Application of knowledge distillation technology for fine segmentation of three-vessel and trachea views in fetal echocardiographic images
10.3760/cma.j.cn131148-20230801-00045
- VernacularTitle:知识蒸馏算法训练在胎儿心脏超声图像三血管气管切面精细化分割中的应用
- Author:
Min DI
1
;
Qiwen CAI
;
Mingming MA
;
Yuanshi TIAN
;
Yang CHEN
;
Bowen ZHAO
;
Ran CHEN
Author Information
1. 浙江大学医学院附属邵逸夫医院超声科 浙江省胎儿心脏超声诊断技术指导中心 浙江大学邵逸夫临床医学研究所，杭州 310016
- Keywords:
Artificial intelligence;
Knowledge distillation technology;
Fetus;
Cardiac ultrasound images
- From:
Chinese Journal of Ultrasonography
2024;33(1):21-26
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the application value of fetal heart ultrasound image segmentation network model based on knowledge distillation technology in the fine segmentation of fetal heart ultrasound image at three-vessel and trachea (3VT) views.Methods:One thousand and three hundred fetals were retrospectively collected from Sir Run Run Shaw Hospital, Zhejiang University College of Medicine from January 2016 to December 2021, the two-dimensional grayscale ultrasound images of fetal heart at 3VT views were analyzed and then divided into training, validation, and test sets. The training and validation sets were used to construct the auxiliary diagnostic network models, and the test set was used to test the reliability of different network models (U-Net, DeepLabv3+ ). The 3VT views were collected and annotated by an experienced doctor as the reference standard. The intersection over union (IoU), pixel accuracy (PA) and Dice coefficient (Dice) were used as the 3 indexes to evaluate the segmentation accuracy, and the diagnostic efficiency of the training model was evaluated. The training model and the most commonly used segmentation models were identified, and the results were compared. A total of 101 images were randomly selected and assigned to junior doctors, AI and junior doctors assisted AI interpretation. Bland-Altman images were drawn to evaluate their consistency with the reference standard, and the results were compared.Results:The training model of knowledge distillation algorithm achieved better results than U-Net, DeepLabv3+ models on all evaluation indexes, and the average IoU, PA and Dice were 68.6%, 81.4% and 81.3%, respectively. Compared with the U-Net model and DeepLabv3+ model, more accurate segmentation boundaries were obtained by the knowledge distillation algorithm training model, and the quantitative evaluation indexes were improved. With the aid of the model, the diagnostic accuracy of junior doctors was improved.Conclusions:The knowledge distillation algorithm training model segmentation method can identify the anatomical structure of the fetal heart in the 3VT view of the fetal heart ultrasound image, and the recognition result is obviously better than other related methods, and can improve the accuracy of image recognition for doctors with low experience.
