	OA	OT
OP1	OP	OP	OP	OP	OP	OP	OA	OP
OT1	OT	OT	OT	OT	OT	OT	OA	OT
OA2	OA	OA	OA	OA	OA	OA	OA	OT
OP2	OP	OP	OP	OP	OP	OP	OA	OP
OA3	OA	OA	OA	OA	OA	OA	OA	OT
OP3	OP	OP	OP	OP	OP	OP	OA	OT
OT3	OT	OT	OT	OT	OT	OT	OA	OT