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Abstract #3798

Lesion Segmentation for Venous Malformations Based on Unet++ Architecture

Jian Dong1, Yaping Wu1, Yan Bai1, and Meiyun Wang1
1Department of Medical Imaging, Henan Provincial People's Hospital, Zhengzhou, China


In this study, we trained a fully automatic lesion segmentation model of venous malformations based on Unet++ structure and fat-saturated T2-weighted images. The results of automatic segmentation of lesions in different sequence directions and locations of the lesions are consistent with the results of manual segmentation by radiologists. The Dice coefficient on the test set reached 0.847. This fully automatic lesion segmentation model can provide support for subsequent automatic diagnosis studies related to venous malformations.

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