To evaluate the reliability of three dimensional spiral fast spin echo pseudo-continuous arterial spin labeling(3D pc-ASL)in measuring cerebral blood flow(CBF)with different post-labeling delay time(PLD)in the resting...To evaluate the reliability of three dimensional spiral fast spin echo pseudo-continuous arterial spin labeling(3D pc-ASL)in measuring cerebral blood flow(CBF)with different post-labeling delay time(PLD)in the resting state and the right finger taping state.Methods 3D pc-ASL and three dimensional T1-weighted fast spoiled gradient recalled echo(3D T1-FSPGR)sequence were applied to eight healthy subjects twice at the same time each day for one week interval.ASL data acquisition was performed with post-labeling delay time(PLD)1.5 seconds and 2.0 seconds in the resting state and the right finger taping state respectively.CBF mapping was calculated and CBF value of both the gray matter(GM)and white matter(WM)was automatically extracted.The reliability was evaluated using the intraclass correlation coefficient(ICC)and Bland and Altman plot.Results ICC of the GM(0.84)and WM(0.92)was lower at PLD 1.5 seconds than that(GM,0.88;WM,0.94)at PLD 2.0 seconds in the resting state,and ICC of GM(0.88)was higher in the right finger taping state than that in the resting state at PLD 1.5 seconds.ICC of the GM and WM was 0.71 and 0.78 for PLD 1.5 seconds and PLD 2.0 seconds in the resting state at the first scan,and ICC of the GM and WM was 0.83 and 0.79 at the second scan,respectively.Conclusion This work demonstrated that 3D pc-ASL might be a reliable imaging technique to measure CBF over the whole brain at different PLD in the resting state or controlled state.展开更多
In the field of medical images,pixel-level labels are time-consuming and expensive to acquire,while image-level labels are relatively easier to obtain.Therefore,it makes sense to learn more information(knowledge)from ...In the field of medical images,pixel-level labels are time-consuming and expensive to acquire,while image-level labels are relatively easier to obtain.Therefore,it makes sense to learn more information(knowledge)from a small number of hard-to-get pixel-level annotated images to apply to different tasks to maximize their usefulness and save time and training costs.In this paper,using Pixel-Level Labeled Images forMulti-Task Learning(PLDMLT),we focus on grading the severity of fundus images for Diabetic Retinopathy(DR).This is because,for the segmentation task,there is a finely labeled mask,while the severity grading task is without classification labels.To this end,we propose a two-stage multi-label learning weakly supervised algorithm,which generates initial classification pseudo labels in the first stage and visualizes heat maps at all levels of severity using Grad-Cam to further provide medical interpretability for the classification task.A multitask model framework with U-net as the baseline is proposed in the second stage.A label update network is designed to alleviate the gradient balance between the classification and segmentation tasks.Extensive experimental results show that our PLDMLTmethod significantly outperforms other stateof-the-art methods in DR segmentation on two public datasets,achieving up to 98.897%segmentation accuracy.In addition,our method achieves comparable competitiveness with single-task fully supervised learning in the DR severity grading task.展开更多
基金Supported by the Foundation for Medical and Health Sci&Tech Innovation Project of Sanya(2016YW37)the Special Financial Grant from China Postdoctoral Science Foundation(2014T70960)
文摘To evaluate the reliability of three dimensional spiral fast spin echo pseudo-continuous arterial spin labeling(3D pc-ASL)in measuring cerebral blood flow(CBF)with different post-labeling delay time(PLD)in the resting state and the right finger taping state.Methods 3D pc-ASL and three dimensional T1-weighted fast spoiled gradient recalled echo(3D T1-FSPGR)sequence were applied to eight healthy subjects twice at the same time each day for one week interval.ASL data acquisition was performed with post-labeling delay time(PLD)1.5 seconds and 2.0 seconds in the resting state and the right finger taping state respectively.CBF mapping was calculated and CBF value of both the gray matter(GM)and white matter(WM)was automatically extracted.The reliability was evaluated using the intraclass correlation coefficient(ICC)and Bland and Altman plot.Results ICC of the GM(0.84)and WM(0.92)was lower at PLD 1.5 seconds than that(GM,0.88;WM,0.94)at PLD 2.0 seconds in the resting state,and ICC of GM(0.88)was higher in the right finger taping state than that in the resting state at PLD 1.5 seconds.ICC of the GM and WM was 0.71 and 0.78 for PLD 1.5 seconds and PLD 2.0 seconds in the resting state at the first scan,and ICC of the GM and WM was 0.83 and 0.79 at the second scan,respectively.Conclusion This work demonstrated that 3D pc-ASL might be a reliable imaging technique to measure CBF over the whole brain at different PLD in the resting state or controlled state.
文摘In the field of medical images,pixel-level labels are time-consuming and expensive to acquire,while image-level labels are relatively easier to obtain.Therefore,it makes sense to learn more information(knowledge)from a small number of hard-to-get pixel-level annotated images to apply to different tasks to maximize their usefulness and save time and training costs.In this paper,using Pixel-Level Labeled Images forMulti-Task Learning(PLDMLT),we focus on grading the severity of fundus images for Diabetic Retinopathy(DR).This is because,for the segmentation task,there is a finely labeled mask,while the severity grading task is without classification labels.To this end,we propose a two-stage multi-label learning weakly supervised algorithm,which generates initial classification pseudo labels in the first stage and visualizes heat maps at all levels of severity using Grad-Cam to further provide medical interpretability for the classification task.A multitask model framework with U-net as the baseline is proposed in the second stage.A label update network is designed to alleviate the gradient balance between the classification and segmentation tasks.Extensive experimental results show that our PLDMLTmethod significantly outperforms other stateof-the-art methods in DR segmentation on two public datasets,achieving up to 98.897%segmentation accuracy.In addition,our method achieves comparable competitiveness with single-task fully supervised learning in the DR severity grading task.
