目的 探索基于知识蒸馏算法训练构建的胎儿心脏超声图像分割网络模型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价值。 方法 回顾性收集2016年1月至2021年12月在浙江大学医学院附属邵逸夫医院接受胎儿心脏超声检查的正常中晚孕期胎儿1 300例，分析胎儿心脏超声三血管气管切面二维灰阶超声图像，将其分为训练集、验证集和测试集。应用训练集与验证集构建辅助诊断网络模型，再用测试集对不同网络模型(U-Net、DeepLabv3＋)进行测试，由一名有经验的医生收集并注释三血管气管切面作为参考标准。以交并比(IoU)、像素精度(PA)和骰子系数(Dice)为3个定量评估分割精度指标，评估该知识蒸馏算法训练模型的诊断效能。并对本模型及最常用的分割模型进行识别，对结果进行比较。随机选取101张图像，分别交由低年资医生、AI及低年资医生辅助AI判读，绘制Bland-Altman图像评价其分别与参考标准的一致性，并对三者结果进行比较。 结果 知识蒸馏算法训练模型在所有评价指标上均取得了比U-Net、DeepLabv3＋模型更好的结果，平均IoU、PA、Dice分别为68.6%、81.4%、81.3%。与U-Net及DeepLabv3＋模型相比，本模型获得了更精确的分割边界，并且在定量评价指标上均有提高。经过该模型辅助，低年资医生对于诊断的精确度有所提高。 结论 知识蒸馏算法训练模型分割方法可在胎儿心脏超声图像的三血管气管切面识别胎儿心脏的解剖结构，其识别结果明显优于相关方法，并可提高低年资医生对于其图像识别的准确度。 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.
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.
万方数据