Medical physics · 2025
Physiology based unsupervised learning for segmentation of COVID-19 lesions in chest 3D CT scans.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Wang Cheng, Cai Zhiqiang, Zhang Yao, Yang Shuqing
Аннотация
BACKGROUND: The existing deep learning (DL) segmentation methods of COVID-19 lesions in chest CT scans require tedious manual labels for training. This is labor-intensive and time-consuming for radiologists, and also limits the application of DL methods in clinical practice.
PURPOSE: To develop an unsupervised DL segmentation method that does not need the participation of radiologists to provide manual labels for training.
METHODS: Two hundred chest 3D CT scans of 48 COVID-19 patients (including 48 baseline CT scans and 152 follow-up CT scans) were used as training set and another 65 chest 3D CT scans were used as test set. A novel self-learning segmentation method was proposed to train a DL segmentation model based on the intrinsic physiology information of the patients instead of the external professional knowledge of the radiologists. The proposed method comprised two modules: a self-annotation module and a training module. In self-annotation module, the progression of COVID-19 pneumonia and the change of lung volumes between a pair of baseline CT and follow-up CT were combined to automatically annotate the abnormal lung region (referred as self-label) in the follow-up CT. Then in training module, the follow-up CT scans and the corresponding self-labels were used to train a DL segmentation model (referred as Model-S). The COVID-19 lesions segmented by Model-S in the test set were quantitatively compared with the ground truth. To further evaluate the proposed method, the manual labels of 152 follow-up CT scans were delineated by radiologists and compared with the corresponding self-labels. Then another DL segmentation model (referred as Model-M) was trained based on these manual labels. The segmentation results between Model-S and Model-M in the test set were also compared.
RESULTS: As for evaluating the self-annotation results, the Dice similarity coefficient (DSC) between self-labels and manual labels in follow-up CT scans was 93.0% ± 10.5%. The lesion volumes of self-labels and manual labels had a high Spearman correlation (r = 0.99; p < 0.001). As for evaluating the self-learning segmentation results, the DSC between the lesions segmented by Model-S and ground truth, and between the lesions segmented by Model-M and ground truth were 82.1% ± 6.0% and 83.4% ± 5.2%, respectively. The lesion volumes segmented by Model-S and the ground truth had a high Spearman correlation (r = 0.99; p < 0.001). Besides, the DSC between the lesions segmented by Model-S and segmented by Model-M was 87.5% ± 4.0%. The lesion volumes segmented by Model-S and Model-M also had a high Spearman correlation (r = 0.98; p < 0.001).
CONCLUSIONS: The proposed unsupervised segmentation method can automatically segment COVID-19 lesions accurately in chest 3D CT scans without requiring radiologists to provide any manual labels for training in the establishment process of the method.
PURPOSE: To develop an unsupervised DL segmentation method that does not need the participation of radiologists to provide manual labels for training.
METHODS: Two hundred chest 3D CT scans of 48 COVID-19 patients (including 48 baseline CT scans and 152 follow-up CT scans) were used as training set and another 65 chest 3D CT scans were used as test set. A novel self-learning segmentation method was proposed to train a DL segmentation model based on the intrinsic physiology information of the patients instead of the external professional knowledge of the radiologists. The proposed method comprised two modules: a self-annotation module and a training module. In self-annotation module, the progression of COVID-19 pneumonia and the change of lung volumes between a pair of baseline CT and follow-up CT were combined to automatically annotate the abnormal lung region (referred as self-label) in the follow-up CT. Then in training module, the follow-up CT scans and the corresponding self-labels were used to train a DL segmentation model (referred as Model-S). The COVID-19 lesions segmented by Model-S in the test set were quantitatively compared with the ground truth. To further evaluate the proposed method, the manual labels of 152 follow-up CT scans were delineated by radiologists and compared with the corresponding self-labels. Then another DL segmentation model (referred as Model-M) was trained based on these manual labels. The segmentation results between Model-S and Model-M in the test set were also compared.
RESULTS: As for evaluating the self-annotation results, the Dice similarity coefficient (DSC) between self-labels and manual labels in follow-up CT scans was 93.0% ± 10.5%. The lesion volumes of self-labels and manual labels had a high Spearman correlation (r = 0.99; p < 0.001). As for evaluating the self-learning segmentation results, the DSC between the lesions segmented by Model-S and ground truth, and between the lesions segmented by Model-M and ground truth were 82.1% ± 6.0% and 83.4% ± 5.2%, respectively. The lesion volumes segmented by Model-S and the ground truth had a high Spearman correlation (r = 0.99; p < 0.001). Besides, the DSC between the lesions segmented by Model-S and segmented by Model-M was 87.5% ± 4.0%. The lesion volumes segmented by Model-S and Model-M also had a high Spearman correlation (r = 0.98; p < 0.001).
CONCLUSIONS: The proposed unsupervised segmentation method can automatically segment COVID-19 lesions accurately in chest 3D CT scans without requiring radiologists to provide any manual labels for training in the establishment process of the method.